Literature DB >> 32305891

Wearable Armband Device for Daily Life Electrocardiogram Monitoring.

Jesus Lazaro, Natasa Reljin, Md-Billal Hossain, Yeonsik Noh, Pablo Laguna, Ki H Chon.   

Abstract

A wearable armband electrocardiogram (ECG) monitor has been used for daily life monitoring. The armband records three ECG channels, one electromyogram (EMG) channel, and tri-axial accelerometer signals. Contrary to conventional Holter monitors, the armband-based ECG device is convenient for long-term daily life monitoring because it uses no obstructive leads and has dry electrodes (no hydrogels), which do not cause skin irritation even after a few days. Principal component analysis (PCA) and normalized least mean squares (NLMS) adaptive filtering were used to reduce the EMG noise from the ECG channels. An artifact detector and an optimal channel selector were developed based on a support vector machine (SVM) classifier with a radial basis function (RBF) kernel using features that are related to the ECG signal quality. Mean HR was estimated from the 24-hour armband recordings from 16 volunteers in segments of 10 seconds each. In addition, four classical HR variability (HRV) parameters (SDNN, RMSSD, and powers at low and high frequency bands) were computed. For comparison purposes, the same parameters were estimated also for data from a commercial Holter monitor. The armband provided usable data (difference less than 10% from Holter-estimated mean HR) during 75.25%/11.02% (inter-subject median/interquartile range) of segments when the user was not in bed, and during 98.49%/0.79% of the bed segments. The automatic artifact detector found 53.85%/17.09% of the data to be usable during the non-bed time, and 95.00%/2.35% to be usable during the time in bed. The HRV analysis obtained a relative error with respect to the Holter data not higher than 1.37% (inter-subject median/interquartile range). Although further studies have to be conducted for specific applications, results suggest that the armband device has a good potential for daily life HR monitoring, especially for applications such as arrhythmia or seizure detection, stress assessment, or sleep studies.

Entities:  

Mesh:

Year:  2020        PMID: 32305891     DOI: 10.1109/TBME.2020.2987759

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  9 in total

Review 1.  Wearables in Cardiovascular Disease.

Authors:  Sanchit Kumar; Angela M Victoria-Castro; Hannah Melchinger; Kyle D O'Connor; Mitchell Psotka; Nihar R Desai; Tariq Ahmad; F Perry Wilson
Journal:  J Cardiovasc Transl Res       Date:  2022-09-09       Impact factor: 3.216

Review 2.  Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data.

Authors:  Craig J Goergen; MacKenzie J Tweardy; Steven R Steinhubl; Stephan W Wegerich; Karnika Singh; Rebecca J Mieloszyk; Jessilyn Dunn
Journal:  Annu Rev Biomed Eng       Date:  2021-12-21       Impact factor: 11.324

Review 3.  An Overview of the Sensors for Heart Rate Monitoring Used in Extramural Applications.

Authors:  Alessandra Galli; Roel J H Montree; Shuhao Que; Elisabetta Peri; Rik Vullings
Journal:  Sensors (Basel)       Date:  2022-05-26       Impact factor: 3.847

4.  A robust ECG denoising technique using variable frequency complex demodulation.

Authors:  Md-Billal Hossain; Syed Khairul Bashar; Jesus Lazaro; Natasa Reljin; Yeonsik Noh; Ki H Chon
Journal:  Comput Methods Programs Biomed       Date:  2020-11-21       Impact factor: 5.428

5.  Using the Redundant Convolutional Encoder-Decoder to Denoise QRS Complexes in ECG Signals Recorded with an Armband Wearable Device.

Authors:  Natasa Reljin; Jesus Lazaro; Md Billal Hossain; Yeon Sik Noh; Chae Ho Cho; Ki H Chon
Journal:  Sensors (Basel)       Date:  2020-08-17       Impact factor: 3.576

Review 6.  Smart Wearables for Cardiac Monitoring-Real-World Use beyond Atrial Fibrillation.

Authors:  David Duncker; Wern Yew Ding; Susan Etheridge; Peter A Noseworthy; Christian Veltmann; Xiaoxi Yao; T Jared Bunch; Dhiraj Gupta
Journal:  Sensors (Basel)       Date:  2021-04-05       Impact factor: 3.576

7.  Feasibility of atrial fibrillation detection from a novel wearable armband device.

Authors:  Syed Khairul Bashar; Md-Billal Hossain; Jesús Lázaro; Eric Y Ding; Yeonsik Noh; Chae Ho Cho; David D McManus; Timothy P Fitzgibbons; Ki H Chon
Journal:  Cardiovasc Digit Health J       Date:  2021-05-21

8.  Armband Sensors Location Assessment for Left Arm-ECG Bipolar Leads Waveform Components Discovery Tendencies around the MUAC Line.

Authors:  Omar Escalona; Sephorah Mukhtar; David McEneaney; Dewar Finlay
Journal:  Sensors (Basel)       Date:  2022-09-24       Impact factor: 3.847

9.  Novel Density Poincaré Plot Based Machine Learning Method to Detect Atrial Fibrillation From Premature Atrial/Ventricular Contractions.

Authors:  Syed Khairul Bashar; Dong Han; Fearass Zieneddin; Eric Ding; Timothy P Fitzgibbons; Allan J Walkey; David D McManus; Bahram Javidi; Ki H Chon
Journal:  IEEE Trans Biomed Eng       Date:  2021-01-20       Impact factor: 4.538

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.