Techniques of EMG signal analysis: detection, processing, classification and applications

419 . Different techniques for EMG signal processing

Electromyography signal can be used for biomedical applications. It is complicated in interpretation, so it acquires advanced methods for detection, decomposition, processing, and classification. The techniques of EMG signal analysis such as: filtering, wavelet transform, and modeling will be presented in this paper to provide efficient and effective ways of understanding the signal. A comparis...

the clustering and classification data mining techniques in insurance fraud detection:the case of iranian car insurance

با توجه به گسترش روز افزون تقلب در حوزه بیمه به خصوص در بخش بیمه اتومبیل و تبعات منفی آن برای شرکت های بیمه، به کارگیری روش های مناسب و کارآمد به منظور شناسایی و کشف تقلب در این حوزه امری ضروری است. درک الگوی موجود در داده های مربوط به مطالبات گزارش شده گذشته می تواند در کشف واقعی یا غیرواقعی بودن ادعای خسارت، مفید باشد. یکی از متداول ترین و پرکاربردترین راه های کشف الگوی داده ها استفاده از ر...

Surface Electromyography Signal Processing and Classification Techniques

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...

Multiscale Peak Detection in Emg Signal Analysis

Electromyography (EMG) represents a method used for the acquisition of electrical activity produced by skeletal muscles. The following analysis based upon the digital signal processing methods can be used to find relation between their neurological activation and separate motor units firing with the typical frequency of 7-20 Hz. Signals obtained can be used for analysis of biomechanics of the h...

Advanced Signal Processing Techniques for Single Trial Electroencephalography Signal Classification for Brain Computer Interface Applications