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Published on: July 20, 2022
A multimodal parallel method for left ventricular dysfunction identification based on phonocardiogram and
Yajing Zeng1, Siyu Yang2, Xiongkai Yu1
1The Fourth Affiliated Hospital Zhejiang University School of Medicine, Jinhua 321000, China.
Insights
This study introduces a new method for identifying left ventricular dysfunction (LVD) using combined electrocardiogram (ECG) and phonocardiogram (PCG) signals. Fusing these signals improves LVD detection accuracy, aiding early diagnosis and treatment for heart failure patients.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence in Medicine
Background:
- Heart failure (HF) is a critical global health issue, with left ventricular ejection fraction (LVEF) being a key diagnostic and prognostic indicator.
- Early identification of left ventricular dysfunction (LVD) is crucial for improving patient outcomes and managing HF.
- Current diagnostic methods may benefit from novel approaches for enhanced accuracy and efficiency.
Purpose of the Study:
- To develop and validate a novel method for LVD identification using synchronous analysis of electrocardiogram (ECG) and phonocardiogram (PCG) signals.
- To establish a comprehensive database (SEP-LVDb) for training and testing LVD detection models.
- To compare the performance of a multimodal approach against single-modality signal analysis.
Main Methods:
- Creation of the Synchronized ECG and PCG Database for Patients with Left Ventricular Dysfunction (SEP-LVDb) with 1046 recordings.
- Implementation of a parallel multimodal deep learning model utilizing two-layer bidirectional gate recurrent unit (Bi-GRU) for feature extraction and Residual Network 18 (ResNet-18) for classification.
- Synchronous analysis of ECG and PCG signals for LVD identification.
Main Results:
- The fused ECG and PCG signal analysis achieved superior performance compared to using ECG or PCG alone, with an accuracy of 93.27%.
- Independent dataset verification demonstrated the model's robustness, yielding an accuracy of 80.00%.
- The Bi-GRU model outperformed other recurrent neural network architectures (Bi-LSTM, RNN), and Saliency Maps confirmed effective feature learning.
Conclusions:
- Synchronous analysis of ECG and PCG signals offers a promising, accurate, and effective method for identifying left ventricular dysfunction.
- The developed multimodal deep learning approach holds potential for early LVD detection, contributing to improved heart failure management.
- This research underscores the value of integrating multiple physiological signals for enhanced diagnostic capabilities in cardiovascular medicine.
Abstract:
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.
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