A new approach for congestive heart failure and arrhythmia classification using downsampling local binary patterns with LSTM

Abstract

Electrocardiogram (ECG) is a vital diagnosis approach for the rapid explication and detection of various heart diseases, especially cardiac arrest, sinus rhythms, and heart failure. For this purpose, in this study, a different perspective based on downsampling one-dimensional-local binary pattern (1D-DS-LBP) and long short-term memory (LSTM) is presented for the categorization of Electrocardiogram (ECG) signals. A transformation method named 1DDS-LBP has been presented for Electrocardiogram signals. The 1D-DS-LBP method processes the bigness smallness relationship between neighbors. According to the proposed method, by downsampling the signal, the histograms of 1D local binary patterns (1D-LBP) calculated from the obtained signal groups are collected and included as a reference to the long short-term memory structure. The long short-term memory structure has been applied to 1D-DS-LBP conversion applied ECG signals with both unidirectional and bidirectional. To test the proposed approach, ECG signals of three (3) different states of congestive heart failure (CHF), arrhythmia (ARR), and normal sinus rhythm (NSR) consisting of 972 signals were used. Signals were taken from the MIT-BIH and BIDMC databases. Experiments were carried out in various scenarios. We observed that the success rate of the proposed approach obtained very high classification accuracies compared to other studies in the literature. The obtained ECG diagnostic performance values varied between 96.80% and 99.79%. Based on this, this approach has a high potential to have a wide field of study in medical applications.

Topics & Concepts