Mobile Apnea Screening System for at-home Recording and Analysis of Sleep Apnea Severity

Bonnesen, Mathias Pinto; Sørensen, Helge Bjarup Dissing; Jennum, Poul

doi:10.1109/embc.2018.8512335

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Snoring differs between healthy people and sleep apnea patients; the noises alone accurately separate the groups [ 28 ]. In addition, various recent apps feature algorithms analyzing oxygen saturation, body position during sleep, and sleep breathing sounds [ 5 – 10 , 12 , 14 , 15 , 29 ]. Furthermore, because smartphones use motion, sound, oximetry, and combinations thereof to detect abnormal sleep, we evaluated the effects of the various methods on diagnostic accuracy; we performed subgroup analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies have compared the reliabilities of such apps to the reliability of PSG [ 5 , 8 , 16 – 19 ]. Because moderate-to-severe OSA ([AHI ≥ 15/h) is associated with high risks of cardiovascular morbidity and mortality, and thus requires treatment [ 30 ], many studies have sought to clinically validate apps by screening for such OSA [ 5 – 10 , 12 , 14 , 15 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

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Diagnostic value of smartphone in obstructive sleep apnea syndrome: A systematic review and meta-analysis

Kim

Hwang

2022

PLoS ONE

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Objectives To assess the diagnostic utility of smartphone-based measurement in detecting moderate to severe obstructive sleep apnea syndrome (OSAS). Methods Six databases were thoroughly reviewed. Random-effect models were used to estimate the summary sensitivity, specificity, negative predictive value, positive predictive value, diagnostic odds ratio, summary receiver operating characteristic curve and measured the areas under the curve. To assess the accuracy and precision, pooled mean difference and standard deviation of apnea hypopnea index (AHI) between smartphone and polysomnography (95% limits of agreement) across studies were calculated using the random-effects model. Study methodological quality was evaluated using the QUADAS-2 tool. Results Eleven studies were analyzed. The smartphone diagnostic odds ratio for moderate-to-severe OSAS (apnea/hypopnea index > 15) was 57.3873 (95% confidence interval [CI]: [34.7462; 94.7815]). The area under the summary receiver operating characteristic curve was 0.917. The sensitivity, specificity, negative predictive value, and positive predictive value were 0.9064 [0.8789; 0.9282], 0.8801 [0.8227; 0.9207], 0.9049 [0.8556; 0.9386], and 0.8844 [0.8234; 0.9263], respectively. We performed subgroup analysis based on the various OSAS detection methods (motion, sound, oximetry, and combinations thereof). Although the diagnostic odds ratios, specificities, and negative predictive values varied significantly (all p < 0.05), all methods afforded good sensitivity (> 80%). The sensitivities and positive predictive values were similar for the various methods (both p > 0.05). The mean difference with standard deviation in the AHI between smartphone and polysomnography was -0.6845 ± 1.611 events/h [-3.8426; 2.4735]. Conclusions Smartphone could be used to screen the moderate-to-severe OSAS. The mean difference between smartphones and polysomnography AHI measurements was small, though limits of agreement was wide. Therefore, clinicians should be cautious when making clinical decisions based on these devices.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Diagnostic value of smartphone in obstructive sleep apnea syndrome: A systematic review and meta-analysis

Kim

Hwang

2022

PLoS ONE

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“…The relative low cost and high quality of smartphone sensors, and its widespread availability, have led to their multiple mHealth applications, including sleep medicine. Some systems and apps have been proposed to screen or monitor OSA patients at home [5][6][7]. They integrate different smartphone built-in or external sensors, such as microphones, accelerometers or pulse oximeters, to extract OSA severity indices.…”

Section: Introductionmentioning

confidence: 99%

Automatic Silence Events Detector from Smartphone Audio Signals: A Pilot mHealth System for Sleep Apnea Monitoring at Home

Castillo-Escario

Ferrer-Lluis

Montserrat

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Obstructive sleep apnea (OSA) is a prevalent disease, but most patients remain undiagnosed and untreated. Recently, mHealth tools are being proposed to screen OSA patients at home. In this work, we analyzed full-night audio signals recorded with a smartphone microphone. Our objective was to develop an automatic detector to identify silence events (apneas or hypopneas) and compare its performance to a commercial portable system for OSA diagnosis (ApneaLink TM , ResMed). To do that, we acquired signals from three subjects with both systems simultaneously. A sleep specialist marked the events on smartphone and ApneaLink signals. The automatic detector we developed, based on the sample entropy, identified silence events similarly than manual annotation. Compared to ApneaLink, it was very sensitive to apneas (detecting 86.2%) and presented an 83.4% positive predictive value, but it missed about half the hypopnea episodes. This suggests that during some hypopneas the flow reduction is not reflected in sound. Nevertheless, our detector accurately recognizes silence events, which can provide valuable respiratory information related to the disease. These preliminary results show that mHealth devices and simple microphones are promising non-invasive tools for personalized sleep disorders management at home.

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Sparse spectrum based swarm decomposition for robust nonstationary signal analysis with application to sleep apnea detection from EEG

Bhalerao

Pachori

2022

Biomedical Signal Processing and Control

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Mobile Apnea Screening System for at-home Recording and Analysis of Sleep Apnea Severity

Cited by 6 publications

References 9 publications

Diagnostic value of smartphone in obstructive sleep apnea syndrome: A systematic review and meta-analysis

Diagnostic value of smartphone in obstructive sleep apnea syndrome: A systematic review and meta-analysis

Automatic Silence Events Detector from Smartphone Audio Signals: A Pilot mHealth System for Sleep Apnea Monitoring at Home

Sparse spectrum based swarm decomposition for robust nonstationary signal analysis with application to sleep apnea detection from EEG

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