What does this research mean for the field?

A hybrid CNN-BiLSTM framework optimized with Genetic Algorithms and wavelet transformers achieves high accuracy (96.3% for EEG, 95.8% for ECG) and reduces inference latency by 38%, outperforming standard baselines for real-time biomedical signal classification. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

October 22, 2025Journal of Machine and Computing1 citationsOpen Access

Hybrid Neural Network Genetic Algorithm Framework for EEG and ECG Signal Classification

HMHussain Ali MutarUniversity of Wasit SNShaima Miqdad Mohamed NajeebUniversity of Wasit MAMohand Lokman Ahmad Al-DabagNorthern Technical University

Structured PICO

Does a hybrid CNN-BiLSTM framework optimized with genetic algorithms improve classification accuracy of EEG seizures and ECG arrhythmias compared to standard baselines?

Population

23 subjects from the CHB-MIT EEG dataset and 47 subjects from the MIT-BIH ECG dataset

Intervention

Hybrid Neural Network-Genetic Algorithm (NN-GA) framework utilizing a CNN-BiLSTM architecture with wavelet-based preprocessing and automated hyperparameter optimization

Comparator

ResNet-18 and SVM baselines

Outcome

Classification accuracy of EEG seizures and ECG arrhythmiassurrogate

A hybrid CNN-BiLSTM framework optimized with genetic algorithms significantly improves the classification accuracy and inference latency for detecting ECG arrhythmias and EEG seizures.

Abstract

Clinically relevant information is extracted from biomedical signals such as electroencephalogram (EEG) and electrocardiogram (ECG) by digital signal processing (DSP) techniques, which are pivotal. A hybrid framework that synergizes Convolutional Neural Networks (CNNs) with Genetic Algorithms (GAs) is introduced in this paper to enhance the classification accuracy of EEG seizures and ECG arrhythmias. Wavelet transformers are employed for noise-robust feature extraction, and a GA is utilized for automated hyperparameter optimization of a CNN-BiLSTM architecture. The proposed model is evaluated on the CHB-MIT EEG dataset (23 subjects) and the MIT-BIH ECG dataset (47 subjects), achieving 96.3% and 95.8% accuracy, respectively, and outperforming ResNet-18 and SVM baselines. The significance of improvements is confirmed by statistical tests (Wilcoxon signed-rank, p<0.01). Inference latency is reduced by 38% by the optimized model, making it suitable for edge deployment in real-time diagnostic systems.

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Cite This Study

Mutar et al. (Wed,) studied this question.

synapsesocial.com/papers/6a1d18c31e7099f69104dd47 https://doi.org/https://doi.org/10.53759/7669/jmc202606021

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