Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms

Bae, Sean; Borac, Silviu; Emre, Yunus; Wang, Jonathan; Wu, Jiang; Kashyap, Mehr; Kang, Si Hyuck; Chen, Liwen; Moran, Melissa; Cannon, Julie; Teasley, Eric S.; Chai, Allen; Liu, Yun; Wadhwa, Neal; Krainin, Michael; Rubinstein, Michael; Maciel, Alejandra; McConnell, Michael V.; Patel, Shwetak; Corrado, Greg S.; Taylor, James A.; Zhan, Jiening; Po, Ming Jack

doi:10.1038/s43856-022-00102-x

Cited by 21 publications

(9 citation statements)

References 51 publications

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“…Mean absolute error (MAE) was used to assess HR and RR performance for multiwavelength PPG and BioZ relative to SS-ECG (ground truth). Following prior work [7], HR was considered to be in agreement with ground truth if within 5 beats/minute (MAE), and RR if within 1 breath/min (MAE).…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Physiological sensing on the upper arm with a wireless multi-modal wearable

Branan,

McMurray,

Jennings

et al. 2024

Optical Diagnostics and Sensing XXIV: Toward Point-of-Care Diagnostics

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In this research, we examine the potential of measuring physiological variables, including heart rate (HR) and respiration rate (RR) on the upper arm using a wireless multimodal sensing system consisting of an accelerometer, a gyroscope, a three-wavelength photoplethysmography (PPG), single-sided electrocardiography (SS-ECG), and bioimpedance (BioZ). The study included collecting HR data when the subject was at rest and typing, and RR data when the subject was at rest. The data from three wavelengths of PPG and BioZ were collected and compared to the SS-ECG as the standard. The accelerometer and gyro signals were used to exclude data with excessive noise due to motion. The results showed that when the subject remained sedentary, the mean absolute error (MAE) for the HR calculation for all three wavelengths of the PPG modality was less than two bpm, while the BioZ was 3.5 bpm compared with SS-ECG HR. The MAE for typing increased for both modalities and was less than three bpm for all three wavelengths of the PPG but increased to 7.5 bpm for the BioZ. Regarding RR, both modalities resulted in RR within one breath per minute of the SS-ECG modality for the one breathing rate. Overall, all modalities on this upper arm wearable worked well when the subject was sedentary. Still, the SS-ECG and PPG showed less variability for the HR signal in the presence of motion during micro-motions such as typing.

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Section: Performance Evaluation Metricsmentioning

confidence: 99%

Physiological sensing on the upper arm with a wireless multi-modal wearable

Branan,

McMurray,

Jennings

et al. 2024

Optical Diagnostics and Sensing XXIV: Toward Point-of-Care Diagnostics

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“…Different sources of light can be used according to the purpose. For example, even smartphones flash as a light source and the camera as a detector has shown to reliably measure heart rate [ 90 ]. Pulse oximeters usually use two light-emitting diodes that emit light at the 660 nm (red) and the 940 nm (near-infrared) wavelengths as a light source [ 91 ].…”

Section: Emerging Technologiesmentioning

confidence: 99%

History and evolution of blood pressure measurement

Noh,

Kim,

Kang

et al. 2024

Clin Hypertens

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Hypertension is the leading cause of morbidity and mortality worldwide. Hypertension mostly accompanies no symptoms, and therefore blood pressure (BP) measurement is the only way for early recognition and timely treatment. Methods for BP measurement have a long history of development and improvement. Invasive method via arterial cannulation was first proven possible in the 1800’s. Subsequent scientific progress led to the development of the auscultatory method, also known as Korotkoff’ sound, and the oscillometric method, which enabled clinically available BP measurement. However, hypertension management status is still poor. Globally, less than half of adults are aware of their hypertension diagnosis, and only one-third of them being treated are under control. Novel methods are actively investigated thanks to technological advances such as sensors and machine learning in addition to the clinical needs for easier and more convenient BP measurement. Each method adopts different technologies with its own specific advantages and disadvantages. Promises of novel methods include comprehensive information on out-of-office BP capturing dynamic short-term and long-term fluctuations. However, there are still pitfalls such as the need for regular calibration since most novel methods capture relative BP changes rather than an absolute value. In addition, there is growing concern on their accuracy and precision as conventional validation protocols are inappropriate for cuffless continuous methods. In this article, we provide a comprehensive overview of the past and present of BP measurement methods. Novel and emerging technologies are also introduced with respect to their potential applications and limitations. Graphical Abstract

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“…Further, respiratory system malfunction can produce physical breathing disorders including pulmonary diseases and sleep apnea. RR may also be an ideal biomarker due to its measurability using mobile devices such as wearables 12 , 13 , smart phones 14 , wrist-wearables 15 , chest bands 16 , and wireless home devices 17 .…”

Section: Introductionmentioning

confidence: 99%

Slower respiration rate is associated with higher self-reported well-being after wellness training

Kral,

Weng,

Mitra

et al. 2023

Sci Rep

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Mind–body interventions such as mindfulness-based stress reduction (MBSR) may improve well-being by increasing awareness and regulation of physiological and cognitive states. However, it is unclear how practice may alter long-term, baseline physiological processes, and whether these changes reflect improved well-being. Using respiration rate (RR), which can be sensitive to effects of meditation, and 3 aspects of self-reported well-being (psychological well-being [PWB], distress, and medical symptoms), we tested pre-registered hypotheses that: (1) Lower baseline RR (in a resting, non-meditative state) would be a physiological marker associated with well-being, (2) MBSR would decrease RR, and (3) Training-related decreases in RR would be associated with improved well-being. We recruited 245 adults (age range = 18–65, M = 42.4): experienced meditators (n = 42), and meditation-naïve participants randomized to MBSR (n = 72), active control (n = 41), or waitlist control (n = 66). Data were collected at pre-randomization, post-intervention (or waiting), and long-term follow-up. Lower baseline RR was associated with lower psychological distress among long-term meditators (p* = 0.03, b = 0.02, 95% CI [0.01, 0.03]), though not in non-meditators prior to training. MBSR decreased RR compared to waitlist (p = 0.02, Cohen’s d = − 0.41, 95% CI [− 0.78, − 0.06]), but not the active control. Decreased RR related to decreased medical symptoms, across all participants (p* = 0.02, b = 0.57, 95% CI [0.15, 0.98]). Post-training, lower RR was associated with higher PWB across training groups compared to waitlist (p* = 0.01, b = 0.06, 95% CI [0.02, 0.10]), though there were no significant differences in change in PWB between groups. This physiological marker may indicate higher physical and/or psychological well-being in those who engage in wellness practices.

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Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms

Cited by 21 publications

References 51 publications

Physiological sensing on the upper arm with a wireless multi-modal wearable

Physiological sensing on the upper arm with a wireless multi-modal wearable

History and evolution of blood pressure measurement

Slower respiration rate is associated with higher self-reported well-being after wellness training

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