Key result
Hybrid LSTM-physiological ECG system outperforms standalone models with ~75% peak F1-score for arrhythmia detection.
Why the study?
Standalone deep learning and physiological models have limitations for ECG-based arrhythmia detection that a hybrid approach combining an LSTM-based beat classifier with rule-based physiological scoring aims to address.
Does a hybrid ECG-based arrhythmia detection system improve detection performance compared to standalone models?
Does a hybrid ECG-based arrhythmia detection system improve detection performance compared to standalone models?
A hybrid AI and physiological modeling approach for ECG arrhythmia detection shows promising performance but highlights challenges in generalization across different clinical datasets.
Should not yet change clinical arrhythmia detection; leaves open generalization of hybrid models across datasets.
This repository contains the framework and evaluation data for a hybrid ECG-based arrhythmia detection system developed at RV College of Engineering. The proposed approach addresses the limitations of standalone deep learning and physiological models by combining an LSTM-based beat classifier with a rule-based physiological scoring system. By extracting temporal morphology features alongside heart rate variability (HRV) and rhythm stability metrics , the framework provides an interpretable and robust pipeline for cardiac anomaly detection. Evaluated on the MIT-BIH Arrhythmia Database using strict patient-wise validation, the hybrid system's weighted adaptive fusion strategy achieved a peak F1-score of 74.5%, outperforming standalone baselines. Cross-database validation on the PTB (87.1% F1) and European ST-T (36.9% F1) datasets highlights the system's generalization capabilities and the ongoing challenges of clinical domain shifts. A Streamlit-based dashboard is included to support real-time clinical decision visualization.
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R.V. College of Engineering (2026) studied Arrhythmia. Hybrid ECG-based arrhythmia detection system vs. Standalone baselines was evaluated on Peak F1-score on MIT-BIH Arrhythmia Database. A hybrid ECG-based arrhythmia detection system combining LSTM and physiological scoring achieved a peak F1-score of 74.5% on the MIT-BIH database, outperforming standalone baselines.
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