A new approach for the diagnosis of congestive heart failure and arrhythmia based on ECG signals: İ-1B-YİÖ+LSTM

The heart, one of the body's most sensitive organs, is one of the organs that has the most vital role in maintaining life. Electrocardiography is a diagnostic technique that measures and records the electrical activity induced by heart muscle contraction and relaxation. Electrocardiogram (ECG) signals are obtained by recording the amplitudes of electrical activities in the heart in the time domain. ECG signals are generally used to diagnose diseases such as arrhythmias. In this study, a new approach based on Down Sampling-One Dimensional-Local Binary Pattern (1D-DS-LBP) and Long Short-Term Memory (LSTM) is proposed for the diagnosis of Congestive Heart Failure (CHF) and Arrhythmia (ARR) from ECG signals. For ECG signals, a conversion method called 1D-DS-LBP has been proposed. This method uses the larger or smaller information created by each value on one-dimensional signals with their neighbors. The histograms of the 1D local binary patterns calculated from the reduced signal groups obtained by this method are combined and given as an input to the LSTM model. LSTM was applied to the ECG signals that applied both unidirectional and bidirectional I-1D-YIS conversion. To test the proposed approach; A total of 972 ECG signals containing 3 different status information, CHF, ARR and Normal Sinus Rhythm (NSR), were used. Signals were taken from the MIT-BIH and BIDMC databases. Experiments were carried out in different scenarios. When the success rates obtained are compared with other studies in the literature, it is seen that acceptable high results are obtained with the proposed approach. The obtained ECG diagnostic performance values varied between %96,80 and %99,79. It is thought that the proposed disease diagnosis system can also be applied to different medical signals.