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

Hayano, Junichiro; Yuda, Emi

doi:10.1111/anec.12901

Cited by 7 publications

(5 citation statements)

References 48 publications

(100 reference statements)

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“…These features would improve not only the ease of incorporating the sensor into bedding but also portability and practicality, reduce the burden of transfers between beds and patient rooms, and facilitate lending from the clinic to patients. Given the significant variability in sleep apnea severity from night to night [18], prolonged monitoring over multiple nights in a home setting is desirable to accurately assess the disease's characteristics in real-world conditions. In such scenarios, the small and lightweight design of the device becomes crucial for convenient handling and ensuring effective maintenance of cleanliness and hygiene.…”

Section: Discussionmentioning

confidence: 99%

“…The method for measuring the heartbeat interval from ballistocardiogram is reported in Appendix. CVHR was detected by previously published algorithm [7,8,18] and Fcv and ECG-Fcv was computed as the frequency of CVHR per hour of TRT. The ECG-Fcv was used to determine whether the association between ballistocardiogram Fcv and AHI TRT is affected by the accuracy of ballistocardiogram-based heartbeat interval measurement.…”

Section: Analysis Of Fcvmentioning

confidence: 99%

“…In this study, we developed an unobtrusive method for HSAT utilizing a piezoelectric rubber sheet sensor. We designated algorithms to extract respiration and ballistocardiogram signals, allowing for the scoring of the respiratory event index (REI) [16,17], as well as measuring the frequency (Fcv) of cyclic variation of heart rate (CVHR) [7,8,18]. The performance of REI and Fcv in classifying sleep apnea severity was assessed using the apnea-hypopnea index (AHI) obtained from the simultaneous polysomnogram as a reference standard.…”

Section: Introductionmentioning

confidence: 99%

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Piezoelectric rubber sheet sensor: a promising tool for home sleep apnea testing

Hayano,

Yamamoto,

Tanaka

et al. 2024

Sleep Breath

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Purpose This study aimed to develop an unobtrusive method for home sleep apnea testing (HSAT) utilizing micromotion signals obtained by a piezoelectric rubber sheet sensor. Methods Algorithms were designated to extract respiratory and ballistocardiogram components from micromotion signals and to detect respiratory events as the characteristic separation of the fast envelope of the respiration component from the slow envelope. In 78 adults with diagnosed or suspected sleep apnea, micromotion signal was recorded with a piezoelectric rubber sheet sensor placed beneath the bedsheet during polysomnography. In a half of the subjects, the algorithms were optimized to calculate respiratory event index (REI), estimating apnea–hypopnea index (AHI). In the other half of subjects, the performance of REI in classifying sleep apnea severity was evaluated. Additionally, the predictive value of the frequency of cyclic variation in heart rate (Fcv) obtained from the ballistocardiogram was assessed. Results In the training group, the optimized REI showed a strong correlation with the AHI (r = 0.93). Using the optimal cutoff of REI ≥ 14/h, subjects with an AHI ≥ 15 were identified with 77.8% sensitivity and 90.5% specificity. When applying this REI to the test group, it correlated closely with the AHI (r = 0.92) and identified subjects with an AHI ≥ 15 with 87.5% sensitivity and 91.3% specificity. While Fcv showed a modest correlation with AHI (r = 0.46 and 0.66 in the training and test groups), it lacked independent predictive power for AHI. Conclusion The analysis of respiratory component of micromotion using piezoelectric rubber sheet sensors presents a promising approach for HSAT, providing a practical and effective means of estimating sleep apnea severity.

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Section: Discussionmentioning

confidence: 99%

Section: Analysis Of Fcvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Piezoelectric rubber sheet sensor: a promising tool for home sleep apnea testing

Hayano,

Yamamoto,

Tanaka

et al. 2024

Sleep Breath

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“…For example, episodes of sleep apnea may be accompanied by cyclic variation in heart rate; therefore, the estimation of sleep stages based on heart rate may be affected by sleep-disordered breathing. 20,21 In addition, frequent sleep stage shifts owing to respiratory events may increase the probability that GW3 incorrectly estimates sleep stages. Many participants who had difficulty maintaining sleep and had poor sleep quality were enrolled in this study, and these factors may have contributed to the erroneous estimation of GW3.…”

Section: Discussionmentioning

confidence: 99%

Validation of the Samsung Smartwatch for Sleep–Wake Determination and Sleep Stage Estimation

