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Biomedical Signal Processing - Science topic

Application of signal processing techniques on biomedical signals
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Publications related to Biomedical Signal Processing (2,587)
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Poster
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Biomedical signals are very useful for evaluating the well-being of a human. The rapid increase in the generation of physiological data, together with the development of big data intelligence, has enabled us to extract new insights from massive physiological signals. These include, among others, bioelectrical signals, like electrocardiogram (ECG),...
Article
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Health sensors and remote measurement tools have saved lives through the possibility of continuous monitoring and intervention tools, and over the years their use has expanded to non-medical areas such as fitness and perceived well-being. This expansion has led to unprecedented data collection, especially since biomedical sensors are now ubiquitous...
Article
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Understanding how movement is controlled by the central nervous system remains a major challenge, with ongoing debate about basic features underlying this control. In current established views, the concepts of motor neuron recruitment order, common synaptic input to motor neurons, muscle synergies, are usually addressed separately and therefore see...
Preprint
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The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biological imaging, digital pathology, molecular imaging, microscopy, and associated computational imagi...
Article
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Blind Source Separation is an optimization method frequently used in statistical signal processing applications. There are many application areas such as ambient listening, denoising, signal detection, and so on. In this study, a new Strength Pareto Evolutionary Algorithm 2-based signal separation method is proposed, which combines Multi-Objective...
Article
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Dietary patterns can be the primary reason for many chronic diseases such as diabetes and obesity. State-of-the-art wearable sensor technologies can play a critical role in assisting patients in managing their eating habits by providing meaningful statistics on critical parameters such as the onset, duration, and frequency of eating. For an accurat...
Article
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With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and...
Article
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Independent Component Analysis (ICA) is a common method exploited in different biomedical signal processing applications, especially in noise removal of electroencephalography (EEG) signals. Among different existing ICA algorithms, FastICA is a popular method with less complexity, which makes it more suitable for practical implementation. However,...
Cover Page
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We welcome book chapter contributions centered but not exclusively) on the following themes: Authors are welcome to propose a new book chapter title related to the book topics. Potential topics include but are not limited to the following: Chapter 1: Introduction to non-invasive biomedical signals for healthcare Chapter 2: Signal Acquisition, Pre...
Article
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Epilepsy diagnosis is a medical care process that requires considerable transformation, mainly in developed countries, to provide efficient and effective care services taking into consideration the low number of available neurologists, especially in rural areas. EEG remains the most common test used to diagnose epilepsy. In recent years, there has...
Article
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Background and objective The automated detection of atrial activations (AAs) recorded from intracardiac electrograms (IEGMs) during atrial fibrillation (AF) is challenging considering their various amplitudes, morphologies and cycle length. Activation time estimation is further complicated by the constant changes in the IEGM active zones in complex...
Article
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The HeartPy Python toolkit for analysis of noisy signals from heart rate measurements is an excellent tool to use in conjunction with novel wearable sensors. Nevertheless, most of the work to date has focused on applying the toolkit to data measured with commercially available sensors. We demonstrate the application of the HeartPy functions to data...
Article
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Portable, custom-made electronic dynamometry for the foot and ankle is a promising assessment method that enables foot and ankle muscle function to be established in healthy participants and those affected by chronic conditions. Diabetic peripheral neuropathy (DPN) can alter foot and ankle muscle function. This study assessed ankle toque in partici...
Preprint
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Cardiovascular diseases (CVDs) are the world's leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive non-invasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about...
Article
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Biomedical signal processing and data analysis play pivotal roles in the advanced medical expert system solutions. Signal processing tools are able to diminish the potential artifact effects and improve the anticipative signal quality. Data analysis techniques can assist in reducing redundant data dimensions and extracting dominant features associa...
Article
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Recently, robotic catheterization has enhanced the outcomes of cardiovascular interventions. Meanwhile, the roles of operator’s natural behavior in the robot-assisted intravascular procedures need more attention. In this paper, operators’ hand activities related to endovascular tool manipulation are studied to explore how operators’ hand motions ai...
Article
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Monitoring blood glucose levels is a vital indicator of diabetes mellitus management. The mainstream techniques of glucometers are invasive, painful, expensive, intermittent, and time-consuming. The ever-increasing number of global diabetic patients urges the development of alternative non-invasive glucose monitoring techniques. Recent advances in...
Article
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Objective: Internal physiological processes govern multiple state variables within the human body. Estimating these from point process-type bioelectric and biochemical observations is a challenge. Here we seek to estimate cortisol-related energy production and sympathetic arousal based on point process and continuous-valued data while permitting a...
Conference Paper
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For semantic segmentation, U-Net provides an end-to-end trainable framework to detect multiple class objects from background. Due to its great achievements in computer vision tasks, U-Net has broadened its application to biomedical signal processing, especially, segmentation of waveforms in ECG signal. Despite its superior performance for QRS compl...
