# A Wearable ECG Monitor for Deep Learning Based Real-Time Cardiovascular Disease Detection

arXiv: https://arxiv.org/abs/2201.10083 · HTML: https://mlsanity.com/paper/2201.10083/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2022.

## Built on by (most cited first, 3 of 3)

- [CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models](https://mlsanity.com/paper/2512.02180/) (2025)
- [MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense](https://mlsanity.com/paper/2410.05159/) (2024)
- [AI Based Digital Twin Model for Cattle Caring](https://mlsanity.com/paper/2205.04034/) (2022)

## Abstract (arXiv)

Cardiovascular disease has become one of the most significant threats endangering human life and health. Recently, Electrocardiogram (ECG) monitoring has been transformed into remote cardiac monitoring by Holter surveillance. However, the widely used Holter can bring a great deal of discomfort and inconvenience to the individuals who carry them. We developed a new wireless ECG patch in this work and applied a deep learning framework based on the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) models. However, we find that the models using the existing techniques are not able to differentiate two main heartbeat types (Supraventricular premature beat and Atrial fibrillation) in our newly obtained dataset, resulting in low accuracy of 58.0 %. We proposed a semi-supervised method to process the badly labelled data samples with using the confidence-level-based training. The experiment results conclude that the proposed method can approach an average accuracy of 90.2 %, i.e., 5.4 % higher than the accuracy of conventional ECG classification methods.