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Electromyogram (EMG) Signal Categorization in Parkinson’s Disease Tremor Detection by Applying MLP (Multilayer Perceptron) Technique: A Review
Abstract
In recent years, there has been extensive interest in the revelation of different technologies for artificial neural network (ANN) application in various classification of biomedical signals. Several publications have focused on diverse applications of ANN techniques for biomedical signals classification, detection, and processing. This review work gives an overview on the multilayer perceptron (MLP) technique of ANN and how this technology is useful for the classification of EMG signal. This paper will be interesting for those researchers who are studying the classification EMG signal for tremor detection in Parkinson’s disease. Many researchers have worked and also working on Parkinson’s disease tremor by using EMG signal and classifying its feature using ANN. Among those various techniques, MLP technique is highlighted in this work for the classification of EMG signal. The core center of this paper is to review the evolution and research works related to the topic mentioned above.
Drop-on-demand (DOD) printing is widely used in bioprinting for tissue engineering because of little damage to cell viability and cost-effectiveness. However, satellite droplets may be generated during printing, deviating cells from the desired position and affecting printing position accuracy. Current control on cell injection in DOD printing is primarily based on trial-and-error process, which is time-consuming and inflexible. In this paper, a novel machine learning technology based on Learning-based Cell Injection Control (LCIC) approach is demonstrated for effective DOD printing control while eliminating satellite droplets automatically. The LCIC approach includes a specific computational fluid dynamics (CFD) simulation model of piezoelectric DOD print-head considering inverse piezoelectric effect, which is used instead of repetitive experiments to collect data, and a multilayer perceptron (MLP) network trained by simulation data based on artificial neural network algorithm, using the well-known classification performance of MLP to optimize DOD printing parameters automatically. The test accuracy of the LCIC method was 90%. With the validation of LCIC method by experiments, satellite droplets from piezoelectric DOD printing are reduced significantly, improving the printing efficiency drastically to satisfy requirements of manufacturing precision for printing complex artificial tissues. The LCIC method can be further used to optimize the structure of DOD print-head and cell behaviors.
rtificial Neural Network (ANN)-based diagnosis of medical diseases has been takeninto great consideration in recent years. In this paper,two types of ANNs are used toclassify effective diagnosis of Parkinson’s disease. Multi-Layer Perceptron (MLP) withback-propagation learning algorithm and Radial Basis Function (RBF) ANNswere used to differentiate between clinical variables of samples (N= 195) who were sufferingfromParkinson's diseaseand whowerenot. For this purpose, Parkinson's diseasedataset, taken from UCI machine learning database was used. Mean squarednormalized error function was used to measure the usefulness of our networks during trainingsand direct performance calculations. It was observed that MLP is the best classification with 93.22% accuracy for the data set.Also, wegot 86.44% accuracy in RBF classification for the same data set. This technique canassist neurologists to make theirultimate decisionswithout hesitation and more astutely
Parkinson's Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.
The aim of this work was to differentiate patients with essential tremor from patients with Parkinson's disease. The electromyographic signal from the biceps brachii muscle was measured during isometric tension from 17 patients with essential tremor, 35 patients with Parkinson's disease, and 40 healthy controls. The EMG signals were high pass filtered and divided to smaller segments from which histograms were calculated using 200 histogram bins. EMG signal histogram shape was analysed with a feature dimension reduction method, the principal component analysis, and the shape parameters were used to differentiate between different patient groups. The height of the histogram and the side difference between left and right hand were the best discriminators between essential tremor and Parkinson's disease groups. With this method, it was possible to discriminate 13/17 patients with essential tremor from 26/35 patients with Parkinson's disease and 14/17 patients with essential tremor from 29/40 healthy controls.
Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
This paper describes the development of neural network models for noise reduction. The networks used to enhance the performance of modeling captured signals by reducing the effect of noise. Both recurrent and multi-layer Backpropagation neural networks models are examined and compared with different training algorithms. The paper presented is to illustrate the effect of training algorithms and network architecture on neural network performance for a given application.
Domiciliary oxygen therapy uses oxygen concentrators, which sound alarms in the event of trouble, and the kind of alarm depends on that of the trouble. To lessen the burden on caregivers, we have proposed a system classifying alarm sounds using a multilayer perceptron neural network and transmitting the recognized trouble to a mon-itor center in a hospital by phone cable. This system has been constructed by a digital signal processor board as an external unit. The difference among kinds of alarm sounds are characterized by the length of alarm sounds and that of alarm periods. Therefore, a short-time Fourier transform is used as a feature extraction method. Experiments using real-world devices indicate that the proposed system works well under noisy conditions.
