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Convolutional Neural Network Fused With Recurrent Network for ECG-Based Detection of Hypertrophic Cardiomyopathy
1Department of ECE, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.
Insights
This study introduces a novel hybrid deep learning model for early and accurate detection of Hypertrophic Cardiomyopathy (HCM) using electrocardiogram (ECG) signals. The model effectively captures spatial and temporal patterns, demonstrating high accuracy and clinical value for diagnosing this heart condition.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Hypertrophic Cardiomyopathy (HCM) is a cardiac condition characterized by left ventricle thickening, increasing risks of atrial fibrillation, heart failure, and sudden cardiac death.
- Early detection of HCM via Electrocardiogram (ECG) is crucial for mitigating mortality risks.
- Existing diagnostic methods often struggle to integrate both spatial and temporal ECG data, limiting their reliability.
Purpose of the Study:
- To develop and validate a hybrid deep learning (DL) network for accurate and reliable detection of Hypertrophic Cardiomyopathy (HCM) from ECG signals.
- To enhance diagnostic capabilities by effectively capturing both spatial and temporal features within ECG data.
- To improve early diagnosis of HCM, thereby reducing associated mortality risks.
Main Methods:
- A hybrid DL network, termed CNNFRN, was developed, integrating Kalman filter pre-processing, Empirical Mode Decomposition (EMD), statistical, and medical feature extraction.
- Feature fusion was performed using a Deep Belief Network (DBN) with Jensen-Shannon distance.
- The CNNFRN model, combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) components, was trained using the Adam optimizer under a supervised framework.
Main Results:
- The CNNFRN model achieved high performance metrics: 0.940 accuracy, 1.000 sensitivity, 0.913 specificity, and 0.956 F1-score.
- Validation was conducted on the PTB Diagnostic ECG Database and hospital-based ECG databases (Shaoxing and Ningbo).
- The model demonstrated robust and effective early detection of HCM, highlighting its significant clinical utility.
Conclusions:
- The proposed hybrid DL model accurately detects HCM by synergistically combining advanced feature extraction techniques with a DL architecture that captures complex spatial and temporal ECG patterns.
- The model's strong performance and reliability across diverse datasets underscore its potential for early and effective clinical diagnosis of HCM.
- This approach offers a valuable tool for improving patient outcomes through timely intervention in Hypertrophic Cardiomyopathy.
Background:
Hypertrophic Cardiomyopathy (HCM) affects the left ventricle of the heart, leading to thickening of the ventricular wall and potentially life-threatening conditions, such as atrial fibrillation, cardiac failure, and sudden death. Early and accurate detection of HCM from Electrocardiogram (ECG) signals is critical for reducing mortality risk. However, most existing methods fail to simultaneously capture spatial and temporal patterns in ECG data, resulting in reduced diagnostic reliability.
Method:
This paper proposes a hybrid Deep Learning (DL) network for detecting HCM using the ECG. Initially, input ECG signals are forwarded for pre-processing by Kalman filter. Then, processed signal is fed to feature extraction phase for extracting Empirical Mode Decomposition (EMD), statistical and medical features, which is followed by feature fusion, wherein the optimal feature is merged by Deep Belief Network (DBN) with Jensen-Shannon distance. Moreover, Convolutional Neural Network Fused with Recurrent Network (CNNFRN) performs HCM detection and final detected output is effectively achieved. The proposed CNNFRN combines Kalman Neural Network (CNN) and Recurrent Neural Network (RNN) based on regression modelling. Finally, the model is trained under a supervised framework using the Adam optimizer.
Results:
The proposed model is validated using the PTB Diagnostic ECG Database and Shaoxing and Ningbo Hospital ECG Database. The results show that the proposed CNNFRN model achieved an accuracy of 0.940, sensitivity of 1.000, specificity of 0.913, and an F1-score of 0.956. These findings confirm the model's effectiveness in robust and early detection of HCM, offering significant clinical value.
Conclusion:
The proposed model accurately detects HCM by combining advanced feature extraction and a hybrid deep learning approach that captures both spatial and temporal ECG patterns. It also shows strong performance and reliability across multiple databases, making it valuable for early and effective clinical diagnosis.
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