Sleep apnea screening based on Photoplethysmography data from wearable bracelets using an information-based similarity approach

Wu, Shan; Chen, Mingjing; Wei, Keming; Liu, Guanzheng

doi:10.1016/j.cmpb.2021.106442

Cited by 11 publications

(10 citation statements)

References 42 publications

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“…The sensitivity of the algorithms presented in these studies averages off at 77%. This might suggest that the intrinsic instability of the wearable sensor placed around the wrist 25 , 50 or the thorax, as in Baty et al 28 may account for the lower performance compared to non-wearable devices (as later discussed in Limitations).…”

Section: Discussionmentioning

confidence: 98%

Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach

Benedetti¹,

Olcese²,

Bruno³

et al. 2022

NSS

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Purpose A large portion of the adult population is thought to suffer from obstructive sleep apnoea syndrome (OSAS), a sleep-related breathing disorder associated with increased morbidity and mortality. International guidelines include the polysomnography and the cardiorespiratory monitoring (CRM) as diagnostic tools for OSAS, but they are unfit for a large-scale screening, given their invasiveness, high cost and lengthy process of scoring. Current screening methods are based on self-reported questionnaires that suffer from lack of objectivity. On the contrary, commercial smartbands are wearable devices capable of collecting accelerometric and photoplethysmographic data in a user-friendly and objective way. We questioned whether machine-learning (ML) classifiers trained on data collected through these wearable devices would help predict OSAS severity. Patients and Methods Each of the patients (n = 78, mean age ± SD: 57.2 ± 12.9 years; 30 females) underwent CRM and concurrently wore a commercial wrist smartband. CRM’s traces were scored, and OSAS severity was reported as apnoea hypopnoea index (AHI). We trained three pairs of classifiers to make the following prediction: AHI <5 vs AHI ≥5, AHI <15 vs AHI ≥15, and AHI <30 vs AHI ≥30. Results According to the Matthews correlation coefficient (MCC), the proposed algorithms reached an overall good correlation with the ground truth (CRM) for AHI <5 vs AHI ≥5 (MCC: 0.4) and AHI <30 vs AHI ≥30 (MCC: 0.3) classifications. AHI <5 vs AHI ≥5 and AHI <30 vs AHI ≥30 classifiers’ sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV) and diagnostic odds ratio (DOR) are comparable with the STOP-Bang questionnaire, an established OSAS screening tool. Conclusion Machine learning algorithms showed an overall good performance. Unlike questionnaires, these are based on objectively collected data. Furthermore, these commercial devices are widely distributed in the general population. The aforementioned advantages of machine-learning algorithms applied to smartbands’ data over questionnaires lead to the conclusion that they could serve a population-scale screening for OSAS.

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Section: Discussionmentioning

confidence: 98%

Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach

Benedetti¹,

Olcese²,

Bruno³

et al. 2022

NSS

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“…In particular, the studies were selected according to the datasets used and to characterize the most widespread and valuable sleep indexes extracted in literature. It is worth noting from the table that the studies based on EEG, ECG and PPG signals [ 32 , 36 , 57 , 58 , 59 , 60 , 61 , 62 ] can be used to extract valuable information on sleep stages or sleep apnea; however, they need higher computational cost and specialized devices for signal acquisition. On the other hand, other works based on motion signal from accelerometer and PBS [ 7 , 10 , 24 , 63 , 64 ] underline the advantage of causing low or mild discomfort to generally detect sleep and wake phases.…”

Section: Discussionmentioning

confidence: 99%

Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device

Coluzzi

Baselli

Bianchi

et al. 2022

Sensors

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Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated (0.72±0.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85±0.007) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects’ awareness.

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“…In particular, for which specific disease states can wearables be used to improve the patients’ situation? For example, current research exists on chronic obstructive pulmonary disease (COPD) [ 61 ], depression [ 62 ], distress [ 63 ], multiple sclerosis [ 64 ], Parkinson's disease [ 65 ], post-surgery complications [ 66 ], and sleep apnea [ 67 ]. We believe that many further application fields exist.…”

Section: Discussionmentioning

confidence: 99%

Adopting wearables to customize health insurance contributions: a ranking-type Delphi

Neumann

Tiberius

Biendarra³

2022

BMC Med Inform Decis Mak

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Background Wearables, as small portable computer systems worn on the body, can track user fitness and health data, which can be used to customize health insurance contributions individually. In particular, insured individuals with a healthy lifestyle can receive a reduction of their contributions to be paid. However, this potential is hardly used in practice. Objective This study aims to identify which barrier factors impede the usage of wearables for assessing individual risk scores for health insurances, despite its technological feasibility, and to rank these barriers according to their relevance. Methods To reach these goals, we conduct a ranking-type Delphi study with the following three stages. First, we collected possible barrier factors from a panel of 16 experts and consolidated them to a list of 11 barrier categories. Second, the panel was asked to rank them regarding their relevance. Third, to enhance the panel consensus, the ranking was revealed to the experts, who were then asked to re-rank the barriers. Results The results suggest that regulation is the most important barrier. Other relevant barriers are false or inaccurate measurements and application errors caused by the users. Additionally, insurers could lack the required technological competence to use the wearable data appropriately. Conclusion A wider use of wearables and health apps could be achieved through regulatory modifications, especially regarding privacy issues. Even after assuring stricter regulations, users’ privacy concerns could partly remain, if the data exchange between wearables manufacturers, health app providers, and health insurers does not become more transparent.

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Sleep apnea screening based on Photoplethysmography data from wearable bracelets using an information-based similarity approach

Cited by 11 publications

References 42 publications

Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach

Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach

Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device

Adopting wearables to customize health insurance contributions: a ranking-type Delphi

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