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Abstract

Artery perforation during a vascular catheterization procedure is a potentially life threatening event. It is of particular importance for the surgeons to be aware of hidden or non-obvious events. To minimize the impact it is crucial for the surgeon to detect such a perforation very early. We propose a novel approach to identify perforations based on the acquisition and analysis of audio signals on the outside proximal end of a guide wire. The signals were acquired using a stethoscope equipped with a microphone and attached to the proximal end of the guide wire via a 3D printed adapter. Bispectral analysis was employed to extract acoustic signatures in the signal and several features were extracted from the bispectrum of the signal. Finally, three machine learning algorithms - K-nearest Neighbor, Support Vector Machine (SVM), and Artificial Neural Network (ANN)- were used to classify a signal as a perforation or as an artifact. The bispectrum-based features resulted in valuable features allowing a perforation to be clearly identifiable from other occurring events. A perforation leaves a clear audio signal trace in the time-frequency domain. The recordings were classified as perforation, friction or guide wire bump using SVM with 97% (polykernel) and 98.62% (RBF) accuracy, k-nearest Neighbor an accuracy of 98.28% and ANN with accuracy of 98.73% was obtained. The presented approach shows that interactions starting at the tip of a guide wire can be picked up at its proximal end providing a valuable additional information that could be used during a guide wire procedure. Temporary Share Link: https://authors.elsevier.com/c/1YYm32OYcrjZu

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... In this paper, we want to report on the initial attempts to add this technology to robotic arms for surface characterization [4] and for interventional vascular procedures that gain increased attention in combination with robotic devices [5,6]. ...
... Additionally, we performed several hundred interventions using pig hearts and vessels with a guide wire simulating a cardiac intervention to verify whether we could detect relevant events (vessel touching, bumping, and penetration) [5,6]. The proximally obtained audio i n f o r m a t i o n w a s p r o c e s s e d u s i n g Ti m e -Va r i a n t Autoregressive Modelling (TV AR), and the results in 2D and 3D together with a calculation of the maximum pole energy (see Fig. 3). ...
... Audio signals for different events during a guide wire / catheter procedure with an audio sensor attached to the proximal end of the guide wire. All examined events produced distinctively different results that allow event clustering with high accuracy[6] ...
Article
Minimal-invasive procedures come with significant advantages for the patient. They also come with problems as the navigation/guidance of the devices to a target location is either based on pre-operatively acquired images and then performed free-hand or is accompanied by intraoperative imaging such as MRI or CT that is expensive, complicated and produces artifacts. Using robotic systems for moving and guiding these interventional and therapeutic devices adds additional issues like lack of palpation sensation and missing tissue feedback. While it is possible to add sensors to the distal tip, this creates other obstacles concerning reduced functionality, cables, sterility issues and added complexity and cost. We propose to use a proximally attached audio sensor to record the tissue tool interaction and provide real-time feedback to the clinician. This paper reports on initial attempts to use this technology with robotic arms for surface characterization and interventional vascular procedures that gain increased attention in combination with robotic devices. In summary, Proximal Audio Sensing could be a versatile, cost-effective and powerful tool to guide minimally invasive needle interventions and enable (semi-) autonomous robot-assisted surgery.
... 1) Linear features: The following features are linear features [16], [17], [18], [19]: ...
... The addition pha1 is Phase entropy, and(E j ) is the entropy of the phase of domain (where j=1,2,3) and therefore, three equations are shown in one. More details about the feature is presented in our previous papers of [12], [16], [17], [18] . ...
Conference Paper
Microwave ablation (MWA) therapy with image guidance by computed tomography (CT) is used for liver tumor destruction. However, because of the noise and therefore low contrast, CT images are not good enough for therapy control and need additional magnetic resonance imaging after the therapy. The ablation process itself is facing two significant challenges: Firstly insufficient tumor ablation, which leads to tumor recurrence. Secondary, total ablated area was significantly larger than the tumor size which causes damaging of healthy tissue. To minimize the impact, it is crucial for the radiologist to perform the therapy well to prevent tumor recurrence. Therefore, it is essential to differentiate among healthy, tumor, and ablated tissue textures in the CT scan images. This research contributes to the understanding of tissue characterization for the reduction of the recurrence rate. In this regard, four machine-learning (ML) algorithms of Naive-Bayesian, Logistic-Regression, Decision-Tree, and Random-Forest were employed for liver tissues classification. In this paper, we propose higher order spectral particularly bispectrum analysis for extracting features from the CT images. Then classifiers were trained by ten new features extracted from the bispectrum analysis. For that, the images were divided into small patches, they were labeled as healthy, tumor, and ablated tissue. A maximum accuracy of 90.5% was obtained. The approach shows that the bispectral analysis provides valuable information that can be used during the MWA therapy for tissue characterization of CT scan even in the presence of noise.
... 6 The Bispectral analysis is a modern signal processing technique that permits the extraction of non-linear characteristics and tracks the deviation of data from Gaussianity. 7 The bispectrum B(f 1 ,f 2 ) of a real process {x(k)} represents the two-dimensional Fourier transform of the third-order correlation function of the signal and is given by ...
... 8 Unlike the power spectrum, which suppress the phase information and can only describe linear mechanisms, bispectrum exclusively measures the correlation of phases between the frequency components 1 , 2 and ( 1 + 2 ). The bispectrum is calculated in the triangular area Ω, Namely the non-redundant region (see Figure 1), This nonredundant region is defined with the triangle 0 ≤ 2 ≤ 1 ≤ 1 + 2 ≤ 1. 7,9,10 ...
Article
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Phonocardiography is a technique for recording and interpreting the mechanical activity of the heart. The recordings generated by such a technique are called phonocardiograms (PCG). The PCG signals are acoustic waves revealing a wealth of clinical information about cardiac health. They enable doctors to better understand heart sounds when presented visually. Hence, multiple approaches have been proposed to analyze heart sounds based on PCG recordings. Due to the complexity and the high nonlinear nature of these signals, a computer-aided technique based on higher-order statistics (HOS) is employed, it is known to be an important tool since it takes into account the non-linearity of the PCG signals. This method also known as the bispectrum technique, can provide significant information to enhance the diagnosis for an accurate and objective interpretation of heart condition. The objective expected by this paper is to test in a preliminary way the parameters which can make it possible to establish a discrimination between the various signals of different pathologies and to characterize the cardiac abnormalities. This preliminary study will be done on a reduced sample (nine signals) before applying it subsequently to a larger sample. This work examines the effectiveness of using the bispectrum technique in the analysis of the pathological severity of different PCG signals. The presented approach showed that HOS technique has a good potential for pathological discrimination of various PCG signals.
... It is also a passive sensing technique involving both fast reactions and high sensitivity to subtle changes in processes. Previous studies have shown the feasibility of AE for acquiring feedback information noninvasively from minimally invasive tools [11][12][13]. This method comes with the advantage to avoid direct contact between sensors and the tissue at the surgical site. ...
... This concept reduces the system complexity and allows non-invasive placement of the sensor. This innovative approach has been tested in the meantime for use with biopsy needles and guide wires [11][12][13], where audio signatures were extracted to detect tissue boundaries during the insertion of a needle and to characterize a guide wire perforation. Recently, this approach has been adapted for obtaining feedback information from a da Vinci grasper instrument for analyzing the feasibility of using audio in RMIS [14]. ...
