ECG signals-based automated diagnosis of congestive heart failure using Deep CNN and LSTM architecture

In humans, Congestive Heart Failure (CHF) refers to the chronic progressive condition that drastically influences the pumping potentiality of the heart muscle. This CHF has the possibility of increasing health expenditure, morbidity, mortality and minimized quality of life. In this context, Electrocardiogram (ECG) is considered as the simplest and a non-invasive diagnosis method that aids in detecting and demonstrating the realizable changes in CHF. However, diagnosing CHF based on manual exploration of ECG signals is frequently impacted by errors as duration and small amplitude of the signals either investigated separately or in the integration is determined to neither specific nor sensitive. At this juncture, the reliability and diagnostic objectivity of ECG signals during the CHF detection process may be enhanced through the inclusion of automated computer-aided system. In this paper, Deep CNN and LSTM Architecture (DCNN-LSTM)-based automated diagnosis system is proposed for detecting CHF using ECG signals. In specific, CNN is included for the purpose of extracting deep features and LSTM is used for attaining the objective of CHF detection using the extracted features. This proposed DCNN-LSTM is evolved with minimal pre-processing of ECG signals and does not involve any classification process or manual engineered features during diagnosis. The experimentation of the proposed DCNN-LSTM conducted using the real time ECG signals datasets confirmed an accuracy of 99.52, sensitivity of 99.31%, specificity of 99.28%, F-Score of 98.94% and AUC of 99.9%, respectively.