ECG-HO-Net: A Hybrid Deep Learning Framework for Automated Arrhythmia Detection Using Hippopotamus Optimization and Liquid Neural Networks
Received: 13 April 2025 | Revised: 1 June 2025, 1 July 2025, and 24 July 2025 | Accepted: 27 July 2025 | Online: 6 October 2025
Corresponding author: Md. Shamshad Begum
Abstract
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.
Keywords:
arrhythmia, CVD, ECG, LNN, accuracy, optimization, heartDownloads
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