Signal-dependent wavelets for electromyogram classification

Neural network adaptive wavelets for signal representation and classification

Electromyogram signal based hypovigilance detection

In recent years, driver drowsiness and driver inattention are the major causes for road accidents leading to severe traumas such as physical injuries, deaths, and economic losses. This necessitates the need for a system that can alert the driver on time, whenever he is drowsy or inattentive. Previous research works report the detection of either drowsiness or inattention only. In this work, we ...

Myoelectric Signal Segmentation And Classification Using Wavelets Based Neural Networks

In this paper a method for Myoelectric signal (MES) segmentation and classification is proposed. The classical moving average technique augmented with Principal Components Analysis (PCA), and timefrequency analysis were used for segmentation. Multiresolution Wavelet Analysis (MRWA) was adopted as an effective feature extraction technique while Artificial Neural Networks (ANN) was used for MES c...

Wavelets and Self-organising Maps in Electromyogram (emg) Analysis

Wavelets are a powerful tool for biomedical signal processing. Wavelets are used for the processing of signals that are non-stationary and time varying. The EMG signal contains transient signals related to muscle activity. EMG signals have typically many transient components, which are very interesting to isolate and classify according to their physiological significance. Wavelet based denoisin...

Adaptive Wavelets for Signal Analysis

Construction of higher multiplicity wavelets adapted to a particular task is discussed. The proposed method uses parameterization of wavelet matrices. Large classes of wavelet matrices are searched to minimize a given cost function on training data. The method is applied to a problem of detecting and possibly removing a disturbance of a certain kind from a signal. The importance of choosing the...