A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP wa...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-01, Vol.20 (3), p.606
Main Authors: Shao, Minggang, Zhou, Zhuhuang, Bin, Guangyu, Bai, Yanping, Wu, Shuicai
Format: Article
Language:English
Subjects:
Algorithms
android smartphone
Atrial Fibrillation - diagnosis
atrial fibrillation detection
Bluetooth
Cardiac arrhythmia
Classification
Cloud Computing
Design
electrocardiogram (ecg) monitoring
Electrocardiography
Electrocardiography - instrumentation
Electrocardiography - methods
Electrocardiography, Ambulatory - instrumentation
Electrocardiography, Ambulatory - methods
Feature selection
Fibrillation
Humans
Machine Learning
Power supply
Real time
Remote monitoring
Signal Processing, Computer-Assisted - instrumentation
Smartphone
Smartphones
Software
Waveforms
wearable ecg patch
Wearable Electronic Devices
Wearable technology
Web browsers
Wireless Technology - instrumentation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor's diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F score of 0.92 on the test set (n = 7,270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20030606