ECG-HO-Net: A Hybrid Deep Learning Framework for Automated Arrhythmia Detection Using Hippopotamus Optimization and Liquid Neural Networks - Begum, Md. Shamshad; Padmaja, Nimmagadda (Engineering, Technology & Applied Science Research, 2025)
With the increasing incidence of cardiovascular disorders and the limitations of existing ECG classification methods in handling signal variability and feature redundancy, there is a pressing need for a hybrid model that ensures both accuracy and adaptability in arrhythmia detection. The proposed ECG-HO-Net combines adaptive Liquid Neural Networks (LNN) and Hippopotamus Optimization (HO) for feature selection to detect arrhythmia. The process starts with preprocessing: Butterworth filtering removes noise, min-max normalization scales signals between 0 and 1, and Pan-Tompkins detects R-peaks to segment heartbeats. Feature extraction includes time-domain metrics such as Mean Heart Rate (99.82 BPM) and RMS (0.609), along with frequency-domain analysis using FFT to capture spectral components of the ECG. The model was evaluated on the MIT-BIH Arrhythmia dataset, comprising more than 109,000 annotated heartbeats of various types of arrhythmias. The novelty of ECG-HO-Net lies in its unique integration of HO for efficient feature selection and LNNs for adaptive temporal learning. Performance analysis shows that the proposed model outperformed state-of-the-art models, achieving 99.2% accuracy, 99.71% precision, 99.51% recall, 99.73% F1-score, and 99.24% specificity.