Key result
MBFRDH deep learning model achieves ~99% ECG classification accuracy, outperforming existing multiclass techniques.
Why the study?
Existing deep learning models for ECG analysis are either highly complex or show lower scalability when applied to clinical scans.
Absolute Event Rate: 99.04% vs 95.88%
A novel multimodal bioinspired deep learning model demonstrated 99.04% accuracy in classifying ECG signals for heart diseases, outperforming existing models and showing potential for real-time clinical use.
Electrocardiograms (ECGs) are generated by checking different beating patterns of heart, and are widely used for identification of multiple heart-related issues. Existing deep learning models that are proposed for ECG analysis are either highly complex, or showcase lower scalability when applied to clinical scans. To overcome these issues, this text proposes design of a novel multimodal bioinspired feature representation deep learning model for identification of heart-diseases. The proposed model initially collects large-scale ECG datasets, and extracts Fourier, Cosine, iVector, Gabor, and Wavelet components. These components are given to a Grey Wolf Optimization (GWO) based feature selection model, which assists in identification of high-inter-class variance feature sets. This is done via modelling a variance-based fitness function and fusing it with an Iterative Learning Model (ILM) that use feedback-accuracy levels for optimization of selected feature sets. The extracted features are used to incrementally train a custom 1D Binary-Augmented Convolutional Neural Network (1D BACNN) that can be trained for multiclass scenarios. The BACNN Model is trained individually for each of the heart diseases. Each BACNN categorizes input ECG samples between ‘Normal’, and ‘Heart-Disease’ categories. Due to use of this binary-type classification, the proposed model is able to achieve a consistent 99.9% accuracy for multiple heart disease sets, which is found to be higher than most of the existing multiclass techniques. The model was tested for Angina, Arrhythmia, Valve disease, and Congenital heart conditions, and was observed to achieve 3.5% higher precision, 4.9% higher accuracy, with 1.2% increase is computational delay, which makes it highly suitable for real-time clinical use cases.
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Jaisinghani et al. (2023) studied Heart Diseases (Arrhythmia, Congestive Heart Failure, Angina, Valve disease, Congenital heart conditions) (n=1,000,000). Multimodal Bioinspired Feature Representation Deep Learning Model (MBFRDH) vs. Existing deep learning models (SONN, SAM, MS CNN) was evaluated on Classification accuracy. The proposed MBFRDH deep learning model achieved a classification accuracy of 99.04% on a 1 million ECG record dataset, outperforming existing multiclass techniques.
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