Literature DB >> 23366214

Statistical analysis of heart rate and heart rate variability monitoring through the use of smart phone cameras.

Jeffrey B Bolkhovsky1, Christopher G Scully, Ki H Chon.   

Abstract

Video recordings of finger tips made using a smartphone camera contain a pulsatile component caused by the cardiac pulse equivalent to that present in a photoplethysmographic signal. By performing peak detection on the pulsatile signal it is possible to extract a continuous heart rate signal. We performed direct comparisons between 5-lead electrocardiogram based heart rate variability measurements and those obtained from an iPhone 4s and Motorola Droid derived pulsatile signal to determine the accuracy of heart rate variability measurements obtained from the smart phones. Monitoring was performed in the supine and tilt positions for independent iPhone 4s (2 min recordings, n=9) and Droid (5 min recordings, n=13) experiments, and the following heart rate and heart rate variability parameters were estimated: heart rate, low frequency power, high frequency power, ratio of low to high frequency power, standard deviation of the RR intervals, and root mean square of successive RR-differences. Results demonstrate that accurate heart rate variability parameters can be obtained from smart phone based measurements.

Mesh:

Year:  2012        PMID: 23366214     DOI: 10.1109/EMBC.2012.6346253

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  13 in total

1.  The Accuracy of Acquiring Heart Rate Variability from Portable Devices: A Systematic Review and Meta-Analysis.

Authors:  Ward C Dobbs; Michael V Fedewa; Hayley V MacDonald; Clifton J Holmes; Zackary S Cicone; Daniel J Plews; Michael R Esco
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2.  The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data.

Authors:  Nicholas C Jacobson; Sy-Miin Chow; Michelle G Newman
Journal:  Behav Res Methods       Date:  2019-02

3.  Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments.

Authors:  Nicholas C Jacobson; Sukanya Bhattacharya
Journal:  Behav Res Ther       Date:  2021-12-11

4.  Compressive recovery of smartphone RGB spectral sensitivity functions.

Authors:  Yuhyun Ji; Yunsang Kwak; Sang Mok Park; Young L Kim
Journal:  Opt Express       Date:  2021-04-12       Impact factor: 3.894

5.  Extraction of heart rate variability from smartphone photoplethysmograms.

Authors:  Rong-Chao Peng; Xiao-Lin Zhou; Wan-Hua Lin; Yuan-Ting Zhang
Journal:  Comput Math Methods Med       Date:  2015-01-12       Impact factor: 2.238

6.  Monitoring of Heart and Breathing Rates Using Dual Cameras on a Smartphone.

Authors:  Yunyoung Nam; Youngsun Kong; Bersain Reyes; Natasa Reljin; Ki H Chon
Journal:  PLoS One       Date:  2016-03-10       Impact factor: 3.240

7.  Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones.

Authors:  Nicholas C Jacobson; Yeon Joo Chung
Journal:  Sensors (Basel)       Date:  2020-06-24       Impact factor: 3.576

Review 8.  Smartphone Sensors for Health Monitoring and Diagnosis.

Authors:  Sumit Majumder; M Jamal Deen
Journal:  Sensors (Basel)       Date:  2019-05-09       Impact factor: 3.576

Review 9.  The Current State of Mobile Phone Apps for Monitoring Heart Rate, Heart Rate Variability, and Atrial Fibrillation: Narrative Review.

Authors:  Ka Hou Christien Li; Francesca Anne White; Gary Tse; Timothy Tipoe; Tong Liu; Martin Cs Wong; Aaron Jesuthasan; Adrian Baranchuk; Bryan P Yan
Journal:  JMIR Mhealth Uhealth       Date:  2019-02-15       Impact factor: 4.773

10.  The Accuracy and Validity of iOS-Based Heart Rate Apps During Moderate to High Intensity Exercise.

Authors:  Alexa M Bouts; Lauren Brackman; Elizabeth Martin; Adam M Subasic; Edward S Potkanowicz
Journal:  Int J Exerc Sci       Date:  2018-01-02
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