Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals

Congestive heart failure (CHF) is a chronic heart condition associated with debilitating symptoms that result in increased mortality, morbidity, healthcare expenditure and decreased quality of life. Electrocardiogram (ECG) is a noninvasive and simple diagnostic method that may demonstrate detectable changes in CHF. However, manual diagnosis of ECG signal is often subject to errors due to the small amplitude and duration of the ECG signals, and in isolation, is neither sensitive nor specific for CHF diagnosis. An automated computer-aided system may enhance the diagnostic objectivity and reliability of ECG signals in CHF. We present an 11-layer deep convolutional neural network (CNN) model for CHF diagnosis herein. This proposed CNN model requires minimum pre-processing of ECG signals, and no engineered features or classification are required. Four different sets of data (A, B, C and D) were used to train and test the proposed CNN model. Out of the four sets, Set B attained the highest accuracy of 98.97%, specificity and sensitivity of 99.01% and 98.87% respectively. The proposed CNN model can be put into practice and serve as a diagnostic aid for cardiologists by providing more objective and faster interpretation of ECG signals.