2023

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Objectives: Galaxy Watch 3 (GW3) is a commercially available smartwatch equipped with a sleep-tracking function capable of collecting longitudinal sleep data in a real-world environment. We aimed to investigate the validity of GW3 for estimating sleep stages compared with reference data from polysomnography (PSG).Methods: Thirty-two healthy adults (mean age 37.8, male 87.5%) were recruited to wear a GW3 concurrently with in-laboratory overnight PSG recording. Sleep parameters, including total sleep time (TST) and the duration of each sleep stage (light, deep, and rapid eye movement [REM] sleep), were calculated for both GW3 and PSG. Sleep parameters were compared using intraclass correlation coefficients (ICCs) and Bland–Altman plots. The epoch-by-epoch classification performance was evaluated to determine the sensitivity, specificity, accuracy, kappa values, and confusion matrices.Results: Bland–Altman plots showed moderate agreement between GW3 and PSG for TST (ICC=0.640), light sleep (ICC=0.518), and deep sleep (ICC=0.639), whereas REM sleep duration was not reliably estimated using the GW3. The GW3 overestimated TST by a mean of 9.5 min. The sensitivity of epoch-by-epoch sleep detection was 0.954; however, the specificity was 0.524. The sensitivity of each sleep stage estimation was 0.695 for light sleep, 0.612 for deep sleep, and 0.598 for REM sleep. The overall accuracy of GW3 in distinguishing the four-stage sleep epochs was 0.651.Conclusions: GW3 demonstrated high performance in sleep detection but moderate performance in wake determination and sleep stage estimation compared with PSG results, which were comparable to previously reported results for other consumer wearable devices.

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“…Secondly, we used CRM instead of polysomnography equipped with electroencephalography channels, which may provide a more detailed characterization of sleep patterns. A further limitation arises from the night-to-night AHI variability, which can at least in part confound the effects of sac/val (61). Moreover, although some patients can experience central apneas even at daytime, while awake and in the upright position (62,63), we chose to focus our attention only on nighttime registration, with the aim of increasing patient adherence.…”

Section: Discussionmentioning

confidence: 99%

Effects of sacubitril-valsartan on clinical, echocardiographic and polygraphic parameters in patients affected by heart failure with reduced ejection fraction and sleep apnea

Armentaro

Pelaia

Volpentesta

et al. 2022

European Heart Journal

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Background Heart failure with reduced ejection fraction (HFrEF) is a clinical condition frequently diagnosed in clinical practice. In patients affected by HFrEF, sleep apnea (SA) can be detected among the most frequent comorbidities. Sacubitril–valsartan (sac/val) association has been proven to be effective in reducing disease progression and all-cause mortality in HFrEF patients. Sac/val treatment can potentially attenuate SA development via several pathophysiologic mechanisms, including improvement of global hemodynamics, reduction of extracellular fluid overload, and decrease of sympathetic neural activity. Purpose The aim of this work is to evaluate the effects of a 6-month therapy with sac/val on hemodynamic and metabolic parameters, as well as on the occurrence of apnea/hypopnea and oxygen saturation in patients with HFrEF and SA, already treated with CPAP. Methods We recruited 132 patients affected by HFrEF and SA, already under treatment with continuous positive airway pressure (CPAP), which was discontinued 24 hours before the scheduled study timepoints. Physical examination, echocardiography, nocturnal cardio-respiratory monitoring, and laboratory tests were performed in each patient at baseline and after a 6-month treatment with sac/val. Results After 6 months, sac/val induced statistically significant changes in clinical, hemodynamic, biohumoral (NT-proBNP, serum electrolytes, creatinine and uric acid), and echocardiographic parameters. In particular, cardiac index (CI), both atrial and ventricular volumes and global longitudinal strain (GLS) improved. Moreover, polysomnography, carried out during a temporary CPAP interruption, revealed a significant reduction in global apnea-hypopnea index (AHI) value (p<0.0001), central AHI (p<0.0001), obstructive AHI (p<0.0001), oxygen desaturation index (ODI) (p<0.0001), and percentage time of saturation below 90% (TC90) (p<0.0001). The changes of CI, estimated glomerular filtration rate (eGFR), NT-proBNP and tricuspid annular plane excursion (TAPSE) contributed to 23.6%, 7.6%, 7.3% and 4.8% of AHI variability, respectively, and the whole model accounted for a 43.3% of AHI variation. Conclusions Our results suggest that treatment with sac/val is able to significantly improve the cardiorespiratory performance of patients with HFrEF and SA, integrating the positive impact of CPAP. Thus, both CPAP and sac/val therapy may synergistically contribute to lower the risks of both cardiac and pulmonary complications in HFrEF patients with SA. Funding Acknowledgement Type of funding sources: None.

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

Cited by 7 publications

References 48 publications

Piezoelectric rubber sheet sensor: a promising tool for home sleep apnea testing

Piezoelectric rubber sheet sensor: a promising tool for home sleep apnea testing

Validation of the Samsung Smartwatch for Sleep–Wake Determination and Sleep Stage Estimation

Effects of sacubitril-valsartan on clinical, echocardiographic and polygraphic parameters in patients affected by heart failure with reduced ejection fraction and sleep apnea

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