Article
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Robotic minimally invasive surgery (RMIS) has played an important role in the last decades. In traditional surgery, surgeons rely on palpation using their hands. However, during RMIS, surgeons use the visual-haptics technique to compensate the missing sense of touch. Various sensors have been widely used to retrieve this natural sense, but there are still issues like integration, costs, sterilization and the small sensing area that prevent such approaches from being applied. A new method based on acoustic emission has been recently proposed for acquiring audio information from tool-tissue interaction during minimally invasive procedures that provide user guidance feedback. In this work the concept was adapted for acquiring audio information from a RMIS grasper and a first proof of concept is presented. Interactions of the grasper with various artificial and biological texture samples were recorded and analyzed using advanced signal processing and a clear correlation between audio spectral components and the tested texture were identified. https://doi.org/10.1016/j.compbiomed.2019.103370
... This may enable more useful information for the robot to have and less time lost during the analysis, resulting in more AI that can actually be used in real-time. Additionally, these types of data could give pixel data another dimension and theoretically improve computers and robots ability to safely perform autonomous tasks [76,77]. Alternative techniques devised to allow for the differentiation of tissues during surgery involve the utilization of electrical bio-impedance sensing and analysis of force feedback, but are still in the prototype phase [78]. ...
Article
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This is a review focused on advances and current limitations of computer vision (CV) and how CV can help us obtain to more autonomous actions in surgery. It is a follow-up article to one that we previously published in Sensors entitled, "Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?" As opposed to that article that also discussed issues of machine learning, deep learning and natural language processing, this review will delve deeper into the field of CV. Additionally, non-visual forms of data that can aid computerized robots in the performance of more autonomous actions, such as instrument priors and audio haptics, will also be highlighted. Furthermore, the current existential crisis for surgeons, endoscopists and interventional radiologists regarding more autonomy during procedures will be discussed. In summary, this paper will discuss how to harness the power of CV to keep doctors who do interventions in the loop.
... In this study, various parametric measures were used for validation of the study. Mahmoodian et al. (2019) have suggested a study that presents an innovative technique to recognize the perforations based on the acquisitions and investigation of audio signals on the external proximal termination of a guidewire. The signal was attained through a stethoscope containing a microphone and connected to the proximal stop of the guidewire using a 3D printed connecter. ...
Article
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With the development and advancement of machine learning (ML), different aspects of our daily lives are now changed and revolutionized. Diverse ML-based smart and intelligent algorithms are deployed to detect faults in wires, especially in power cables. These approaches are beneficial for reliable, and better wire designs as some of them can estimate marks before they happen. Different sensing devices are made with the integration of intelligent ML-based methods to make the traditional fault detection methods very effective and productive. With the integration of machine learning and artificial intelligence-based architectures, fault detection is now very developed and efficient. Due to the deployment of these technologies in various sectors like power transmission networks, many people's lives can be saved. These intelligent procedures primarily work in real-time and can provide assistance and guidance within no time during any unwanted situation. Current research has considered that the Byol algorithm is used to detect wire damage for safety procedures. The experimental work was done, and the applications of the algorithm in the area of research show the effectiveness of the study. In this study, various parametric measures were used for the validation of the study.
... Interestingly, the Versius Complete Surgical System has haptic capabilities, but the detection of resting human tremor makes the haptics useless and potentially bothersome [3]. Symbolically, one of the first tools in modern medicine was sound, and, as highlighted by the development of the stethoscope, it is in this spirit that researchers began to harness the information generated by friction, bumps and perforations by analyzing them with various ML algorithms, finding that differentiation of these events was possible during vascular catheterizations with guidewires [112]. The ML algorithms included artificial neural networks, K-nearest Neighbor and Support Vector Machine. ...
Article
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Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.
... Recently, an audio-based technique has been proposed in [9] for listening to the needle tip-tissue interaction dynamics using a sensor placed at the proximal end of the tool. The authors of this work has shown promising preliminary results for monitoring medical interventional devices such as needles [9], guide wires [15], and laparoscopic tools [4]. However, even if audio has proved to be a tool with potential for providing guidance information such as tissue-tissue passage, puncture and perforation events or palpation information, the generated audio dynamics are still not fully understood. ...
Article
Full-text available
In robot-assisted procedures, the surgeon controls the surgical instruments from a remote console, while visually monitoring the procedure through the endoscope. There is no haptic feedback available to the surgeon, which impedes the assessment of diseased tissue and the detection of hidden structures beneath the tissue, such as vessels. Only visual clues are available to the surgeon to control the force applied to the tissue by the instruments, which poses a risk for iatrogenic injuries. Additional information on haptic interactions of the employed instruments and the treated tissue that is provided to the surgeon during robotic surgery could compensate for this deficit. Acoustic emissions (AE) from the instrument/tissue interactions, transmitted by the instrument are a potential source of this information. AE can be recorded by audio sensors that do not have to be integrated into the instruments, but that can be modularly attached to the outside of the instruments shaft or enclosure. The location of the sensor on a robotic system is essential for the applicability of the concept in real situations. While the signal strength of the acoustic emissions decreases with distance from the point of interaction, an installation close to the patient would require sterilization measures. The aim of this work is to investigate whether it is feasible to install the audio sensor in non-sterile areas far away from the patient and still be able to receive useful AE signals. To determine whether signals can be recorded at different potential mounting locations, instrument/tissue interactions with different textures were simulated in an experimental setup. The results showed that meaningful and valuable AE can be recorded in the non-sterile area of a robotic surgical system despite the expected signal losses.
... Since the transducer remains outside of the body and in distance to the surgical side, the concept can be characterized as out-patient and passive acoustic sensing. The capability of the concept has been proven for various applications such as laparoscopic access [9], cardiac catheterization [10] or surface texture differentiation [11]. The aim of this work is to highlight the potential of this approach to overcome some of the mentioned limitations of arthroscopic surgery and cartilage classification. ...
Article
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Arthroscopic surgery is a technically challenging but common minimally invasive procedure with a long learning curve and a high incidence of iatrogenic damage. These damages can occur due to the lack of feedback and supplementary information regarding tissue-instrument-contact during surgery. Deliberately performed interactions can be used however to obtain clinically relevant information, e.g. when a surgeon uses the tactile feedback to assess the condition of articular cartilage. Yet, the perception of such events is highly subjective. We propose a novel proximally attached sensing concept applied to arthroscopic surgery to allow an objective characterization and utilization of interactions. It is based on acoustic emissions which originate from tissue-instrument-contact, that propagate naturally via the instrument shaft and that can be obtained by a transducer setup outside of the body. The setup was tested on its ability to differentiate various conditions of articular cartilage. A femoral head with varying grades of osteoarthritic cartilage was tapped multiple times ex-vivo with a conventional Veress needle with a sound transducer attached at the outpatient end. A wavelet-based processing of the obtained signals and subsequent analysis of distribution of spectral energy showed the potential of tool-tissue-interactions to characterize different cartilage conditions. The proposed concept needs further evaluation with a dedicated design of the palpation tool and should be tested in realistic arthroscopic scenarios.
