Literature DB >> 26558395

Smart ECG Monitoring Patch with Built-in R-Peak Detection for Long-Term HRV Analysis.

W K Lee1, H Yoon1, K S Park2,3.   

Abstract

Since heart rate variability (HRV) analysis is widely used to evaluate the physiological status of the human body, devices specifically designed for such applications are needed. To this end, we developed a smart electrocardiography (ECG) patch. The smart patch measures ECG using three electrodes integrated into the patch, filters the measured signals to minimize noise, performs analog-to-digital conversion, and detects R-peaks. The measured raw ECG data and the interval between the detected R-peaks can be recorded to enable long-term HRV analysis. Experiments were performed to evaluate the performance of the built-in R-wave detection, robustness of the device under motion, and applicability to the evaluation of mental stress. The R-peak detection results obtained with the device exhibited a sensitivity of 99.29%, a positive predictive value of 100.00%, and an error of 0.71%. The device also exhibited less motional noise than conventional ECG recording, being stable up to a walking speed of 5 km/h. When applied to mental stress analysis, the device evaluated the variation in HRV parameters in the same way as a normal ECG, with very little difference. This device can help users better understand their state of health and provide physicians with more reliable data for objective diagnosis.

Entities:  

Keywords:  Day-to-day activities; Heart rate variability; Peak detection algorithm; Physiological measurement; Stress assessment; Ubiquitous healthcare; Wearable sensor

Mesh:

Year:  2015        PMID: 26558395     DOI: 10.1007/s10439-015-1502-5

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  6 in total

Review 1.  A Systematic Review of Wearable Patient Monitoring Systems - Current Challenges and Opportunities for Clinical Adoption.

Authors:  Mirza Mansoor Baig; Hamid GholamHosseini; Aasia A Moqeem; Farhaan Mirza; Maria Lindén
Journal:  J Med Syst       Date:  2017-06-19       Impact factor: 4.460

Review 2.  Heart Rate Variability: An Old Metric with New Meaning in the Era of using mHealth Technologies for Health and Exercise Training Guidance. Part One: Physiology and Methods.

Authors:  Nikhil Singh; Kegan James Moneghetti; Jeffrey Wilcox Christle; David Hadley; Daniel Plews; Victor Froelicher
Journal:  Arrhythm Electrophysiol Rev       Date:  2018-08

Review 3.  A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses.

Authors:  Erik Reinertsen; Gari D Clifford
Journal:  Physiol Meas       Date:  2018-05-15       Impact factor: 2.833

Review 4.  Integrating sleep, neuroimaging, and computational approaches for precision psychiatry.

Authors:  Andrea N Goldstein-Piekarski; Bailey Holt-Gosselin; Kathleen O'Hora; Leanne M Williams
Journal:  Neuropsychopharmacology       Date:  2019-08-19       Impact factor: 7.853

5.  Predicting intradialytic hypotension using heart rate variability.

Authors:  Samel Park; Wook-Joon Kim; Nam-Jun Cho; Chi-Young Choi; Nam Hun Heo; Hyo-Wook Gil; Eun Young Lee
Journal:  Sci Rep       Date:  2019-02-22       Impact factor: 4.379

6.  Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter.

Authors:  Tam Nguyen; Xiaoli Qin; Anh Dinh; Francis Bui
Journal:  Sensors (Basel)       Date:  2019-09-16       Impact factor: 3.576

  6 in total

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