Automated Sleep Apnea Quantification Based on Respiratory Movement

Bianchi, Matt T.; Lipoma, Thomas; Darling, C.; Alameddine, Yvonne; Westover, M. Brandon

doi:10.7150/ijms.9303

Cited by 19 publications

(13 citation statements)

References 31 publications

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“…Moreover, if the cutoff value was set at 5/h for both REI_eTST and REI_TIB, the specificities and kappa coefficients for predicting AHI ≥ 5 were clearly low with the 2 REIs. These results suggest that screening of AHI ≥ 5 with the SBV may be difficult, a problem that has been noted with wearable PMs (12,27,28). In contrast, when the REI value was set to 17.0 for REI_eTST or 15.9 for REI_TIB, the sensitivities and specificities for AHI ≥ 15 were good.…”

Section: Discussionmentioning

confidence: 81%

Validation of a sheet-shaped body vibrometer for screening of obstructive sleep apnea

Kogure

Kobayashi²,

Okawa

et al. 2017

DD&T

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Section: Discussionmentioning

confidence: 81%

Validation of a sheet-shaped body vibrometer for screening of obstructive sleep apnea

Kogure

Kobayashi²,

Okawa

et al. 2017

DD&T

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“…A recent study using tracheal sounds to identify apneas reported 92.8% sensitivity and 99.7% specificity [12]. Using only the thoracic respiratory effort, a comparison based on sleep and wake periods between estimated AHI and scored AHI resulted in a correlation coefficient of r 2 = 0.73 for training and r 2 = 0.55 for validation set [13]. A study estimating AHI using only SpO 2 reported a Cohen's kappa of 0.71 and an accuracy of 91% [4].…”

Section: Discussionmentioning

confidence: 99%

Detection of Respiratory Events by Respiratory Effort and Oxygen Desaturation

et al. 2020

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Purpose Sleep respiratory events are scored based on the reduction of airflow measured by a thermistor or nasal pressure cannula, together with oxygen desaturation and arousal criteria for hypopneas. We investigated whether automatic scoring can be performed without using the uncomfortable oronasal sensors and developed an automatic scoring system that is compatible with level III home sleep apnea testing devices. Methods We developed a respiratory event detection algorithm, based on SpO 2 and respiratory effort signal measured from respiratory inductance plethysmograph (uncalibrated RIPsum), that outputs the time and duration of detected events and calculates an apnea-hypopnea-index (AHI) based on total recording time. The algorithm was tested on 98 polysomnography (PSG) recordings of patients, 77 with suspected sleep apnea and 21 without. The results were compared to annotations provided by the PSG systems where PSG AHI was computed using the total sleep time. The predicted AHI was evaluated for correlation and agreement with the PSG AHI using the intra-class correlation coefficient (ICC). Severity classification was performed and evaluated using the following categories: normal (< 5), mild (5-15), moderate (15-30), and severe ( ≥ 30). Results The ICC between predicted AHI and PSG AHI scored r = 0.96 (0.95-0.97, p < 0.001). The algorithm correctly predicted the severity for 74 recordings, overestimated 16, and underestimated 8. There was no misclassification by more than one severity level. Conclusion Using respiratory effort and SpO 2 , our algorithm was able to detect respiratory events with high correlation and agreement compared to full PSG-based detection.

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“…Because the level-IV system includes single-or dual-bio-sensor recordings, algorithms use either thoracic, abdominal, or both belt signals (RIPsum). For example, one study developed an OSA detection algorithm that adopts an adaptive envelope-tracking function following the amplitude excursion of a thoracic signal [16]. The tracking function is adaptive in that for each breath, a new threshold is defined based on the height of the previous peak.…”

Section: Related Workmentioning

confidence: 99%

Three-Stage Breathing Effort Quantification for Obstructive Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals

Adha

Igasaki

2021

IEEE Access

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A level-IV home-based sleep apnea monitoring system that utilizes alternative sensors, such as dual respiratory inductance plethysmography (RIP) belts, is proposed to promote routine apnea monitoring. Notably, continued excursion may occur in RIP belt signals, owing to the imperfect relationship between thoracic and abdominal movements during obstructive events. Therefore, we propose a novel algorithm to detect obstructive apnea based on an obstructive reciprocal divergence (ORD) continued excursion model and to explore the possibility of a multistage breathing-effort evaluation model using only RIP signals. Using the developed approach, we detected obstructive sleep apnea with a high accuracy of 99.83 ± 0.71% and a slight reduction of 73.34 ± 28.35% in hypopnea performance with overall combined objective metrics of 89.38 ± 10.53%. We found that introducing many stages improves specificity (p < 0.001). Furthermore, apart from apneic detection, we detected subtle changes in RIP signals qualitatively, which can help represent the inspiratory flow limitation (IFL) of the RIP. This study was validated by predicting an apnea hypopnea index (AHI) based on paradoxical breathing during sleep. A strong exponential relationship was observed between the proposed parameter based on the number of transitions with AHI (R 2 = 0.98; p < 0.001). The proposed approach can assist sleep technologists in characterizing obstructive and nonobstructive apneic events. Moreover, ORD is competent for further quantitative and qualitative IFL analysis and will significantly benefit the automated IFL detection system studies.

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Automated Sleep Apnea Quantification Based on Respiratory Movement

Cited by 19 publications

References 31 publications

Validation of a sheet-shaped body vibrometer for screening of obstructive sleep apnea

Validation of a sheet-shaped body vibrometer for screening of obstructive sleep apnea

Detection of Respiratory Events by Respiratory Effort and Oxygen Desaturation

Three-Stage Breathing Effort Quantification for Obstructive Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals

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