... Therefore, bispectrum is particularly effective in extracting feature of modulation signals [5]. In addition to image processing in medicine [6][7][8], bispectrum analysis is also gradually used in vibration signal processing of mechanical equipment. Li et al. [9] proposed a fault diagnosis method based on bispectrum entropy and deep belief network which accurately predicted the trends and random fluctuations during the performance degradation of the hydraulic pump. ...
Article
To improve the efficiency and accuracy of fault diagnostics of planetary gearboxes, an intelligent diagnosis approach is proposed based on deep convolutional neural networks (CNN) and vibration bispectrum. Rather than using raw vibration signals, bispectrum is appreciated as the input for the CNN models (denoted as bispectrum-CNN) because the bispectrum allows nonlinear feature enhancement and noise reduction. In addition, transfer learning (TL) is accompanied to address the challenges of CNN difficulties. The proposed bispectrum-CNN is verified firstly to diagnose a number of common faults including gear states: normal, tooth wear, tooth root crack, tooth breakage and missing tooth, achieving an accuracy of 97.36% in identifying different faults. Then its TL capability is evaluated based on the sun gear faults datasets. The classification accuracy of the planet gear faults is over 95.1%. After the transfer learning, the classification accuracy of the sun gear fault is still higher than 97.9%, and the computational time consumed by proposed method is also less compared to other diagnosis methods. This paper has twofold contributions: 1) the development of a bispectrum based CNN model for fault diagnosis; and 2) the extensive evaluation of CNN-TL methods for monitoring and diagnosing planetary gearboxes.
... Recently, an audio-based technique has been proposed in [9] for listening to the needle tip-tissue interaction dynamics using a sensor placed at the proximal end of the tool. The authors of this work has shown promising preliminary results for monitoring medical interventional devices such as needles [9], guide wires [15], and laparoscopic tools [4]. However, even if audio has proved to be a tool with potential for providing guidance information such as tissue-tissue passage, puncture and perforation events or palpation information, the generated audio dynamics are still not fully understood. ...
Conference Paper
Full-text available
Accurate needle placement is highly relevant for puncture of anatomical structures. The clinician’s experience and medical imaging are essential to complete these procedures safely. However, imaging may come with inaccuracies due to image artifacts. Sensor-based solutions have been proposed for acquiring additional guidance information. These sensors typically require to be embedded in the instrument tip, leading to direct tissue contact, sterilization issues, and added device complexity, risk, and cost. Recently, an audio-based technique has been proposed for “listening” to needle tip-tissue interactions by an externally placed sensor. This technique has shown promising results for different applications. But the relation between the interaction event and the generated audio excitation is still not fully understood. This work aims to study this relationship, using a force sensor as a reference, by relating events and dynamical characteristics occurring in the audio signal with those occurring in the force signal. We want to show that dynamical information that a well-known sensor as force can provide could also be extracted from a low-cost and simple sensor such as audio. In this aim, the Pearson coefficient was used for signal-to-signal correlation between extracted audio and force indicators. Also, an event-to-event correlation between audio and force was performed by computing features from the indicators. Results show high values of correlation between audio and force indicators in the range of 0.53 to 0.72. These promising results demonstrate the usability of audio sensing for tissue-tool interaction and its potential to improve telemanipulated and robotic surgery in the future.
... HOS in particular bispectrum is an advance signal processing technique. Third-order spectrum known as bispectrum is defined as the Fourier transform of the third-order correlation of signal and is defined in [8,29,45,46,47]. ...
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Epilepsy is a neurological disorder that happens because of the propagation of abnormal signals produced by clusters of neurons in the brain. The majority of those with epileptic seizures can be treated by drug therapies and surgery. However, 25% of the patients with diagnosed epilepsy continue to have seizures. Seizures can cause serious injuries and limit the independence and mobility of an individual. Seizure detection and prediction could lead to a better understanding of seizures and with that help preventing patient injury.This paper discusses extraction and evaluation of nonlinear multivariate features using the cross-bispectral method to help predict epileptic seizure occurrences. These ten statistic features were employed to discriminate pre-ictal from interictal states. Therefore, the features were given to the support vector machine classifier as the input. Outputs were then processed in order to evaluate the sensitivity, false positive rate (FPR) and the prediction time. The proposed method obtained sensitivity of 100% and average FPR of 0.044 per hour by using the “Freiburg epileptic seizure prediction” dataset. This high sensitivity index and low FPR index compared with other studies show the ability of cross-higher-order spectral method to analyze epileptic EEG signals. The proposed method is also fast and easy and may be helpful in other applications of EEG analysis such as sleep stage identification and brain–computer interface.
... Concerning the guide wire, it was possible to recognize and characterize dynamics of a guide wire perforation and to distinguish these dynamics from other events that can be present during a real clinical intervention. These results were confirmed and improved in [3], where time-varying bispectrum computation was used in order to take into account the non linearities that involves the audio signal during guide wire insertion (see Fig. 2b). ...
Conference Paper
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In this work we summarize applications of a novel approach for providing complementary information for guiding medical interventional devices (MID) and that have been recently published by our research team. This approach consist of using an audio sensor located in the proximal end of the MID in order to extract meaningful information concerning the interaction between the tip of the instrument and the tissue. The approach was successfully evaluated with different setups and MIDs.
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Ultrasound (US) imaging is used for the diagnosis and also evaluation of thyroid nodules. A Thyroid Imaging Reporting and Data System (TIRADS) is used for the risk stratification of thyroid nodules through US images. The composition of thyroid nodules plays an important role in the risk-stratification process. The percentages of cystic and solid components in a thyroid nodule are one of the features that are can be indicative of the risk of malignancy. In this work, we attempt to classify and estimate solid and cystic regions within nodules. 20x20 texture patches were extracted from solid and cystic regions and converted into signals. These signals are decomposed into low, mid, and high-frequency bands using Continuous Wavelet Transform (CWT). A total of 36 features were extracted from the decomposed signals using Auto- Regressive Modeling. The features were fed into three different Machine Learning (ML) algorithms (Artificial Neural Networks, K-Nearest Neighbors, and Random Forest Classifier) to provide us with a classification of solid versus cystic regions in thyroid nodule US images. The Random Forest Classifier obtained an Accuracy, Sensitivity, and Specificity of 90.41%, 99% and 91% respectively which was the highest among the three chosen ML algorithms. Additionally, the output from the classification phase was also be used to determine the percentage of cystic and solid regions with a given thyroid nodule US image.
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Phonocardiography is a dynamic non-invasive and relatively low-cost technique used to monitor the state of the mechanical activity of the heart. The recordings generated by such a technique is called phonocardiogram (PCG) signals. When shown visually, PCG signals can provide more insights of heart sounds for medical doctors. Thus, several approaches have been proposed to analyse these sounds through PCG recordings. However, due to the complexity and the high nonlinear nature of these recordings, a computer-assisted technique based on higher-order statistics HOS is shown to be, among these techniques, an important tool in PCG signal processing. The third-order spectra technique is one of these techniques; known as bispectrum, it can provide significant information to support physicians with an accurate and objective interpretation of heart condition. This technique is implemented and discussed in this paper. The implemented technique is used for the analysis of heart severity on nine different PCG recordings. These are normal, innocent murmur, coarctation of the aorta, ejection click, atrial gallop, opening snap, aortic stenosis, drum rumble, and aortic regurgitation. A unique bispectrum representation is generated for each type of heart sounds signal. Then, based on the bispectrum analysis, fifteen higher-order spectra HOS features such as the bispectral amplitude, the entropies, the moments, and the weighted center are extracted from each PCG record. The obtained HOS-features showed a well-correlated evolution with the increasing importance of heart severity leading therefore to a high potential in discriminating pathological PCG signals. One should know that, generally, classification of pathological PCG signals refers to the distinction between the presence of a pathology from its absence (binary response) while the discrimination considered in this paper provides an analogue response (value) which can vary from one pathology to another in an increasing or decreasing way.
