A Lightweight CNN-Attention-BiLSTM Architecture for Multi-Class Arrhythmia Classification on Standard and Wearable ECGs

• URL: http://arxiv.org/abs/2511.08650v1
• Date: Thu, 13 Nov 2025 01:01:29 GMT
• Title: A Lightweight CNN-Attention-BiLSTM Architecture for Multi-Class Arrhythmia Classification on Standard and Wearable ECGs
• Authors: Vamsikrishna Thota, Hardik Prajapati, Yuvraj Joshi, Shubhangi Rathi,
• Abstract summary: We propose a lightweight deep learning model combining 1D Convolutional Neural Networks (CNN), attention mechanisms, and Bidirectional Long Short-Term Memory (BiLSTM) for classifying arrhythmias from both 12-lead and single-lead ECGs.<n>With only 0.945 million parameters, our model is well-suited for real-time deployment in wearable health monitoring systems.
• Score: 0.37331950863394864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early and accurate detection of cardiac arrhythmias is vital for timely diagnosis and intervention. We propose a lightweight deep learning model combining 1D Convolutional Neural Networks (CNN), attention mechanisms, and Bidirectional Long Short-Term Memory (BiLSTM) for classifying arrhythmias from both 12-lead and single-lead ECGs. Evaluated on the CPSC 2018 dataset, the model addresses class imbalance using a class-weighted loss and demonstrates superior accuracy and F1- scores over baseline models. With only 0.945 million parameters, our model is well-suited for real-time deployment in wearable health monitoring systems.

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