Clinical Usefulness of New R-R Interval Analysis Using the Wearable Heart Rate Sensor WHS-1 to Identify Obstructive Sleep Apnea: OSA and RRI Analysis Using a Wearable Heartbeat Sensor

Abstract

Obstructive sleep apnea (OSA) is highly associated with cardiovascular diseases, but most patients remain undiagnosed. Cyclic variation of heart rate (CVHR) occurs during the night, and R-R interval (RRI) analysis using a Holter electrocardiogram has been reported to be useful in screening for OSA. We investigated the usefulness of RRI analysis to identify OSA using the wearable heart rate sensor WHS-1 and newly developed algorithm. WHS-1 and polysomnography simultaneously applied to 30 cases of OSA. By using the RRI averages calculated for each time series, tachycardia with CVHR was identified. The ratio of integrated RRIs determined by integrated RRIs during CVHR and over all sleep time were calculated by our newly developed method. The patient was diagnosed as OSA according to the predetermined criteria. It correlated with the apnea hypopnea index and 3% oxygen desaturation index. In the multivariate analysis, it was extracted as a factor defining the apnea hypopnea index (r = 0.663, p = 0.003) and 3% oxygen saturation index (r = 0.637, p = 0.008). Twenty-five patients could be identified as OSA. We developed the RRI analysis using the wearable heart rate sensor WHS-1 and a new algorithm, which may become an expeditious and cost-effective screening tool for identifying OSA.

Night‐to‐night variability of sleep apnea detected by cyclic variation of heart rate during long‐term continuous ECG monitoring

“…In this study, an algorithm of the auto‐correlated wave detection with adaptive threshold was used to detect CVHR and Fcv was used to estimate AHI. The usefulness of the CVHR as a maker of sleep apnea has been reported by many researchers (Arikawa et al, 2020; Guilleminault et al, 1984; Hayano et al, 2011; Hsu et al, 2020; Magnusdottir & Hilmisson, 2018; Shimizu et al, 2015; Yatsu et al, 2021), and the accuracy of AHI estimation by Fcv has been confirmed by several earlier studies (Hayano et al, 2011, 2013, 2020, 2021). In a study of 864 polysomnographic subjects with suspected sleep apnea, patients with AHI > 15 were detected with a sensitivity of 83% and specificity of 88% by using Fcv > 15 cph as a criterion (Hayano et al, 2011).…”

“…In this study, we applied an ECG-based screening method of sleep apnea on seven-day continuous ECG monitoring, which has recently become available in clinical practice. Studies analyzing R-R interval variability of ECG during polysomnography have reported that the frequency of cyclic variation of heart rate (CVHR) reflects the AHI (Arikawa et al, 2020;Hayano et al, 2011Hayano et al, , 2013Hsu et al, 2020;Magnusdottir & Hilmisson, 2018;Yatsu et al, 2021). CVHR is a characteristic heart rate variability that appears with the episodes of sleep apnea and consists of bradycardia during apnea and transient tachycardia at the cessation of apnea (Guilleminault et al, 1984).…”

Effects of different intermittent pneumatic compression stimuli on ankle dorsiflexion range of motion

“…Heart rate variability (HRV) as an index of parasympathetic activation was measured using a wearable heart rate sensor (WHS-1, Union Tool, Japan) for 2 min at Pre, Post-0, and Post-20 in the supine position ( Arikawa et al, 2020 ). It is generally recommended for HRV that analysis be performed on a recording at least 2 min ( Smith et al, 2013 ).…”

Prospective RFID Sensors for the IoT Healthcare System

“…However, in practical applications, these can also be affected by certain environmental factors. In the figure, different sensors can be deployed at different body locations to monitor physical conditions by applying sensors in wearable and implantable devices as carriers, which can be attached to the subject's body to gauge parameters such as EEG [13], glucose [14][15][16][17][18][19][20], heartbeat [21], exercise [22,23], pH [24][25][26], pressure [27][28][29][30], skin perception [31], breathing [32], and epilepsy [33]. These data can then be transmitted via the device (RFID or WiFi) to the IoT cloud infrastructure and made available to the target user.…”