Article
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Background: Uterine activity (UA) monitoring is an essential element of pregnancy management. The gold-standard intrauterine pressure catheter (IUPC) is invasive and requires ruptured membranes, while the standard-of-care, external tocodynamometry (TOCO)’s accuracy is hampered by obesity, maternal movements, and belt positioning. There is an urgent...
Article
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Additive white Gaussian noise level estimation has found its application in many fields such as biomedical signal processing, communication system, and image processing. Many methods have been proposed with different output accuracy, system complexity, power consumption, and speed. In this paper, three of the most well-known and largely used algori...
Article
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This article is devoted to rebuilt for an important fragmentary of wavelet models of Biomedical Signal Processing. These models were built using Haar’s fragmentary-unchanged wavelets as well as Doubechi wavelets. The Haar’s fragmentary-unchanged wavelets models has a high accuracy for Biomedical signals on digital work, and this provides doctors fo...
Article
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Through wearable technology, several chronic diseases are diagnosed by long-term monitoring of vital signs specifically ECG, EMG, EEG biosignals. Such prolonged monitoring and transmitting these multiple recordings may decline the battery power of wireless wearable device. This work aims at preserving the battery power of wireless wearables by join...
Article
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Introduction. Neurophysiological phenomena, such as muscle coactivation, have been used to identify motor tasks requiring greater joint stability in healthy people or with movement disorders. Nonetheless, there are many ways to calculate the coactivation index (CI). This article aimed to create a processing pipeline to calculate the muscular CI by...
Article
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Biomedical Signal Processing and Control Volume 77, August 2022, 103844 Periodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly application Kubilay Muhammed Sunnetci, Sezer Ulukaya, Ahmet Alkan https://doi.org/10.1016/j.bspc.2022.103844
Article
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Heart Rate Variability (HRV) evaluates the autonomic nervous system regulation and can be used as a monitoring tool in conditions such as cardiovascular diseases, neuropathies and sleep staging. It can be extracted from the electrocardiogram (ECG) and the photoplethysmogram (PPG) signals. Typically, the HRV is obtained from the ECG processing. Bein...
Article
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Emotional recognition through exploring the electroencephalography (EEG) characteristics have been widely performed in recent studies. Nonlinear analysis and feature extraction methods for understanding complex dynamical phenomena are associated with the EEG patterns of different emotions. The phase space reconstruction (PSR) is a typical nonlinear...
Chapter
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Biomedical signals represent the variation in electric potential due to physiological processes and are recorded through certain types of sensors or electrodes. In practice, the biomedical signals are typically complex and non-stationary. This makes adaptive data-driven techniques a natural choice for processing biomedical signals. Signal processin...
Article
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In all developing countries, the application of biomedical signals has been growing, and there is a potential interest to apply it to healthcare management systems. However, with the existing infrastructure, the system will not provide high-end support for the transfer of signals by using a communication medium, as biomedical signals need to be cla...
Preprint
Full-text available
Emotional recognition through exploring the electroencephalography (EEG) characteristics has been widely performed in recent studies. Nonlinear analysis and feature extraction methods for understanding the complex dynamical phenomena are associated with the EEG patterns of different emotions. The phase space reconstruction is a typical nonlinear te...
Article
Full-text available
Over the years, the privacy of a biomedical signal processing is protected using the encryption techniques design and meta-heuristic algorithms which are significant domain and it will be more significant shortly. Present biomedical signal processing research contained security because of their critical role in any developing technology that contai...
Article
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Biomedicine signal processing algorithms offer a variety of opportunities to improve performance. In this study, a new wavelet delta-function (WDF) method was proposed to effectively detect pathology from ECG signals. In this method, the delta function is determined for each value of the ECG signal. This is done through the WDF sum with a coefficie...
Article
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The recent trend in healthcare is to use the automated biomedical signals processing for an augmented and precise diagnosis. In this context, an original approach is presented for categorization of stress and non-stress classes by processing the multichannel Electroencephalogram (EEG) signals. The EEG signals are decomposed by using the “Empirical...
Article
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The novel coronavirus, renamed SARS-CoV-2 and most commonly referred to as COVID-19, has infected nearly 44.83 million people in 224 countries and has been designated SARS-CoV-2. In this study, we used ‘web of Science’, ‘Scopus’ and ‘goggle scholar’ with the keywords of “SARS-CoV-2 detection” or “coronavirus 2019 detection” or “COVID 2019 detection...
Article
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Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spat...
Article
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Otitis media (OM), known as inflammation of the middle ear, is a condition especially seen in children. To carry out a definitive diagnosis of the discomfort that manifests itself with various symptoms such as pain in the ear, fever, and discharge, the eardrum in the middle ear should be examined by a specialist. In this study, a convolution neural...
