Automated Diagnosis of Congestive Heart Failure Using Dual Tree Complex Wavelet Transform and Statistical Features Extracted from 2 Seconds of ECG Signals

Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2seconds duration up to six levels to obtain the coefficients.

Source: Computers in Biology and Medicine - February 6, 2017 Category: Bioinformatics Authors: Vidya K Sudarshan, U Rajendra Acharya, Oh Shu Lih, Muhammad Adam, Tan Jen Hong, Chua Kuang Chua, Chua Kok Poo, Tan Ru San Source Type: research