Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach

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.new approaches to make health information more accessible than before. Among these approaches, smartphone photoplethysmography (PPG) has gained prominence [21,22]; it is an optical method that uses sensors to monitor the microvascular blood volume changes in body tissues [23]. As hemoglobin absorbs more light than the surrounding tissue, an increase (systole) or decrease (diastole) in the amount of blood can be assessed by employing the differences in the intensities of the lights and the converted waveforms. Smartphone PPG detects heartbeats by recording videos of fingertips using the in-built camera [24,25].The primary motivation for using smartphone PPG, compared to other wearable devices, is that it requires minimal equipment. Given that smartphones with in-built cameras have become a part of modern life, using them to access health information is an ideal alternative when ECGs or similar medical devices are not available [26]. In addition, there have been several reported techniques for increasing the accuracy of smartphone PPG, such as point-of-interest selection [27], bandpass filtering [28], adaptive signal thresholding [29], motion detection techniques [30][31][32], interpolation techniques [33], and signal decomposition methods [34][35][36]. Bioengineering studies indicate that the average HR [37] and HRV measured using smartphone PPG are comparable with those measured using gold standard ECGs [21,28,[38][39][40].Although it is a promising solution for practical data collection and has an accuracy that has been well proved in several experiments, using smartphone PPG to measure HRV has received limited research attention in applied disciplines such as medicine or psychology [41]; a possible explanation is the lack of robustness in practical scenarios. When smartphone users measure their heartbeats outside a laboratory environment, slight hand movements or ...

High Sampling Rate Smartphone-PPG via Built-in Rolling Shutter Image Sensor

“…It is assumed that the spatially averaged intensity signal 𝑦 𝑎𝑣𝑔 (𝑡) is related to a small change in the skin color and would be proportional to the changes in the blood volume in the arteries and capillaries underneath the skin during the cardiac cycle [15,[17][18]. The main problem of this method is its [32] 128 Hz SBP and HR (multimodal PPG and ECG) capability of PPG data sampling is limited to the maximum capture rate fps of the CIS, hence the acquired Fs of 𝑦 𝑎𝑣𝑔 is typically ≤ CIS fps [19][20].…”

“…Other researchers tried to solve this problem by not focusing on the low-sampling rate problem but instead developing an algorithm that detects and separate clean PPG signal from the noisy ones [21][22][23]. For example, Tabei et al [21], proposed a novel personalized motion and noise artifact detection algorithm using neural network and signal quality index to reduce false positive alarm on atrial fibrillation (AF) detection.…”

“…It showed the proposed EMD, and Hilbert transform could reduce the interference of motion artifacts. Liu et al [23] proposed a sinusoidal function-based PPG quality index to filter out low-quality PPG data and perform HRV measurements from smartphone PPG signals. Moreover, A parabola approximation method is developed to increase the accuracy of PPG peak detection extraction from low-samplingrate PPG signal, for further reliable HRV parameter analysis [24].…”

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

“…Taking into account that the possible HR values ranges from 40 [bradycardia in young users (Kawachi et al, 1995;Longin et al, 2005;Sharma et al, 2015)] to around 220 bpm (which is the maximum HR value in young users or in newborns), a minimum sampling rate of 8 fps should be guaranteed (sampling theorem). However, considering the analysis of heart rate variability (HRV), different studies investigated the minimum sampling frequency (f s ) that should be assured (Choi and Shin, 2017;Liu et al, 2020;Béres and Hejjel, 2021). Authors in (Choi and Shin, 2017) showed that the minimum f s depends on the HRV variables which can be obtained (e.g., f s > 10 Hz for LF/HF, f s > 20 Hz for NN50 and pNN50; see Choi and Shin (2017) for further details).…”

The Vagal Autonomic Pathway of COVID-19 at the Crossroad of Alzheimer’s Disease and Aging: A Review of Knowledge

“…Since life-threatening forms of COVID-19 involve a systemic inflammatory surge similar to sepsis, future HRV assessment in a large cohort of COVID-19 and healthy subjects is worth doing. Indeed, HRV monitoring is now possible not only in intensive care units [135], but also at a populational level due to the wide use of smartphones and both iOS and Android HRV applications [136,137]. Notably, a few years ago, a study suggested that such ambulatory monitoring of heart rate in 43 individuals up to 2 years could allow early sepsis detection with 100% sensitivity [138].…”