Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography

Narayan, Sanjiv M.; Shivdare, Priyanka; Niranjan, Tharun; Williams, Kathryn; Freudman, Jon; Sehra, Ruchir

doi:10.1007/s11325-018-1695-6

Cited by 21 publications

(16 citation statements)

References 32 publications

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“…Smartphones feature various apps [ 25 ]. Internal sensors and external (connectable) devices measure blood oxygen, pulse, body movement (using accelerometers or sonars), and breath sounds during sleep [ 12 ]. Studies using oximeter/accelerometer combinations to diagnose sleep apnea found that body position data aided in respiratory movement assessment [ 26 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…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%

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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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“…A less accurate, but very low–burden, detection method is to use smartphone screen interaction to approximate bedtimes and waking times 157 . Smartphones have also shown promise for the evaluation of perceived sleep quality using patient reports as well as detection of sleep‐disordered breathing using built‐in microphones 158‐161 . In addition, the MyHeart Counts study from Apple uses smartphones to detect ambient light 162 .…”

Section: Digital Phenotyping: Mobile Technology To Collect Pghdmentioning

confidence: 99%

Innovations in research and clinical care using patient‐generated health data

Jim

Hoogland

Brownstein

et al. 2020

CA A Cancer J Clinicians

114

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Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

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“…Narayan et al demonstrated that it is possible to detect disturbed and normal breathing patterns from ambient sounds recorded by an unmodified smartphone microphone . The authors studied 91 patients undergoing polysomnography by using a Samsung Galaxy S5 smartphone placed less than 1 m from the head of the bed to record ambient sounds.…”

Section: Four System Architectures Of Biosensors For Personal Mhealthmentioning

confidence: 99%

Biosensors for Personal Mobile Health: A System Architecture Perspective

Arumugam

Colburn

Sia

2019

Adv Materials Technologies

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Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor‐based sensors are enabling measurement of vital signs, and microfluidic‐based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it can become critical for researchers to design biosensors—with appropriate functionalities and specifications—to work seamlessly with accompanying connected hardware and software. Here, recent research in biosensors, as well as current mobile health devices in use is described and classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. The path forward for integrating biosensors into smartphone‐based mobile health devices is also discussed.

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Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography

Abstract: NCT03288376; clinicaltrials.org.

Cited by 21 publications

References 32 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

Innovations in research and clinical care using patient‐generated health data

Biosensors for Personal Mobile Health: A System Architecture Perspective

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