Shumit Saha1, Muammar Kabir2, Nasim Montazeri Ghahjaverestan1, Maziar Hafezi1, Bojan Gavrilovic2, Kaiyin Zhu2, Hisham Alshaer3, Azadeh Yadollahi4. 1. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada; KITE-Toronto Rehabilitation Institute, University Health Network, Canada. 2. KITE-Toronto Rehabilitation Institute, University Health Network, Canada. 3. KITE-Toronto Rehabilitation Institute, University Health Network, Canada; BresoTEC Inc, Toronto, ON, Canada. 4. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada; KITE-Toronto Rehabilitation Institute, University Health Network, Canada. Electronic address: Azadeh.Yadollahi@uhn.ca.
Abstract
STUDY OBJECTIVE: To develop an algorithm for improving apnea hypopnea index (AHI) estimation which includes event by event validation and event duration estimation. The algorithm uses breathing sounds, respiratory related movements and blood oxygen saturation (SaO2). METHODS: Adults with suspected sleep apnea underwent overnight polysomnography (PSG) at Toronto Rehabilitations Institute. Simultaneously with PSG, breathing sounds and respiratory related movements were recorded over the suprasternal notch using the Patch. The Patch had a microphone and an accelerometer to record respiratory sounds and movement, respectively. First, we calculated the amount of drops in SaO2 from pulse oximeter. Subsequently, energy of breaths and accelerometer were extracted. Features were normalized, weighted, summed and passed through a threshold to estimate PatchAHI. PatchAHI was compared to the AHI obtained from PSG (PSGAHI). Furthermore, performance of event detection was evaluated using F1-score. Moreover, event duration difference between estimated and PSG-based events was compared. RESULTS: Data from 69 subjects were investigated. PatchAHI had high correlation with PSGAHI (r2 = 0.88). Considering a diagnostic AHI cut-off of ≥15, sensitivity and specificity were 91.42 ± 11.92% and 89.29 ± 7.62%, respectively. F1-score for individual event detection increased from 0.22 ± 0.10 for AHI≤5 to 0.72 ± 0.09 for AHI >30. Moreover, event duration difference between estimated events and PSG-based events was 5.33 ± 8.17 sec. CONCLUSION: Our proposed algorithm had high accuracy in estimating individual respiratory events during sleep. The algorithm can increase reliability of acoustic methods for diagnosis of sleep apnea at home.
STUDY OBJECTIVE: To develop an algorithm for improving apnea hypopnea index (AHI) estimation which includes event by event validation and event duration estimation. The algorithm uses breathing sounds, respiratory related movements and blood oxygen saturation (SaO2). METHODS: Adults with suspected sleep apnea underwent overnight polysomnography (PSG) at Toronto Rehabilitations Institute. Simultaneously with PSG, breathing sounds and respiratory related movements were recorded over the suprasternal notch using the Patch. The Patch had a microphone and an accelerometer to record respiratory sounds and movement, respectively. First, we calculated the amount of drops in SaO2 from pulse oximeter. Subsequently, energy of breaths and accelerometer were extracted. Features were normalized, weighted, summed and passed through a threshold to estimate PatchAHI. PatchAHI was compared to the AHI obtained from PSG (PSGAHI). Furthermore, performance of event detection was evaluated using F1-score. Moreover, event duration difference between estimated and PSG-based events was compared. RESULTS: Data from 69 subjects were investigated. PatchAHI had high correlation with PSGAHI (r2 = 0.88). Considering a diagnostic AHI cut-off of ≥15, sensitivity and specificity were 91.42 ± 11.92% and 89.29 ± 7.62%, respectively. F1-score for individual event detection increased from 0.22 ± 0.10 for AHI≤5 to 0.72 ± 0.09 for AHI >30. Moreover, event duration difference between estimated events and PSG-based events was 5.33 ± 8.17 sec. CONCLUSION: Our proposed algorithm had high accuracy in estimating individual respiratory events during sleep. The algorithm can increase reliability of acoustic methods for diagnosis of sleep apnea at home.
Authors: Vivek Tangudu; Kahkashan Afrin; Sandy Reddy; Nicolaas E P Deutz; Steven Woltering; Satish T S Bukkapatnam Journal: Sleep Breath Date: 2020-08-31 Impact factor: 2.816