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Sensorineural hearing loss occurs due to damage to the inner and outer hair cells of the peripheral auditory system. Hearing loss can cause decreases in audibility, dynamic range, frequency and temporal resolution of the auditory system, and all of these effects are known to affect speech intelligibility. In this study, a new reference-free speech intelligibility metric is proposed using 2-D neurograms constructed from the output of a computational model of the auditory periphery. The responses of the auditory-nerve fibers with a wide range of characteristic frequencies were simulated to construct neurograms. The features of the neurograms were extracted using third-order statistics referred to as bispectrum. The phase coupling of neurogram bispectrum provides a unique insight for the presence (or deficit) of supra-threshold nonlinearities beyond audibility for listeners with normal hearing (or hearing loss). The speech intelligibility scores predicted by the proposed method were compared to the behavioral scores for listeners with normal hearing and hearing loss both in quiet and under noisy background conditions. The results were also compared to the performance of some existing methods. The predicted results showed a good fit with a small error suggesting that the subjective scores can be estimated reliably using the proposed neural-response-based metric. The proposed metric also had a wide dynamic range, and the predicted scores were well-separated as a function of hearing loss. The proposed metric successfully captures the effects of hearing loss and supra-threshold nonlinearities on speech intelligibility. This metric could be applied to evaluate the performance of various speech-processing algorithms designed for hearing aids and cochlear implants.
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The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capabilities rely on the adequate identification of seizure activity precursors from electroencephalography recordings. Although a long list of features has been proposed, none of these is able to independently characterize the brain states during transition to a seizure. This work assessed the feasibility of using the bispectrum, an advanced signal processing technique based on higher order statistics, as a precursor of seizure activity. Quantitative features were extracted from the bispectrum and passed through two statistical tests to check for significant differences between preictal and interictal recordings. Results showed statistically significant differences (p < 0.05) between preictal and interictal states using all bispectrum-extracted features. We used normalized bispectral entropy, normalized bispectral squared entropy, and mean of magnitude as inputs to a 5-layer multilayer perceptron classifier and achieved respective held-out test accuracies of 78.11%, 72.64%, and 73.26%.
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An early and accurate diagnosis of Alzheimer’s disease (AD) has been progressively attracting more attention in recent years. One of the main problems of AD is the loss of language skills. This paper presents a computational framework for classifying AD patients compared to healthy control subjects using information from spontaneous speech signals. Spontaneous speech data are obtained from 30 AD patients and 30 healthy controls. Because of the nonlinear and dynamic nature of speech signals, higher order spectral features (specifically bispectrum) were used for analysis. Four classifiers (k-Nearest Neighbor, Support Vector Machine, Naïve Bayes and Decision tree) were used to classify subjects into three different levels of AD and healthy group based on their performance in terms of the HOS-based features. Ten-fold cross-validation method was used to test the reliability of the classifier results. The results showed that the proposed method had a good potential in AD diagnosis. The proposed method was also able to diagnose the earliest stage of AD with high accuracy. The method has the great advantage of being non-invasive, cost-effective, and associated with no side effects. Therefore, the proposed method can be a spontaneous speech directed test for pre-clinical evaluation of AD diagnosis.
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We propose a new and complementary approach to image guidance for monitoring medical interventional devices (MID) with human tissue interaction and surgery augmentation by acquiring acoustic emission data from the proximal end of the MID outside the patient to extract dynamical characteristics of the interaction between the distal tip and the tissue touched or penetrated by the MID. We conducted phantom based experiments (n = 955) to show dynamic tool/tissue interaction during tissue needle passage (a) and vessel perforation caused by guide wire artery perforation (b). We use time-varying auto-regressive (TV-AR) modelling to characterize the dynamic changes and time-varying maximal energy pole (TV-MEP) to compute subsequent analysis of MID/tissue interaction characterization patterns. Qualitative and quantitative analysis showed that the TV-AR spectrum and the TV-MEP indicated the time instants of the needle path through different phantom objects (a) and clearly showed a perforation versus other generated artefacts (b). We demonstrated that audio signals acquired from the proximal part of an MID could provide valuable additional information to surgeons during minimally invasive procedures.
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Artery guidewire-induced perforation during coronary interventions is an uncommon but potentially serious complication with significant morbidity and mortality rates. For minimizing its impact it is crucial for the surgeon to early detect that a perforation has occurred. However, this is not always easy since perforation sometimes is not characterized by any symptom or sign. In this work a time-varying (TV) characterization of coronary artery perforation is proposed through a TV parametrical modelling of an audio signal acquired from the distal part of a guidewire. A stethoscope equipped with a microphone connected to a computer has been attached to the distal part of a 0.014-inch guidewire using a coupling box allowing a direct contact between the distal part of the guidewire and the stethoscope membrane. Coronary arteries belonging to several pork hearts were perforated using the tip of the guidewire. During the procedure audio signals and time of perforation were recorded. An audio database has been implemented in order to evaluate the performances of the proposed characterization technique. It included 100 coronary artery perforations audio recordings, each one with a duration of 30 seconds and 200 recordings with different types of induced guidewire audio artifacts. Each audio signals has been first decimated and then filtered using a wavelet based band-pass filter. The resulting signal has been modelled using a TV autoregressive (AR) model for estimating a TV power spectral density and TV poles. Finally different features has been computed from the AR spectrum and AR poles, based mainly on spectral energy dispersion and tracking of the pole of maximal energy. Results show that that guidewire perforation leaves a characteristic TV trace which can be tracked through the TV poles and spectrum and that clear differentiating patterns can be extracted allowing 90% of correct perforation classification.
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Key Clinical Message The successful use of autologous skin to management may provide a useful and widely applicable method for dealing with the troublesome complication of guidewire‐induced coronary perforation.
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One main challenge for medical investigators is the early diagnosis of Alzheimer’s disease (AD) because it provides greater opportunities for patients to be eligible for more clinical trials. In this study, higher order spectra of human speech signals during AD were explored to analyze and compare the quadratic phase coupling of spontaneous speech signals for healthy and AD subjects using bispectrum and bicoherence. The results showed that the quadratic phase couplings of spontaneous speech signal of persons with Alzheimer’s were reduced compared to healthy subject. However, the speech phase coupled harmonics shifted to the higher frequencies in Alzheimer’s than healthy subjects. In addition, it was shown not only are there significant differences between Alzheimer’s and control subjects in parameters estimated, but also the speech patterns appeared to have fluctuated in both types of spontaneous speech.