Article
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Image processing aims to enhance the image's quality such that it is simple for both people and robots to understand. Medical image processing and Biomedical signal processing have many conceptual similarities. Medical image processing involves evaluation, enhancement, and presentation. The focus of medical imaging is on obtaining photographs for b...
Preprint
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FFT algorithm is one of the most applied algorithmsin digital signal processing. Digital signal processing hasgradually become important in biomedical application. Herehardware implementation of FFTs have found useful appli-cations for bio-wearable devices. However, for these devices, low-power and low-area are of utmost importance.In this report,...
Article
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Abstract Radar has been shown potentially to be used for non‐contact sensing of biosignals in a more comfortable and easier way than wearable and contact devices. By detecting and extracting body motions linked to physiological activities, accurate estimation of the heart rate is possible. However, the detection and estimation of heartbeat signal i...
Article
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The impact of neurodegenerative disorders is twofold; they affect both quality of life and healthcare expenditure. In the case of Parkinson’s disease, several strategies have been attempted to support the pharmacological treatment with rehabilitation protocols aimed at restoring motor function. In this scenario, the study of upper limb control mech...
Book
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PREFACE: Our bodies are always providing us information about our health. Physiological instruments can be used to record this information. They can measure heart rate, blood pressure, oxygen saturation levels, blood glucose, nerve conduction, brain activity, and other things, as well as many other things. Traditionally, these kinds of measurement...
Article
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The study of walking pattern also known as gait is affected by several underlying musculoskeletal and neurological factors. All humans have contrasting gait pattern however the pattern lies within a predictable range for all human with no underlying health disorders affecting gait. However, deviation from a regular distribution range can indicate u...
Article
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Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Elect...
Article
Full-text available
Over the years, the privacy of a biomedical signal processing is protected using the encryption techniques design and meta-heuristic algorithms which are significant domain and it will be more significant shortly. Present biomedical signal processing research contained security because of their critical role in any developing technology that contai...
Article
Full-text available
This study proposes two denoising autoencoder (DAE) models with discrete cosine transform (DCT) and discrete wavelet transform (DWT), namely DCT–DAE and DWT–DAE, to remove electrode motion artifacts in noisy electrocardiography (ECG). Initially, the discrete cosine transform and discrete wavelet transform efficiently removed the high-frequency nois...
Article
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Time-frequency analysis is of necessity for wrist pulse signal due to its complexity, among which, empirical mode decomposition (EMD) algorithm and its improved noise-assisted versions (such as ensemble EMD, noise-assisted multivariate EMD (NA-MEMD) and very recently median EMD) are deemed to be the most representative ones. In this study, we provi...
Article
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An Electro cardiogram is commonly used in biomedical signal processing. It is used to monitor minor electrical changes in the human body. The electrical changes originate due to the function of heart. The anomalies of heart are found by ECG. In this work the Whale optimization algorithm is used to de-noising the ECG signal. The Whale optimization a...
Article
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In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive u...
Article
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Epileptic seizures are temporary episodes of convulsions, where approximately 70 percent of the diagnosed population can successfully manage their condition with proper medication and lead a normal life. Over 50 million people worldwide are affected by some form of epileptic seizures, and their accurate detection can help millions in the proper man...
Article
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Measuring cardiac activity from the chest using an accelerometer is commonly referred to as seismocardiography. Unfortunately, it cannot provide clinically valid data because it is easily corrupted by motion artefacts. This paper proposes two methods to improve peak detection from noisy seismocardiography data. They rely on discrete wavelet transfo...
Article
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Background Heart rate (HR) is an important vital sign for evaluating the physiological condition of a newborn infant. Recently, for measuring HR, novel RGB camera-based non-contact techniques have demonstrated their specific superiority compared with other techniques, such as dopplers and thermal cameras. However, they still suffered poor robustnes...
Article
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The number of deaths due to cardiovascular and respiratory diseases is increasing annually. Cardiovascular diseases with high mortality rates, such as strokes, are frequently caused by atrial fibrillation without subjective symptoms. Chronic obstructive pulmonary disease is another condition in which early detection is difficult owing to the slow p...
Article
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Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency and cost-effective nature. From this signal, several biomarkers can be collected, such as heart and respiration rate. For the usual acquisition scenarios, PPG is an artefact-ridden signal, which mandates the need for the designated classification algorithms to be a...
Article
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The analysis of cardiac vibration signals has been shown as an interesting tool for the follow-up of chronic pathologies involving the cardiovascular system, such as heart failure (HF). However, methods to obtain high-quality, real-world and longitudinal data, that do not require the involvement of the patient to correctly and regularly acquire the...
Article
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Electromyogram (EMG) classification is a key technique in EMG-based control systems. Existing EMG classification methods, which do not consider EMG features that have distribution with skewness and kurtosis, have limitations such as the requirement to tune hyperparameters. In this paper, we propose a neural network based on the Johnson SU translati...