Abstract
Heart rate variability (HRV) provides essential health information such as the risks of heart attacks and mental disorders. However, inconvenience related to the accurate detection of HRV limits its potential applications. The ubiquitous use of smartphones makes them an excellent choice for regular and portable health monitoring. Following this trend, smartphone photoplethysmography (PPG) has recently garnered prominence; however, the lack of robustness has prevented both researchers and practitioners from embracing this technology. This study aimed to bridge the gap in the literature by developing a novel smartphone PPG quality index (SPQI) that can filter corrupted data. A total of 226 participants joined the study, and results from 1343 samples were used to validate the proposed sinusoidal function-based model. In both the correlation coefficient and Bland–Altman analyses, the agreement between HRV measurements generated by both the smartphone PPG and the reference electrocardiogram improved when data were filtered through the SPQI. Our results support not only the proposed approach but also the general value of using smartphone PPG in HRV analysis.
Highlights
Heart rate (HR) is an indicator of the balance of multiple physiological systems such as the cerebral cortex, autonomic nervous system, endocrine system, and baroreflex [1,2]
HR continuously adapts to physiological adjustments such as changes in arterial pressure caused by breathing [3]
Given that mental states influence the activation of the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS) [14,15], and that HR is modulated by the SNS
Summary
Heart rate (HR) is an indicator of the balance of multiple physiological systems such as the cerebral cortex, autonomic nervous system, endocrine system, and baroreflex [1,2]. By observing HR variability (HRV), researchers can assess our physical capability to adapt to internal physiological requests or changes in our surroundings. Studies have linked HRV to several health-related variables such as gender [4], body mass index [5], exercise habits [6], quality of sleep [7,8], insulin resistance [9], and inflammation [10]. A low HRV has been used to predict several health problems including heart attacks [11], headaches [12], and renal impairment [13]. Given that mental states influence the activation of the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS) [14,15], and that HR is modulated by the SNS and PNS, researchers have associated HRV with mental characteristics such as attention span [14,16], decision making [17], social behavior [18,19], and emotional modulation [20]
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