Key result
AQO-CNN-LSTM improves ECG classification accuracy by ~0.15% over conventional methods.
Why the study?
Conventional ECG classification methods are limited by noise susceptibility, computational cost, and variable performance, restricting their real-time utility.
Does the AQO-CNN-LSTM model improve classification accuracy, precision, and processing time for ECG signals compared to conventional machine learning methods?
Does the AQO-CNN-LSTM model improve classification accuracy, precision, and processing time for ECG signals compared to conventional machine learning methods?
The integration of Aquila Optimization with CNN-LSTM networks improves ECG classification accuracy and processing time, offering a robust and noise-resilient solution for real-time cardiac monitoring.
The importance of the Electrocardiogram (ECG) signal is that its classification is necessary to identify abnormalities in the cardiovascular system. Conventional methods have several disadvantages, such as susceptibility to noise and computational cost, which limit real-time utility and performance. Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree (DT) perform moderately well but are noisy and require massive computational power, leading to variable performance. In response to these issues, an ideal hybrid model with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture is proposed, supplemented by Aquila Optimization (AQO). AQO-CNN-LSTM exploits the spatial feature extraction capability of CNN and the temporal learning capability of LSTM, whereas AQO fine-tunes the key parameters to make the classification more robust and efficient. The proposed AQO-CNN-LSTM model demonstrates quantifiable improvement, with a 0.15% increase in classification accuracy, a 0.20% reduction in processing time, and a 0.18% increase in classification precision compared to conventional methods. Such an improvement makes AQO-CNN-LSTM an efficient and robust solution for real-time classification of the ECG signal, making it highly suitable for cardiac monitoring and diagnosis.
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Anu et al. (2025) studied Cardiovascular abnormalities (Arrhythmia). AQO-CNN-LSTM model vs. Conventional methods (SVM, KNN, DT) was evaluated on Classification accuracy (0.15% increase). The AQO-CNN-LSTM model improved ECG classification accuracy by 0.15%, reduced processing time by 0.20%, and increased precision by 0.18% compared to conventional methods.
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