Key result
Novel AM-RNN framework achieves ~99% accuracy for ECG classification, outperforming standard machine learning models.
Why the study?
Effective and accurate classification of ECG signals remains challenging due to noisy data and complexity in distinguishing various cardiac patterns.
Does an AM-RNN model with optimized feature selection improve the classification accuracy of ECG signals compared to traditional deep learning models?
Does an AM-RNN model with optimized feature selection improve the classification accuracy of ECG signals compared to traditional deep learning models?
An advanced deep learning framework utilizing AM-RNN with MBO feature selection demonstrates highly accurate classification of ECG signals, potentially aiding automated cardiac diagnosis.
AM-RNN model yields high ECG classification accuracy; hypothesis-generating and should not yet change clinical practice.
Electrocardiogram (ECG) signals are essential in diagnosis and monitoring of cardiac conditions. However, effective and accurate classification of these signals remain as a challenge, due to noisy nature of data and increased complexity in distinguishing between various cardiac patterns. This work presents novel approach for ECG signal classification through an integrated Deep Learning (DL) framework. Initially, ECG signal pre-processing is performed where normalization is done to mitigate noise and ensure consistency across the dataset. Feature extraction is conducted using Power Spectral Density (PSD) analysis, which identifies the power distribution of signal in frequency domain. To further enhance classification performance, a feature selection process is implemented using the Monarch Butterfly Optimization (MBO) algorithm, which efficiently reduces the dimensionality of feature set via choosing the relevant and discriminative features. Selected features are subsequently fed into an Attention-infused morphological Recurrent Neural Network (AMRNN) for classification. The AM-RNN incorporates adaptable filters that adjust based on input data resulting in superior performance in classifying numerous cardiac conditions. Proposed system is implemented using Python software, and results demonstrate the superiority of the work in comparison to existing techniques.
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Rema et al. (2026) studied Cardiovascular Disease. Attention-infused morphological Recurrent Neural Network (AM-RNN) with Monarch Butterfly Optimization (MBO) vs. Conventional classifiers (e.g., CNN-LSTM, FODW-SVM, SOARO-AE) was evaluated on Classification accuracy for binary dataset. The proposed AM-RNN framework with optimized feature selection achieved a prediction accuracy of 99.10% for ECG signal classification, outperforming conventional machine learning models.
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