# rECGnition_v1.0: Arrhythmia detection using cardiologist-inspired multi-modal architecture incorporating demographic attributes in ECG

arXiv: https://arxiv.org/abs/2410.18985 · HTML: https://mlsanity.com/paper/2410.18985/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 72 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS](https://mlsanity.com/paper/1912.06059/) (2019)
- [Feature Importance Ranking for Deep Learning](https://mlsanity.com/paper/2010.08973/) (2020)
- [The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models](https://mlsanity.com/paper/1801.10269/) (2018)
- [ECG arrhythmia classification using a 2-D convolutional neural network](https://mlsanity.com/paper/1804.06812/) (2018)
- [Bootstrap Generalization Ability from Loss Landscape Perspective](https://mlsanity.com/paper/2209.08473/) (2022)
- [Selecting the suitable resampling strategy for imbalanced data classification regarding dataset properties](https://mlsanity.com/paper/2201.07932/) (2021)

## Abstract (arXiv)

A substantial amount of variability in ECG manifested due to patient characteristics hinders the adoption of automated analysis algorithms in clinical practice. None of the ECG annotators developed till date consider the characteristics of the patients in a multi-modal architecture. We employed the XGBoost model to analyze the UCI Arrhythmia dataset, linking patient characteristics to ECG morphological changes. The model accurately classified patient gender using discriminative ECG features with 87.75% confidence. We propose a novel multi-modal methodology for ECG analysis and arrhythmia classification that can help defy the variability in ECG related to patient-specific conditions. This deep learning algorithm, named rECGnition_v1.0 (robust ECG abnormality detection Version 1), fuses Beat Morphology with Patient Characteristics to create a discriminative feature map that understands the internal correlation between both modalities. A Squeeze and Excitation based Patient characteristic Encoding Network (SEPcEnet) has been introduced, considering the patient's demographics. The trained model outperformed the various existing algorithms by achieving the overall F1-score of 0.986 for the ten arrhythmia class classification in the MITDB and achieved near perfect prediction scores of ~0.99 for LBBB, RBBB, Premature ventricular contraction beat, Atrial premature beat and Paced beat. Subsequently, the methodology was validated across INCARTDB, EDB and different class groups of MITDB using transfer learning. The generalizability test provided F1-scores of 0.980, 0.946, 0.977, and 0.980 for INCARTDB, EDB, MITDB AAMI, and MITDB Normal vs. Abnormal Classification, respectively. Therefore, with a more enhanced and comprehensive understanding of the patient being examined and their ECG for diverse CVD manifestations, the proposed rECGnition_v1.0 algorithm paves the way for its deployment in clinics.