Application of Deep Neural Network for Congestive Heart Failure Detection Using ECG Signals

Abstract

Congestive heart failure (CHF) is a common serious heart disease that requires a number of clinical examinations to diagnose, which are costly and time-consuming. Electrocardiogram (ECG) is widely used in the diagnosis of various cardiovascular diseases due to its advantages of non-invasive, convenient and cheap, so the automatic CHF detection algorithm based on ECG signals can overcome the above shortcomings and has great application prospects. In this paper, inspired by the idea of DenseNet in computer version, we refined it to be applicable to CHF detection task, thus improving the diagnosis accuracy of the model. Secondly, to improve the robustness of the algorithm, we built a CHF database on PhysioBank, which contained more diverse data compared with similar studies, and conducted experiments on the built database. Finally, we presented an evaluation method based on the “inter-patient” pattern to evaluate the performance of the method more objectively. The results show that our algorithm can efficiently detect CHF with accuracy, sensitivity and specificity up to 94.97%, 89.38% and 99.50%, respectively. The algorithm proposed in this study can provide reliable references for doctors, and can be used in portable devices to realize real-time monitoring for patients.