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Electroencephalograph (EEG) signals associated with motor imagery (MI) are highly non-Gaussian, non-stationary and have non- linear characteristics. Bispectral analysis is an advanced signal processing technique that quantifies quadratic non-linearities (phase-coupling) among the components of a signal and holds promise for characterizing MI-related EEG. Studies have been reported on the applicability of bispectrum for MI classification; often with different choice of high order spectra features. Question remains as to which of the different features of non-linear interactions over frequency components are best suited for MI classification. In this paper, an analysis based on bispectrum is reported to extract multiple high order spectra features of EEG for MI classification. MI signals from C3 and C4 channels for two tasks are used in the analysis. Based on bispectrum analysis, four high order spectra features are extracted. The classification results indicate that the extracted features could differentiate the two MI tasks with an accuracy of 90±4.71%.
Article
Full-text available
The development of steerable guide wire or catheter designs has been strongly limited by the lack of enabling actuator technologies. This paper presents the properties of an electrostrive actuator technology for steerable actuation. By carefully tailoring material properties and the actuator design, which can be integrated in devices, this technology should realistically make it possible to obtain a steerable guide wire design with considerable latitude. Electromechanical characteristics are described, and their impact on a steerable design is discussed.
Article
Full-text available
A ring-shaped tri-axial force sensor with a 200 µm × 200 µm sensor area using silicon nanowires (SiNWs) as piezoresistive sensing elements is developed and characterized. The sensor comprises a suspended ring structure located at the center of four suspended beams that can be integrated on the distal tip of a guidewire by passing through the hollow core of the sensor. SiNWs with a length of 6 µm and a cross section of 90 nm × 90 nm are embedded at the anchor of each silicon bridge along 〈1 1 0〉 direction as the piezoresistive sensing element. Finite element analysis has been used to determine the location of maximum stress and the simulation results are verified with the experimental measurements. Taking advantage of the high sensitivity of SiNWs, the fabricated ring-shaped sensor is capable of detecting small displacement in nanometer ranges with a sensitivity of 13.4 × 10−3 µm−1 in the z-direction. This tri-axial force sensor also shows high linearity (>99.9%) to the applied load and no obvious hysteresis is observed. The developed SiNW-based tri-axial force sensor provides new opportunities to implement sensing capability on medical instruments such as guidewires and robotic surgical grippers, where ultra-miniaturization and high sensitivity are essential.
Article
Full-text available
The objective of this paper is to provide a review of recent advances in automatic vibration- and audio-based fault diagnosis in machinery using condition monitoring strategies. It presents the most valuable techniques and results in this field and highlights the most profitable directions of research to present. Automatic fault diagnosis systems provide greater security in surveillance of strategic infrastructures, such as electrical substations and industrial scenarios, reduce downtime of machines, decrease maintenance costs, and avoid accidents which may have devastating consequences. Automatic fault diagnosis systems include signal acquisition, signal processing, decision support, and fault diagnosis. The paper includes a comprehensive bibliography of more than 100 selected references which can be used by researchers working in this field.
Article
Full-text available
Surface electromyographic signals provide useful information about motion intentionality. Therefore, they are a suitable reference signal for control purposes. A continuous classification scheme of five upper limb movements applied to a myoelectric control of a robotic arm is presented. This classification is based on features extracted from the bispectrum of four EMG signal channels. Among several bispectrum estimators, this paper is focused on arithmetic mean, median, and trimmed mean estimators, and their ensemble average versions. All bispectrum estimators have been evaluated in terms of accuracy, robustness against outliers, and computational time. The median bispectrum estimator shows low variance and high robustness properties. Two feature reduction methods for the complex bispectrum matrix are proposed. The first one estimates the three classic means (arithmetic, harmonic, and geometric means) from the module of the bispectrum matrix, and the second one estimates the same three means from the square of the real part of the bispectrum matrix. A two-layer feedforward network for movement's classification and a dedicated system to achieve the myoelectric control of a robotic arm were used. It was found that the classification performance in real-time is similar to those obtained off-line by other authors, and that all volunteers in the practical application successfully completed the control task.
Article
Full-text available
Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.
Article
Full-text available
Doppler-tipped coronary guide-wires (FW) are well-established tools in interventional cardiology to quantitatively analyze coronary blood flow. Doppler wires are used to measure the coronary flow velocity reserve (CFVR). The CFVR remains reduced in some patients despite anatomically successful coronary angioplasty. It was the aim of our study to test the influence of changes in flow profile on the validity of intra-coronary Doppler flow velocity measurements in vitro. It is still unclear whether turbulent flow in coronary arteries is of importance for physiologic studies in vivo. We perfused glass pipes of defined inner diameters (1.5-5.5 mm) with heparinized blood in a pulsatile flow model. Laminar and turbulent flow profiles were achieved by varying the flow velocity. The average peak velocity (APV) was recorded using 0.014 inch FW. Flow velocity measurements were also performed in 75 patients during coronary angiography. Coronary hyperemia was induced by intra-coronary injection of adenosine. The APV maximum was taken for further analysis. The mean luminal diameter of the coronary artery at the region of flow velocity measurement was calculated by quantitative angiography in two orthogonal planes. In vitro, the measured APV multiplied with the luminal area revealed a significant correlation to the given perfusion volumes in all diameters under laminar flow conditions (r2 > 0.85). Above a critical Reynolds number of 500--indicating turbulent flow--the volume calculation derived by FW velocity measurement underestimated the actual rate of perfusion by up to 22.5 % (13 +/- 4.6 %). In vivo, the hyperemic APV was measured irrespectively of the inherent deviation towards lower velocities. In 15 of 75 patients (20%) the maximum APV exceeded the velocity of the critical Reynolds number determined by the in vitro experiments. Doppler guide wires are a valid tool for exact measurement of coronary flow velocity below a critical Reynolds number of 500. Reaching a coronary flow velocity above the velocity of the critical Reynolds number may result in an underestimation of the CFVR caused by turbulent flow. This underestimation of the flow velocity may reach up to 22.5 % compared to the actual volumetric flow. Cardiologists should consider this phenomena in at least 20 % of patients when measuring CFVR for clinical decision making.
Article
Full-text available
Higher order statistics (HOS) are used to characterize acoustic emission events in ring-type samples from steel pipes for the oil industry. Cumulants are used twofold. First, diagonal bispectrum allows the separation of the primary (original) deformation from the reflections produced mainly in the suppressed chord. These longitudinal reflections can hardly be extracted via second-order methods, e.g., wavelet packets and power spectra, because they are partially masked by both Gaussian and non-Gaussian noise. Second, a cumulant-based independent component analysis may be used before the bispectrum, as a preprocessing complement, in the case of multiple-source and multiple-channel recordings. This algorithm suppresses the mutual influence of the sources and sensors. Sample registers were acquired by wide-frequency-range transducers (100-800 kHz) and digitalized by a 2.5-MHz, 12-bit analog-to-digital converter.
Article
Full-text available
This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. Because matrix computation is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while the standard chunking SVM algorithm scales somewhere between linear and cubic in the training set size. SMO's computation time is dominated by SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. On realworld sparse data sets, SMO can be more than 1000 times...
