Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 67, Issue 12, Pages 3464-3473Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2020.2987759
Keywords
Electrocardiography; Monitoring; Electromyography; Biomedical monitoring; Electrodes; Principal component analysis; Channel estimation; Wearable devices; electrocardiogram (ECG); ECG denoising; electromyogram (EMG); artifact detection
Categories
Funding
- European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under Marie Sklodowska-Curie Grant [745755]
- Government of Aragon
- European Social Fund (EU) through BSICoS group [T39_20R]
- CIBER in Bioengineering, Biomaterials & Nanomedicine (CIBER-BBN) through Instituto de Salud Carlos III
- NSF SBIR Phase I [1746589, R43 HL135961]
- Div Of Industrial Innovation & Partnersh
- Directorate For Engineering [1746589] Funding Source: National Science Foundation
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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.
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