In this paper we present a method to analyze five types with fifteen wavelet families for eighteen different EMG signals. A comparison study is also given to show performance of various families after modifying the results with back propagation Neural Network. This is actually will help the researchers with the first step of EMG analysis. Huge sets of results (more than 100 sets) are proposed and then classified to be discussed and reach the final
Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is de...
This paper illustrates the classification of Electromyography (EMG) signals through designing and ...
Abstract—This paper introduces the wristwork pattern recognition with the method of Auto-regressive ...
Classification of EMG signals is an important area in biomedical signal processing. Several algorith...
Electromyography signal can be used for biomedical applications. It is complicated in interpretation...
Electromyograph (EMG) Signal is a biomedical signal that non-stationary, making it difficult to dete...
A range of signal processing techniques have been adopted and developed as a methodology which can b...
Electromyography (EMG) signal processing has been investigated remarkably regarding various applicat...
Electromyography (EMG) signal processing has been investigated remarkably regarding various applicat...
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an ...
Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most power...
Besides prosthetic device control and neuromuscular disease identification, electromyography (EMG) ...
Electromyograph signal (EMG) is a non-stationary biomedical signal, making it difficult todetermine ...
This paper presents the design, optimization and performance evaluation of artificial neural netwo...
Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in e...
Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is de...
This paper illustrates the classification of Electromyography (EMG) signals through designing and ...
Abstract—This paper introduces the wristwork pattern recognition with the method of Auto-regressive ...
Classification of EMG signals is an important area in biomedical signal processing. Several algorith...
Electromyography signal can be used for biomedical applications. It is complicated in interpretation...
Electromyograph (EMG) Signal is a biomedical signal that non-stationary, making it difficult to dete...
A range of signal processing techniques have been adopted and developed as a methodology which can b...
Electromyography (EMG) signal processing has been investigated remarkably regarding various applicat...
Electromyography (EMG) signal processing has been investigated remarkably regarding various applicat...
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an ...
Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most power...
Besides prosthetic device control and neuromuscular disease identification, electromyography (EMG) ...
Electromyograph signal (EMG) is a non-stationary biomedical signal, making it difficult todetermine ...
This paper presents the design, optimization and performance evaluation of artificial neural netwo...
Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in e...
Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is de...
This paper illustrates the classification of Electromyography (EMG) signals through designing and ...
Abstract—This paper introduces the wristwork pattern recognition with the method of Auto-regressive ...