Article
As the connection at the proximal tip plays an important role for sensing guidewires, we compared various sensing guidewires with regard to their proximal connectors. The strengths and weaknesses of each are discussed and recommendations for future development are provided. A literature search limited to the English language for the time period from the 1960s to the 2010s has been performed on the USPTO database, Espacenet, and Web of Science. The results have been categorized on the basis of the connector design. A comprehensive overview and classification of proximal connectors for sensing guidewires used for cardiovascular interventions is presented. The classification is based on both the type of connector (fixed or removable) and the type of connection (physical, wireless, or a combination). Considering the complexity of the currently prototyped and tested connectors, future connector development will necessitate an easy and cost-effective manufacturing process that can ensure safe and robust connections.
Conference Paper
In minimal invasive cardiac surgery (MICS), the surgeon is missing haptic feedback of the guide wire for navigation through the vessels. A wide range of guide wires with various properties and performance characteristics are available to reduce the risk of complications during the intervention. This paper presents a force sensing guide wire for cardiac catheterization, which provides the surgeon a haptic feedback on the guide wire tip. Three conventional wires for the recanalization of chronic total coronary occlusions (CTO) are investigated and general design requirements for the force sensing guide wire are determined. A comparison of the developed with the conventional guide wires concerning the tip force is conducted. The measured tip force of the force sensing guide wire is 50 mN, which is slightly lower than two of the compared conventional wires. As a result, the guide wire offers a good compromise between a soft, low-traumatic and hard guide wire tip.
Article
In theory, kernel support vector machines (SVMs) can be reformulated to linear SVMs. This reformulation can speed up SVM classifications considerably, in particular, if the number of support vectors is high. For the widely-used Gaussian radial basis function (RBF) kernel, however, this theoretical fact is impracticable because the reproducing kernel Hilbert space (RKHS) of this kernel has infinite dimensionality. Therefore, we derive a finite-dimensional approximative feature map, based on an orthonormal basis of the kernel’s RKHS, to enable the reformulation of Gaussian RBF SVMs to linear SVMs. We show that the error of this approximative feature map decreases with factorial growth if the approximation quality is linearly increased. Experimental evaluations demonstrated that the approximative feature map achieves considerable speed-ups (about 18-fold on average), mostly without loosing classification accuracy. Therefore, the proposed approximative feature map provides an efficient SVM evaluation method with minimal loss of precision.
Conference Paper
Major depression (MD) is associated with increased cardiovascular risk. Although alterations in autonomic regulation have been proposed as one potential pathophysiological mechanism to explain this comorbidity, studies using standard HRV features in depressed subjects have been inconclusive. In this study, 48 patients with MD and 48 healthy controls (HC) were randomly assigned to an audio-visual task with two different versions: one emotionally neutral (N) and the other emotionally arousing (E). ECG signal (lead II) was collected at 250 Hz, and point process nonlinear analysis of heartbeat dynamics was performed to obtain instantaneous features from standard time-domain analysis, as well as spectral (LF, HF, LF/HF) and bispectral (LL, LH, and HH) analysis. Mean values of all features were computed over the 30s segment of the emotional elicitation session. Only bispectral parameters LH and HH were significantly different between patients and HC (p<0.02). Our results suggest that time-varying nonlinear dynamics of parasympathetic activity are significantly reduced in MD compared to HC in response to emotional elicitation. We conclude that instantaneous bispectral analysis could be a promising tool for assessment of autonomic modulation in MD.
Article
Objective: To examine the clinical outcome of percutaneous coronary intervention where the procedure was complicated by vessel perforation. Setting: Tertiary referral centre. Methods: The procedural records of 6245 patients undergoing coronary intervention were reviewed. In 52 patients (0.8%) the procedure was complicated by vessel perforation, ranging from wire exit to free flow of contrast into the pericardial space. The majority of lesions treated were complex (37% type B, 59% type C) and 9 of 52 (17%) were chronic occlusions. Ten patients (19%) received abciximab. Four underwent rotational atherectomy (8%). Results: In 28 of 52 patients (54%) the perforation was benign and managed conservatively without the development of haemodynamically significant sequelae. In 24 of 52 (46%) a significant pericardial effusion ensued requiring drainage. Of these 24 procedures 6 had involved the treatment of a chronic occlusion (25%). Eight of the 24 patients were referred for emergency bypass surgery (33%), 3 of whom died. Of the remaining 16 not referred for surgery, 3 died. Of the 10 procedures complicated by vessel perforation where abciximab had been administered, 9 (90%) led to pericardial tamponade. Latterly 2 vessel perforations were successfully treated by the deployment of a covered stent. Conclusions: Coronary artery perforation with sequelae during intervention is rare—26 of 6245 (0.4%). This complication was seen in the treatment of chronic occlusions, which are therefore not risk-free procedures. The development of pericardial tamponade carries a high mortality. While prompt surgical intervention may be life saving, expertise in the use of covered stents may provide a valuable rescue option for this serious complication. Caution should be exercised where coronary perforation occurs and abciximab has been used.
Conference Paper
Epilepsy is a neurological disorder which affects the nervous system. Epileptic seizures are due to hyperactivity in certain parts of the brain. Automatic seizure detection helps in diagnosis and monitoring of epilepsy especially during long term recordings of EEG. This paper presents the bispectrum analysis of electroencephalogram (EEG) for the detection of epilepsy. Bispectrum is a higher order spectrum. It characterizes the nonlinearities in the signal. Features extracted from the bispectrum of EEG are applied to the neural network classifier to detect normal and epileptic EEGs. The classification accuracy of 81.67% is obtained. The results demonstrate that the proposed features are more effective in differentiating epileptic EEG as compared to features from the conventional power spectrum.
Book
The first idea for a book about haptic devices was born in 2003. Originally intended as an addition to the dissertation of Thorsten A. Kern, it was soon thought of filling a gap: The regrettably small number of comprehensive recapitulating publications on haptics available for, e.g,. a technically interested person, confronted with the task of designing a haptic device for the first time. In 2004, besides a considerable number of conference proceedings, journals or PhD-theses, no document was available giving a summary of the major findings of this challenging subject. The first edition was edited by Thorsten A. Kern during a Post-Doc time. It was funded by the Deutsche Forschungsgemeinschaft (DFG) with special regard to the consolidation of the design methodology for haptic devices. In 2008, the German version Entwicklung Haptischer Geräte and in 2009 the English version Engineering Haptic Devices were published by Springer. In 2010 the idea of a second edition of the book was born. With the change of Dr. Kern from university to an industrial employer, also the attention shifted from mainly kinaesthetic to tactile devices. This made severe gaps in the first edition eminent. In parallel, science made great progress in understanding the individual tactile modalities, blurring the borders between different old concepts of the same perception, offering now an opportunity to find an engineering approach to more than the pure vibrotactile perception. It took however until the year 2013, that the work on the second edition started. In that year, Christian Hatzfeld finished his dissertation dealing with the perception of vibrotactile forcesHe took the lead in editing the second edition. As well as the first edition, this work was also funded by the DFG, pointing out the importance of an adapted design approach for haptic systems. With the cooperation of Springer and the series editors, the second edition of this book was integrated in the Springer Series on Touch and Haptic Systems, as we felt that the design of task-specific haptic interfaces would be complemented well by the other works in these series. Naturally, there is scientific progress and new and relevant results for the design of haptic systems are published every day. We like to fill the gap between the static form of the written book with this website. Occasionally, we will present information about new findings and recent developments on this page as an addition to the book contents. We hope, that not only the book but also this website will alleviate the work of students and engineers new to the exciting and challenging development of haptic systems and serves as a useful resource for all developers. Find more information about the book at www.hapticdevices.eu
Article
The electrocardiogram (ECG) is the P-QRS-T wave representing the information about the condition of the heart. The shape and size of the ECG signal may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human eye and may indicate a particular cardiac abnormality. Also, the ECG is highly subjective, the symptoms may appear at random in the time scale. Hence computer assisted methods can help physicians to monitor cardiac health easily and accurately. The ECG signal is nonlinear and non-stationary in nature. These subtle variations can be captured using non-linear dynamical Higher Order Statistics (HOS) techniques. Bispectrum is the third order spectra which captures information beyond mean and standard deviation. In this work we have analyzed five types of beats namely: Normal, Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). The extracted bispectrum features are subjected to principal component analysis (PCA) for dimensionality reduction. These principal components were fed to four layered feed forward neural network and Least Square-Support Vector Machine (LS-SVM) for automated pattern identification. In our work, we have obtained highest average accuracy of 93.48%, average sensitivity and specificity of 99.27% and 98.31% respectively using LS-SVM with Radial Basis Function (RBF) kernel. Our system is clinically ready to run on large amount of data sets.
