A Recurrent Probabilistic Neural Network for EMG Pattern Recognition

A Recurrent Probabilistic Neural Network for EMG Pattern Recognition

Toshio Tsuji (Hiroshima University, Japan), Nan Bu (Hiroshima University, Japan) and Osamu Fukuda (National Institute of Advanced Industrial Science and Technology, Japan)
Copyright: © 2006 |Pages: 24
DOI: 10.4018/978-1-59140-848-2.ch006
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Abstract

In the field of pattern recognition, probabilistic neural networks (PNNs) have been proven as an important classifier. For pattern recognition of EMG signals, the characteristics usually used are: (1) amplitude, (2) frequency, and (3) space. However, significant temporal characteristic exists in the transient and non-stationary EMG signals, which cannot be considered by traditional PNNs. In this article, a recurrent PNN, called recurrent log-linearized Gaussian mixture network (R-LLGMN), is introduced for EMG pattern recognition, with the emphasis on utilizing temporal characteristics. The structure of R-LLGMN is based on the algorithm of a hidden Markov model (HMM), which is a routinely used technique for modeling stochastic time series. Since R-LLGMN inherits advantages from both HMM and neural computation, it is expected to have higher representation ability and show better performance when dealing with time series like EMG signals. Experimental results show that R-LLGMN can achieve high discriminant accuracy in EMG pattern recognition.

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Table of Contents
Preface
Rezaul Begg, Joarder Kamruzzaman, Ruhul Sarker
Acknowledgments
Rezaul Begg, Joarder Kamruzzaman, Ruhul Sarker
Chapter 1
Joarder Kamruzzaman, Rezaul Begg, Ruhul Sarker
Artificial neural network (ANN) is one of the main constituents of the artificial intelligence techniques. Like in many other areas, ANN has made a... Sample PDF
Overview of Artificial Neural Networks and their Applications in Healthcare
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Chapter 2
Wolfgang I. Schollhorn, Jörg M. Jager
This chapter gives an overview of artificial neural networks as instruments for processing miscellaneous biomedical signals. A variety of... Sample PDF
A Survey on Various Applications of Artificial Neural Networks in Selected Fields of Healthcare
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Chapter 3
Chris D. Nugent, Dewar D. Finlay, Mark P. Donnelly, Norman D. Black
Electrical forces generated by the heart are transmitted to the skin through the body’s tissues. These forces can be recorded on the body’s surface... Sample PDF
The Role of Neural Networks in Computerized Classification of the Electrocardiogram
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Chapter 4
G. Camps-Valls, J. F. Guerrero-Martinez
In this chapter, we review the vast field of application of artificial neural networks in cardiac pathology discrimination based on... Sample PDF
Neural Networks in ECG Classification: What is Next for Adaptive Systems?
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Chapter 5
Peng Li, Kap L. Chan, Sheng Fu, Shankar M. Krishnan
n this chapter, a new concept learning-based approach is presented for abnormal ECG beat detection to facilitate long-term monitoring of heart... Sample PDF
A Concept Learning-Based Patient-Adaptable Abnormal ECG Beat Detector for Long-Term Monitoring of Heart Patients
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Chapter 6
Toshio Tsuji, Nan Bu, Osamu Fukuda
In the field of pattern recognition, probabilistic neural networks (PNNs) have been proven as an important classifier. For pattern recognition of... Sample PDF
A Recurrent Probabilistic Neural Network for EMG Pattern Recognition
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Chapter 7
Toshio Tsuji, Kouji Tsujimura, Yoshiyuki Tanaka
In this chapter, an advanced intelligent dual-arm manipulator system teleoperated by EMG signals and hand positions is described. This myoelectric... Sample PDF
Myoelectric Teleoperation of a Dual-Arm Manipulator Using Neural Networks
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Chapter 8
Markad V. Kamath, Adrian R. Upton, Jie Wu, Harjeet S. Bajaj, Skip Poehlman, Robert Spaziani
The artificial neural networks (ANNs) are regularly employed in EEG signal processing because of their effectiveness as pattern classifiers. In this... Sample PDF
Artificial Neural Networks in EEG Analysis
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Chapter 9
Robert T. Davey, Paul J. McCullagh, H. Gerry McAllister, H. Glen Houston
We have analyzed high and low level auditory brainstem response data (550 waveforms over a large age range; 126 were repeated sessions used in... Sample PDF
The Use of Artificial Neural Networks for Objective Determination of Hearing Threshold Using the Auditory Brainstem Response
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Chapter 10
Rezaul Begg, Joarder Kamruzzaman
This chapter provides an overview of artificial neural network applications for the detection and classification of various gaits based on their... Sample PDF
Movement Pattern Recognition Using Neural Networks
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Chapter 11
G. Camps-Valls, J. D. Martin-Guerrero
Recently, important advances in dosage formulations, therapeutic drug monitoring (TDM), and the emerging role of combined therapies have resulted in... Sample PDF
Neural and Kernal Methods for Therapeutic Drug Monitoring
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Chapter 12
Yos S. Morsi, Subrat Das
This chapter describes the utilization of computational fluid dynamics (CFD) with neural network (NN) for analysis of medical devices. First, the... Sample PDF
Computational Fluid Dynamics and Neural Network for Modeling and Simulations of Medical Devices
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Chapter 13
Balázs Benyo
This chapter deals with the analysis of spontaneous changes occurring in two physiological parameters: the cerebral blood flow and respiration.... Sample PDF
Analysis of Temporal Patterns of Physiological Parameters
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