A multimodal parallel method for left ventricular dysfunction identification based on phonocardiogram and electrocardiogram signals synchronous analysis

Abstract

<abstract> <p>Heart failure (HF) is widely acknowledged as the terminal stage of cardiac disease and represents a global clinical and public health problem. Left ventricular ejection fraction (LVEF) measured by echocardiography is an important indicator of HF diagnosis and treatment. Early identification of LVEF reduction and early treatment is of great significance to improve LVEF and the prognosis of HF. This research aims to introduce a new method for left ventricular dysfunction (LVD) identification based on phonocardiogram (ECG) and electrocardiogram (PCG) signals synchronous analysis. In the present study, we established a database called Synchronized ECG and PCG Database for Patients with Left Ventricular Dysfunction (SEP-LVDb) consisting of 1046 synchronous ECG and PCG recordings from patients with reduced (n = 107) and normal (n = 699) LVEF. 173 and 873 recordings were available from the reduced and normal LVEF group, respectively. Then, we proposed a parallel multimodal method for LVD identification based on synchronous analysis of PCG and ECG signals. Two-layer bidirectional gate recurrent unit (Bi-GRU) was used to extract features in the time domain, and the data were classified using residual network 18 (ResNet-18). This research confirmed that fused ECG and PCG signals yielded better performance than ECG or PCG signals alone, with an accuracy of 93.27%, precision of 93.34%, recall of 93.27%, and F1-score of 93.27%. Verification of the model's performance with an independent dataset achieved an accuracy of 80.00%, precision of 79.38%, recall of 80.00% and F1-score of 78.67%. The Bi-GRU model outperformed Bi-directional long short-term memory (Bi-LSTM) and recurrent neural network (RNN) models with a best selection frame length of 3.2 s. The Saliency Maps showed that SEP-LVDPN could effectively learn features from the data.</p> </abstract>