The aim of this study is to find an automobile pricing model using artificial neural networks (ANN). As commonly known, pricing is a difficult matter for both automobile manufacturers and buyers/sellers. Developing a neural networks based on the technical properties of automobiles will allow both groups to price autos with great ease. However, in this study there are two basic assumptions. The first is that supply and demand are in equilibrium and they have no positive or negative effect on pricing. Alfred Marshall [Alfred Marshall. (1920). Principles of economics (Vol. 9). Macmillan] describes how the price and availability of goods and services are related to consumer demand in competitive markets in the Law of supply and demand. The second is that our data set represents the whole market since we will determine market prices of other automobiles according to the network that is trained by this dataset. Proposed novel model estimates prices of automobiles on a stable market from their technical and physical properties.
To answer the questions of how information about the physical world is sensed, in what form is information remembered, and how does information retained in memory influence recognition and behavior, a theory is developed for a hypothetical nervous system called a perceptron. The theory serves as a bridge between biophysics and psychology. It is possible to predict learning curves from neurological variables and vice versa. The quantitative statistical approach is fruitful in the understanding of the organization of cognitive systems. 18 references.
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) and antiparkinsonian medication have proved to be effective treatments for tremor in Parkinson's disease. To date it is not known how and to what extent STN DBS alone and in combination with antiparkinsonian medication alters the pathophysiology of resting and postural tremor in idiopathic Parkinson's disease. The purpose of this study was to examine the effects of STN DBS and antiparkinsonian medication on the neurophysiological characteristics of resting and postural hand tremor in Parkinson's disease. Resting and postural hand tremor were recorded using accelerometry and surface electromyography (EMG) from 10 Parkinson's disease patients and 10 matched control subjects. The Parkinson's disease subjects were examined under four treatment conditions: (i) off treatment; (ii) STN DBS; (iii) medication; and (iv) medication plus STN DBS. The amplitude, EMG frequency, regularity, and 1-8 Hz tremor-EMG coherence were analysed. Both STN DBS and medication reduced the amplitude, regularity and tremor-EMG coherence, and increased the EMG frequency of resting and postural tremor in Parkinson's disease. STN DBS was more effective than medication in reducing the amplitude and increasing the frequency of resting and postural tremor to healthy physiological levels. These findings provide strong evidence that effective STN DBS normalizes the amplitude and frequency of tremor. The findings suggest that neural activity in the STN is an important modulator of the neural network(s) responsible for both resting and postural tremor genesis in Parkinson's disease.
Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.
Correct discrimination of essential tremor from Parkinson's tremor is a major problem in clinical neurology as minor differences in the tremor patterns are hard to distinguish. Mathematical analysis of tremor signals recorded non-invasively has been widely accepted for tremor differentiation. However, classification of tremor signals collected from electromyograph or accelerometer, based on time and frequency domain techniques has limited accuracy because of overlapping frequency range and non-stationary nature of those signals. This paper describes a simple, non-invasive decision making logic method for discrimination of tremor. Wavelet transform based feature extraction technique in combination with feed forward type artificial neural network is proposed. Fractal dimensions of wavelet features of the decomposed detailed coefficients are used as the feature matrix. The neural network classified the tremor sEMG signals with 91.66% accuracy and 100% in case of accelerometer signals. Although, the classification accuracy of sEMG signal is comparable to that of accelerometer but the localized involuntary vibratory nature of tremor at the extremities of human body puts accelerometer as a better option in cases where tremor fails to excite the muscle. This proposed classification algorithm adds strength to the non-invasive signal detection methods at reduced cost and higher sensitivity.
Artificial Neural Network (ANN)-based diagnosis of medical diseases has been taken into great consideration in recent years. In this paper, two types of ANNs are used to classify effective diagnosis of Parkinson's disease. Multi- Layer Perceptron (MLP) with back-propagation learning algorithm and Radial Basis Function (RBF) ANNs were used to differentiate between clinical variables of samples (N = 195) who were suffering from Parkinson's disease and who were not. For this purpose, Parkinson's disease data set, taken from UCI machine learning database was used. Mean squared normalized error function was used to measure the usefulness of our networks during trainings and direct performance calculations. It was observed that MLP is the best classification with 93.22% accuracy for the data set. Also, we got 86.44% accuracy in RBF classification for the same data set. This technique can assist neurologists to make their ultimate decisions without hesitation and more astutely.
In this paper we present the initial results using an artificial neural network to predict the onset of Parkinson's Disease tremors in a human subject. Data for the network was obtained from implanted deep brain electrodes. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings.
