2012/08/06 by SUBHA D. PUTHANKATTIL, Subha D. Puthankattil, Paul K. Joseph +1 · 115 citations
Computer Science · Mathematics · Medicine · Neuroscience · #Artificial intelligence #Artificial neural network #Blind Source Separation Techniques #Computer science #Digital signal processing #Discrete wavelet transform #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #Electroencephalography #Energy (signal processing) #Feedforward neural network #Mathematics #Medicine #Pattern recognition (psychology) #SIGNAL (programming language) #Signal processing #Speech recognition #Statistics #Waveform #Wavelet #Wavelet transform
paper · doi:10.1142/s0219519412400192
published in Journal of Mechanics in Medicine and Biology 12(04), 1240019 (World Scientific)
openalex publication_date 2012/08/06 · crossref created 2012/08/06 · crossref issued 2012/09/01 · crossref published 2012/09/01 · crossref published-print 2012/09/01 · crossref published-online 2012/10/23 · crossref deposited 2019/08/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31 · crossref indexed 2026/07/31
EEG is useful for the analysis of the functional activity of the brain and a detailed assessment of this non-stationary waveform can provide crucial parameters indicative of the mental state of patients. The complex nature of EEG signals calls for automated analysis using various signal processing methods. This paper attempts to classify the EEG signals of normal and depression patients using well-established signal processing techniques involving relative wavelet energy (RWE) and artificial feedForward neural network. High frequency noise present in the recorded signal is removed using total variation filtering (TVF). Classification of the frequency bands of EEG signals into appropriate detail levels and approximation level is carried out using an eight-level multiresolution decomposition method of discrete wavelet transform (DWT). Parseval's theorem is used for calculating the energy at different resolution levels. RWE analysis gives information about the signal energy distribution at different decomposition levels. Both RWE and feedforward Network are used to classify the signals from normal controls and depression patients. The performance of the artificial neural network was evaluated using the classification accuracy and its value of 98.11% indicates a great potential for classifying normal and depression signals.