ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
Spectral and wavelet based assessment of congestive heart failure patients
Computers in Biology and Medicine
Wavelets and filter banks: theory and design
IEEE Transactions on Signal Processing
Information extraction from sound for medical telemonitoring
IEEE Transactions on Information Technology in Biomedicine
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
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In this paper, our aim is to determine two photic stimulation frequencies, which would represent normal and diseased subjects, separately. Following features were extracted for this aim; linear prediction coefficients (LPC), subband wavelet entropy (SWE), subband wavelet variance (SWV), and relative power (RP). After extracting related features, analysis of variance (ANOVA) statistical test was used for the statistical evaluation of these features. According to the obtained results, wavelet transform-based entropy gave the best results to determine the representing stimulation frequencies. As a result, 29Hz stimulation frequency was determined as the most representative frequency for normal subjects, whereas 8Hz stimulation frequency was determined as the most representative frequency for diseased subjects.