Techniques for Decomposition of EMG Signals

2011 ◽  
pp. 467-477
Author(s):  
Arun Kumar Wadhwani ◽  
Sulochana Wadhwani
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Muscle Fatigue ◽  
Cross Correlation ◽  
Neuromuscular Disorders ◽  
Clinical Importance ◽  
Neuromuscular Control ◽  
Diagnostic Information ◽  
Self Organizing Map ◽  
Statistical Pattern

The information extracted from the EMG recordings is of great clinical importance and is used for the diagnosis and treatment of neuromuscular disorders and to study muscle fatigue and neuromuscular control mechanism. Thus there is a necessity of efficient and effective techniques, which can clearly separate individual MUAPs from the complex EMG without loss of diagnostic information. This chapter deals with the techniques of decomposition based on statistical pattern recognition, cross-correlation, Kohonen self-organizing map and wavelet transform.

Techniques for Decomposition of EMG Signals

2011 ◽  
pp. 187-197
Author(s):  
Arun Kumar Wadhwani ◽  
Sulochana Wadhwani
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Muscle Fatigue ◽  
Cross Correlation ◽  
Neuromuscular Disorders ◽  
Clinical Importance ◽  
Neuromuscular Control ◽  
Diagnostic Information ◽  
Self Organizing Map ◽  
Statistical Pattern

The information extracted from the EMG recordings is of great clinical importance and is used for the diagnosis and treatment of neuromuscular disorders and to study muscle fatigue and neuromuscular control mechanism. Thus there is a necessity of efficient and effective techniques, which can clearly separate individual MUAPs from the complex EMG without loss of diagnostic information. This chapter deals with the techniques of decomposition based on statistical pattern recognition, cross-correlation, Kohonen self-organizing map and wavelet transform.

scholarly journals CLASSIFICATION OF EEG SIGNALS FOR DETECTION OF EPILEPTIC SEIZURES BASED ON WAVELETS AND STATISTICAL PATTERN RECOGNITION

2014 ◽  
Vol 26 (02) ◽  
pp. 1450021 ◽  
Author(s):  
Dragoljub Gajic ◽  
Zeljko Djurovic ◽  
Stefano Di Gennaro ◽  
Fredrik Gustafsson
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Epileptic Seizures ◽  
Feature Space ◽  
Epileptic Activity ◽  
Statistical Pattern Recognition ◽  
Eeg Signals ◽  
Eeg Data ◽  
Statistical Pattern

The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. The detection of epileptic activity is, therefore, a very demanding process that requires a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper describes an automated classification of EEG signals for the detection of epileptic seizures using wavelet transform and statistical pattern recognition. The decision making process is comprised of three main stages: (a) feature extraction based on wavelet transform, (b) feature space dimension reduction using scatter matrices and (c) classification by quadratic classifiers. The proposed methodology was applied on EEG data sets that belong to three subject groups: (a) healthy subjects, (b) epileptic subjects during a seizure-free interval and (c) epileptic subjects during a seizure. An overall classification accuracy of 99% was achieved. The results confirmed that the proposed algorithm has a potential in the classification of EEG signals and detection of epileptic seizures, and could thus further improve the diagnosis of epilepsy.

Automated feature extraction for the classification of humanin vivo13C NMR spectra using statistical pattern recognition and wavelets

1996 ◽  
Vol 35 (6) ◽  
pp. 834-840 ◽  
Author(s):  
A. Rosemary Tate ◽  
Des Watson ◽  
Stephen Eglen ◽  
Theodores N. Arvanitis ◽  
E. Louise Thomas ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Nmr Spectra ◽  
Statistical Pattern Recognition ◽  
Statistical Pattern ◽  
Automated Feature Extraction
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