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Keywords = cyclic variation of heart rate (CVHR)

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14 pages, 2933 KiB  
Article
Detection of Cyclic Variation in Heart Rate (CVHR) During Sleep Using a Ring-Type Silicon Sensor and Evaluation of Intra-Weekly Variability
by Emi Yuda, Hiroyuki Edamatsu, Kenji Hosomi and Junichiro Hayano
Electronics 2025, 14(3), 629; https://doi.org/10.3390/electronics14030629 - 6 Feb 2025
Viewed by 1414
Abstract
Patients with sleep apnea syndrome (SAS) have a risk of stroke that is more than three times higher than that of healthy individuals. Early detection and appropriate treatment are crucial for preventing serious complications, and detecting cyclic variation in heart rate (CVHR) plays [...] Read more.
Patients with sleep apnea syndrome (SAS) have a risk of stroke that is more than three times higher than that of healthy individuals. Early detection and appropriate treatment are crucial for preventing serious complications, and detecting cyclic variation in heart rate (CVHR) plays a key role in early diagnosis. This study investigated the feasibility of detecting CVHR during sleep using a wearable, comfortable device and evaluated the ability to assess weekly fluctuations. Heart rate, blood oxygen saturation, and bio-acceleration were measured for seven consecutive nights in eight healthy subjects (45.7 ± 10.1 years old). The CVHR values obtained using a ring-type sensor were compared to those derived from the apnea–hypopnea index (AHI) measured with a Holter ECG. The results revealed that CVHR values measured with the ring-type sensor were higher than those measured with the Holter monitor. Although correction is required, the ring-type sensor successfully detected intra-weekly fluctuations. These findings suggest that a ring-type sensor could be a practical tool for monitoring CVHR and identifying weekly trends in a comfortable, non-invasive manner. Full article
(This article belongs to the Special Issue What Is Next in Wearable Computing for Mental and Physical Health Monitoring?)
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16 pages, 2410 KiB  
Article
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
by Takuo Arikawa, Toshiaki Nakajima, Hiroko Yazawa, Hiroyuki Kaneda, Akiko Haruyama, Syotaro Obi, Hirohisa Amano, Masashi Sakuma, Shigeru Toyoda, Shichiro Abe, Takeshi Tsutsumi, Taishi Matsui, Akio Nakata, Ryo Shinozaki, Masayuki Miyamoto and Teruo Inoue
J. Clin. Med. 2020, 9(10), 3359; https://doi.org/10.3390/jcm9103359 - 20 Oct 2020
Cited by 14 | Viewed by 4893
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Sleep-Disordered Breathing in Cardiovascular Disease)
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