# LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices

arXiv: https://arxiv.org/abs/1812.04818 · HTML: https://mlsanity.com/paper/1812.04818/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2018.

## Built on by (most cited first, 13 of 13)

- [Synthetic ECG Signal Generation using Probabilistic Diffusion Models](https://mlsanity.com/paper/2303.02475/) (2023)
- [Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks](https://mlsanity.com/paper/2110.02215/) (2021)
- [Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks](https://mlsanity.com/paper/2204.00144/) (2022)
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- [LLM-ABBA: Understanding time series via symbolic approximation](https://mlsanity.com/paper/2411.18506/) (2024)
- [Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network](https://mlsanity.com/paper/2206.10592/) (2022)
- [ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning](https://mlsanity.com/paper/2304.04646/) (2023)
- [Transfer Learning in ECG Diagnosis: Is It Effective?](https://mlsanity.com/paper/2402.02021/) (2024)
- [A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility](https://mlsanity.com/paper/2503.07276/) (2025)
- [Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset](https://mlsanity.com/paper/2209.00989/) (2022)
- [IR-UWB Radar-Based Contactless Silent Speech Recognition of Vowels, Consonants, Words, and Phrases](https://mlsanity.com/paper/2312.09572/) (2023)
- [AICRN: Attention-Integrated Convolutional Residual Network for Interpretable Electrocardiogram Analysis](https://mlsanity.com/paper/2508.12162/) (2025)
- [ECG-NAT: A Self-supervised Neighborhood Attention Transformer for Multi-lead Electrocardiogram Classification](https://mlsanity.com/paper/2605.13194/) (2026)

## Abstract (arXiv)

Objective: A novel ECG classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. Methods: The proposed solution employs a novel architecture consisting of wavelet transform and multiple LSTM recurrent neural networks. Results: Experimental evaluations show superior ECG classification performance compared to previous works. Measurements on different hardware platforms show the proposed algorithm meets timing requirements for continuous and real-time execution on wearable devices. Conclusion: In contrast to many compute-intensive deep-learning based approaches, the proposed algorithm is lightweight, and therefore, brings continuous monitoring with accurate LSTM-based ECG classification to wearable devices. Significance: The proposed algorithm is both accurate and lightweight. The source code is available online [1].