Article
En el presente estudio se valora la incidencia, los parámetros relacionados y la evolución de la perforación coronaria por guía intracoronaria durante la realización de angioplastia. Se analizaron retrospectivamente 4.353 procedimientos consecutivos, correspondientes a 6.994 lesiones tratadas en 8 años. Se detectó perforación coronaria por guía en 15 casos (0,35%). Ésta se relacionó con el número de guías hidrófilas usadas (odds ratio [OR] = 2,33; intervalo de confianza [IC] del 95%, 1,34-4,05) y el tratamiento de oclusiones crónicas (OR = 3,31; IC del 95%, 1,05-10,46). En 7 casos (46,7%) hubo taponamiento cardiaco, 6 de manera subaguda; 3 se solucionaron con pericardiocentesis y 4 requirieron drenaje quirúrgico. El taponamiento se relacionó con el número de guías utilizadas (p = 0,039) y el uso de abciximab (p = 0,016). No se produjeron muertes.
Article
Obstructive Sleep Apnea (OSA) is a respiratory disorder with serious consequences that is characterized by repetitive cessation of breathing for more than 10s often associated with a drop of more than 4% in the blood's Oxygen saturation level. The gold standard for OSA diagnosis is full-night Polysomnography (PSG), which is a time-consuming, inconvenient, and costly assessment. On the other hand, our team has showed that the analysis of tracheal respiratory sounds recorded during wakefulness holds promises to be used as a simple and effective tool for screening moderate and severe OSA. In this paper, we examine the nonlinear characteristics of tracheal breath sounds and the possibility to extract features from Higher Order Spectra (HOS) for OSA screening. The data used in this study were recorded during wakefulness in two body positions, supine and upright, and during mouth and nose breathing. We estimated the bispectrum of the sounds in each respiratory cycle, calculated the median bifrequencies and the energy of the bispectrum, and investigated any statistically significant differences between the extracted features in two groups of non-OSA and severe OSA data. The differences in the features between body positions and nose/mouth breathing were also looked at. One-way ANOVA revealed significant differences in the features between non-OSA individuals and those with severe OSA. The results encourage the use of these features in future studies for OSA screening.
Chapter
Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.
Article
The goal of much effort in recent years has been to provide a simplified interpretation of the electroencephalogram (EEG) for a variety of applications, including the diagnosis of neurological disorders and the intraoperative monitoring of anesthetic efficacy and cerebral ischemia. Although processed EEG variables have enjoyed limited success for specific applications, few acceptable standards have emerged. In part, this may be attributed to the fact that commonly usedsignal processing tools do not quantify all of the information available in the EEG. Power spectral analysis, for example, quantifies only power distribution as a function offrequency, ignoring phase information. It also makes the assumption that thesignal arises from alinear process, thereby ignoring potential interaction betweencomponents of the signal that are manifested asphase coupling, a common phenomenon in signals generated fromnonlinear sources such as the central nervous system (CNS). This tutorial describes bispectral analysis, a method of signal processing that quantifies the degree of phase coupling between the components of a signal such as the EEG. The basic theory underlying bispectral analysis is explained in detail, and information obtained from bispectral analysis is compared with that available from thepower spectrum. The concept of abispectral index is introduced. Finally, several model signals, as well as a representative clinical case, are analyzed using bispectral analysis, and the results are interpreted.
Chapter
Medical and industrial telemanipulation and assistive systems require force sensors as a part of the effector interacting with objects or organs. The importance of haptic perception for medical application is still growing and numerous research groups are focusing on utilizing haptic technology in that area. Within the HapCath project, a concept to improve navigation of catheters and guide wires during radiological interventions is put into practice. In this paper a piezoresistive force sensor for measuring contact forces within the arteries is presented. It is manufactured to be mounted at the tip of guide wires with a diameter of 360 μ m. Two different sensor structures are introduced in terms of measurement principles, manufacturing technologies and characterized by measured sensor performance. Technological challenges, e.g. the packaging for medical applications and its effect on sensor performance, are addressed. Future applications for tactile measurements are shown and the advantages, but also the challenges of tactile measurement technology based on piezoresistive force sensors are summarized. KeywordsMicro Force Sensor-Catheterization-Guide Wire-Haptic Feedback-Force Vector
Conference Paper
No doubt reliable electroencephalogram (EEG) analysis methods capable of predicting the epileptic seizures would be of great value. This paper presents a new approach, based on bispectrum analysis of EEGs and artificial neural network(ANN), which predicts seizures in seven patients with epilepsy. Eight channels of EEG were collected in each subject in Epilepsy Center of Xijing Hospital. The maximum magnitude and the weighted center of EEG bispectrum (WCOB) were extracted from the EEG bispectrum contour and a four layer(24-10-2-1) ANN was employed for prediction. Training and testing the ANN used the 'leave one out' method. The proposed system was able to correctly predict the succedent seizures and prediction times are from 12 to 24 seconds, prior to the onset of epileptic seizures.
Article
Sleep scoring is one of the most important methods for diagnosis in psychiatry and neurology. Sleep staging is a time consuming and difficult task conducted by sleep specialists. The purposes of this work are to automatic score the sleep stages and to help to sleep physicians on sleep stage scoring. In this work, a novel data preprocessing method called k-means clustering based feature weighting (KMCFW) has been proposed and combined with k-NN (k-nearest neighbor) and decision tree classifiers to classify the EEG (electroencephalogram) sleep into six sleep stages including awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3, REM, and non-sleep (movement time). First of all, frequency domain features belonging to sleep EEG signal have been extracted using Welch spectral analysis method and composed 129 features from EEG signal relating each sleep stages. In order to decrease the features, the statistical features comprising minimum value, maximum value, standard deviation, and mean value have been used and then reduced from 129 to 4 features. In the second phase, the sleep stages dataset with four features has been weighted by means of k-means clustering based feature weighting. Finally, the weighted sleep stages have been automatically classified into six sleep stages using k-NN and C4.5 decision tree classifier. In the classification of sleep stages, the k values of 10, 20, 30, 40, 50, and 60 in k-NN classifier have been used and compared with each other. In the experimental results, while sleep stages has been classified with 55.88% success rate using k-NN classifier (for k value of 40), the weighted sleep stages with KMCFW has been recognized with 82.15% success rate k-NN classifier (for k value of 40). And also, we have investigated the relevance between sleep stages and frequency domain features belonging to EEG signal. These results have demonstrated that proposed weighting method have a considerable impact on automatic determining of sleep stages. This system could be used as an online system in the automatic scoring of sleep stages and helps to sleep physicians in the sleep scoring process.