This paper concentrates on Electromyogram (EMG) signal processing with the emphasis on Neuropathies and Myopathies Disease.
This review paper also will focus on the structure and learning algorithm of the classification techniques with potential
of further expansion are identified for future research. This paper discussed more into depth on two algorithms which is Detrended
Fluctuation Analysis for feature extraction and Support Vector Machine for classification.
It is proposed that electromyographic (EMG) patterns can be
analyzed and classified by neural networks for EMG-controlled prosthetic
members. The thrust of this work is that neural networks make EMG
pattern recognition much easier and more efficient; thus, use of EMG
control in a prosthetic arm/hand would involve much less physical and
mental effort on the part of the subject. The FFT-analyzed pattern for a
one-channel surface EMG of the flexor digitorium superficialis can be
recognized by the standard back-propagation neural network.
Classification of the five finger movements based on the recognition of
their patterns is successfully accomplished
This paper describes the development of neural network models for noise reduction. The networks used to enhance, the performance of modeling captured signals by reducing the effect of noise. Both recurrent and multi-layer Backpropagation neural networks models are examined and compared with different training algorithms. The paper presented is to illustrate the effect of training algorithms and network architecture on neural network performance for a given application.
It is always crucial to estimate process capability index (PCI) when the quality characteristic does not follow normal distribution, however skewed distributions come about in many processes. The classical method to estimate process capability is not applicable for non-normal processes. In the existing methods for non-normal processes, probability density function (pdf) of the process or an estimate of it is required. Estimating pdf of the process is a hard work and resulted PCI by estimated pdf may be far from real value of it. In this paper an artificial neural network is proposed to estimate PCI for right skewed distributions without appeal to pdf of the process. The proposed neural network estimates PCI using skewness, kurtosis and upper specification limit as input variables. Performance of proposed method is validated by simulation study for different non-normal distributions. Finally, a case study using the actual data from a manufacturing process is presented.
This article introduces the area of scientific study of human movement. It is primarily intended for readers who wish to form a judgement on the usefulness of scientific movement analysis techniques in the treatment process of patients with abnormal movement patterns. With a focus on the analysis of human locomotion, the paper outlines the historical development of a biomechanical approach towards the understanding of human movement patterns. This approach alone proves to be inadequate in supplying reliable information on neuromuscular control of movement. It follows that electromyographic techniques are essential for this purpose. Scientific literature reveals relevant practical usability of such information. This is the rationale for a review of the historical, physiological, technical and methodological background of electromyographic analysis of movement. The field of management and rehabilitation of motor disability is identified as one important application area. On the basis of relevant literature, the present paper asserts that scientific analysis of human movement patterns can materially affect patient treatment. It provides evidence that patient management and rehabilitation processes in central neurological disorders can be improved through electromyographic techniques. In particular, this evidence supports the use of electromyography for surgical planning in children with cerebral palsy. The paper concludes with a view on future directions in research, development and applications of scientific analysis of human movement. Copyright 1998 Elsevier Science B.V.
Using a wavelet analysis approach, it is possible to investigate better the transient and intermittent behavior of multiple electromyographic (EMG) signals during ballistic movements in Parkinsonian patients. In particular, a wavelet cross-correlation analysis on surface signals of two different shoulder muscles allows us to evidence the related unsteady and synchronization characteristics. With a suitable global parameter extracted from local wavelet power spectra, it is possible to accurately classify the subjects in terms of a reliable statistic and to study the temporal evolution of the Parkinson's disease level. Moreover, a local intermittency measure appears as a new promising index to distinguish the low-frequency behavior from normal subjects to Parkinsonian patients.
The aim of this study was to evaluate a variety of traditional and novel surface electromyography (SEMG) characteristics of biceps brachii muscle in patients with Parkinson's disease (PD) and compare the results with the healthy old and young control subjects. Furthermore, the aim was to define the optimal biceps brachii loading level that would most likely differentiate patients from controls. The results indicated that such nonlinear SEMG parameters as %Recurrence, %Determinism and SEMG distribution kurtosis, correlation dimension and sample entropy were significantly different between the PD patients and healthy controls. These novel nonlinear parameters, unlike traditional spectral or amplitude parameters, correlated with the Unified Parkinson's Disease Rating Scale (UPDRS) and finger tapping scores. The most significant between group differences were found in the loading condition where no additional weights were applied in isometric elbow flexion. No major difference of SEMG characteristics was detected between old and young control subjects. In conclusion, the novel SEMG parameters can differentiate the patients with PD from healthy control subjects and these parameters may have potential in the assessment of the severity of PD.
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