Article
In this paper, we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% from support vector machine. We observed that the classification rate is high for a support vector machine classifier compared to self-organizing map-based approach.
Article
Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. Research efforts have reported with increasing confirmation that the support vector machines (SVM) have greater accurate diagnosis ability. In this paper, breast cancer diagnosis based on a SVM-based method combined with feature selection has been proposed. Experiments have been conducted on different training-test partitions of the Wisconsin breast cancer dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The performance of the method is evaluated using classification accuracy, sensitivity, specificity, positive and negative predictive values, receiver operating characteristic (ROC) curves and confusion matrix. The results show that the highest classification accuracy (99.51%) is obtained for the SVM model that contains five features, and this is very promising compared to the previously reported results.
Article
The aims of this study were to determine the incidence of coronary artery perforation by intracoronary guide wires during angioplasty, to identify associated factors, and to assess outcomes. The retrospective analysis covered 4,353 consecutive procedures, corresponding to a total 6,994 lesions treated over a period of 8 years. Coronary artery perforation by guide wires occurred in 15 cases (0.35%). Perforation was associated with the number of hydrophilic wires used (odds ratio=2.33; 95% confidence interval, 1.34-4.05) and treatment of chronic occlusions (odds ratio=3.31; 95% confidence interval, 1.05-10.46). Cardiac tamponade occurred in seven cases (46.7%), six of which were subacute. Three cases were resolved by pericardiocentesis, while four required surgical drainage. Cardiac tamponade was associated with the number of guide wires used (P=.039) and the use of abciximab (P=.016). No death occurred.
Article
The goal of much effort in recent years has been to provide a simplified interpretation of the electroencephalogram (EEG) for a variety of applications, including the diagnosis of neurological disorders and the intraoperative monitoring of anesthetic efficacy and cerebral ischemia. Although processed EEG variables have enjoyed limited success for specific applications, few acceptable standards have emerged. In part, this may be attributed to the fact that commonly used signal processing tools do not quantify all of the information available in the EEG. Power spectral analysis, for example, quantifies only power distribution as a function of frequency, ignoring phase information. It also makes the assumption that the signal arises from a linear process, thereby ignoring potential interaction between components of the signal that are manifested as phase coupling, a common phenomenon in signals generated from nonlinear sources such as the central nervous system (CNS). This tutorial describes bispectral analysis, a method of signal processing that quantifies the degree of phase coupling between the components of a signal such as the EEG. The basic theory underlying bispectral analysis is explained in detail, and information obtained from bispectral analysis is compared with that available from the power spectrum. The concept of a bispectral index is introduced. Finally, several model signals, as well as a representative clinical case, are analyzed using bispectral analysis, and the results are interpreted.
Article
To examine the clinical outcome of percutaneous coronary intervention where the procedure was complicated by vessel perforation. Tertiary referral centre. The procedural records of 6245 patients undergoing coronary intervention were reviewed. In 52 patients (0.8%) the procedure was complicated by vessel perforation, ranging from wire exit to free flow of contrast into the pericardial space. The majority of lesions treated were complex (37% type B, 59% type C) and 9 of 52 (17%) were chronic occlusions. Ten patients (19%) received abciximab. Four underwent rotational atherectomy (8%). In 28 of 52 patients (54%) the perforation was benign and managed conservatively without the development of haemodynamically significant sequelae. In 24 of 52 (46%) a significant pericardial effusion ensued requiring drainage. Of these 24 procedures 6 had involved the treatment of a chronic occlusion (25%). Eight of the 24 patients were referred for emergency bypass surgery (33%), 3 of whom died. Of the remaining 16 not referred for surgery, 3 died. Of the 10 procedures complicated by vessel perforation where abciximab had been administered, 9 (90%) led to pericardial tamponade. Latterly 2 vessel perforations were successfully treated by the deployment of a covered stent. Coronary artery perforation with sequelae during intervention is rare--26 of 6245 (0.4%). This complication was seen in the treatment of chronic occlusions, which are therefore not risk-free procedures. The development of pericardial tamponade carries a high mortality. While prompt surgical intervention may be life saving, expertise in the use of covered stents may provide a valuable rescue option for this serious complication. Caution should be exercised where coronary perforation occurs and abciximab has been used.
Article
We investigated whether embolic particles could be detected as high-intensity transient signals (HITS) with a Doppler guide wire during percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) We also assessed whether these signals could be reduced using a distal protection (DP) device. Embolization of thrombi and plaque components to the microcirculation is a major complication of PCI in patients with AMI. Embolic particles running in the cerebral artery are detected as HITS by transcranial Doppler ultrasound. We prospectively studied 16 consecutive patients with AMI who underwent direct PCI within 24 h after the onset of symptoms. A PercuSurge GuardWire (MedtronicAVE, Santa Rosa, California) was used as the DP device. Eight patients were randomly assigned to the non-DP group, and the remaining eight were assigned to the DP group. Coronary flow velocity was recorded continuously from before the first balloon inflation to after balloon deflation. All patients in the non-DP group had HITS detected (12 +/- 9 counts) within five consecutive beats (4 +/- 1 beat) after balloon deflation, but none were detected in any of the patients in the DP group. The Doppler guide wire can be used to visually detect and count emboli as HITS, and the DP device is effective for prevention of distal embolization.
Article
This correspondence is concerned with the definition and properties of the bispectrum of complex-valued signals. The symmetry properties and the relationship between all forms of third-order cumulants of complex signals are investigated. It is shown that all cumulants (for different position of the complex conjugate) are related by simple transformations. This article also investigates autoregressive modeling of complex-valued signals using third-order cumulants. It is shown that modeling of complex-valued signals requires a different approach from modeling of real-valued signals.
Miniaturized Haptic User-Interface for Heart Catheterizations Concept and Design
  • T Opitz
  • C Neupert
  • T Rossner
  • N Stefanova
  • T Meiss
  • R Werthschtzky
T. Opitz, C. Neupert, T. Rossner, N. Stefanova, T. Meiss, R. Werthschtzky, Miniaturized Haptic User-Interface for Heart Catheterizations Concept and Design, Biomedical Engineering/Biomedizinische Technik, 2013.
Entwurf und Realisierung eines haptischen Assistenzsystems für Herzkatheteruntersuchungen, Doctoral dissertation
  • T Opitz
T. Opitz, Entwurf und Realisierung eines haptischen Assistenzsystems für Herzkatheteruntersuchungen, Doctoral dissertation, Technische Universitt (2016).
Comparing ffr tools: new wires and a pressure micro-catheter
  • Kern
M. Kern, Comparing ffr tools: new wires and a pressure micro-catheter, Cath. Lab. Dig. 24 (2016) 4-9.
Combination Sensor Guidewire and Methods of Use
  • M Ahmed
  • E A Oliver
  • J Puleo
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