Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography

Dietz-Terjung, Sarah; Martin, Amelie Ricarda; Finnsson, Eysteinn; Agustsson, J. S.; Helgason, Snorri; Helgadóttir, Halla; Welsner, Matthias; Taube, Christian; Weinreich, Gerhard; Schöbel, Christoph

doi:10.1007/s11325-021-02316-0

Cited by 13 publications

(17 citation statements)

References 21 publications

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“…On the other hand, ( 11 ) obtained a κ of 0.65 for wake-NREM-REM classification with pulse photoplethysmography in OSA patients with a median AHI of 16.8. Their performance was superior, but the used CNN was fed with a sequence of 100 epochs of 30 s. Similarly, ( 9 ) reached a κ of 0.62 for wake-NREM-REM using actigraphy and RIP in patients with an average AHI of 19.0. Their algorithm required a manual feature extraction on 25 epochs of 30 s. Although the current network reached lower κ scores compared to the latter studies, it offers a realistic approach for sleep-wake classification with unobtrusive sensors, as it is based on single 30 s epochs.…”

Section: Discussionmentioning

confidence: 93%

“…The following studies developed specific sleep staging algorithms for OSA patients based on cardiac and respiratory information. Often, feature-based approaches were implemented to differentiate between sleep stages when expert knowledge was available (7)(8)(9)(10). This implied a disadvantage of the method as prior knowledge was required to find appropriate features.…”

Section: Introductionmentioning

confidence: 99%

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Sleep Diagnostics for Home Monitoring of Sleep Apnea Patients

Huysmans

Borzée

Buyse

et al. 2021

Front. Digit. Health

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Objectives: Sleep time information is essential for monitoring of obstructive sleep apnea (OSA), as the severity assessment depends on the number of breathing disturbances per hour of sleep. However, clinical procedures for sleep monitoring rely on numerous uncomfortable sensors, which could affect sleeping patterns. Therefore, an automated method to identify sleep intervals from unobtrusive data is required. However, most unobtrusive sensors suffer from data loss and sensitivity to movement artifacts. Thus, current sleep detection methods are inadequate, as these require long intervals of good quality. Moreover, sleep monitoring of OSA patients is often less reliable due to heart rate disturbances, movement and sleep fragmentation. The primary aim was to develop a sleep-wake classifier for sleep time estimation of suspected OSA patients, based on single short-term segments of their cardiac and respiratory signals. The secondary aim was to define metrics to detect OSA patients directly from their predicted sleep-wake pattern and prioritize them for clinical diagnosis.Methods: This study used a dataset of 183 suspected OSA patients, of which 36 test subjects. First, a convolutional neural network was designed for sleep-wake classification based on healthier patients (AHI < 10). It employed single 30 s epochs of electrocardiograms and respiratory inductance plethysmograms. Sleep information and Total Sleep Time (TST) was derived for all patients using the short-term segments. Next, OSA patients were detected based on the average confidence of sleep predictions and the percentage of sleep-wake transitions in the predicted sleep architecture.Results: Sleep-wake classification on healthy, mild and moderate patients resulted in moderate κ scores of 0.51, 0.49, and 0.48, respectively. However, TST estimates decreased in accuracy with increasing AHI. Nevertheless, severe patients were detected with a sensitivity of 78% and specificity of 89%, and prioritized for clinical diagnosis. As such, their inaccurate TST estimate becomes irrelevant. Excluding detected OSA patients resulted in an overall estimated TST with a mean bias error of 21.9 (± 55.7) min and Pearson correlation of 0.74 to the reference.Conclusion: The presented framework offered a realistic tool for unobtrusive sleep monitoring of suspected OSA patients. Moreover, it enabled fast prioritization of severe patients for clinical diagnosis.

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Sleep Diagnostics for Home Monitoring of Sleep Apnea Patients

Huysmans

Borzée

Buyse

et al. 2021

Front. Digit. Health

View full text Add to dashboard Cite

show abstract

“…Comparison of the classifier to the literature is difficult, on the one hand, as studies generally use a sequence of multiple epochs as input. Korkelainen et al [ 17 ] and Dietz-Terjung et al [ 18 ] developed deep learning networks for Wake-REM-NREM classification in suspected OSA patients, respectively, based on PSG PPG and PSG RIP with actigraphy. Both studies reached a k score larger than 0.60.…”

Section: Discussionmentioning

confidence: 99%

Capacitively-Coupled ECG and Respiration for Sleep–Wake Prediction and Risk Detection in Sleep Apnea Patients

Huysmans

Castro

Borzée

et al. 2021

Sensors

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Obstructive sleep apnea (OSA) patients would strongly benefit from comfortable home diagnosis, during which detection of wakefulness is essential. Therefore, capacitively-coupled electrocardiogram (ccECG) and bioimpedance (ccBioZ) sensors were used to record the sleep of suspected OSA patients, in parallel with polysomnography (PSG). The three objectives were quality assessment of the unobtrusive signals during sleep, prediction of sleep–wake using ccECG and ccBioZ, and detection of high-risk OSA patients. First, signal quality indicators (SQIs) determined the data coverage of ccECG and ccBioZ. Then, a multimodal convolutional neural network (CNN) for sleep–wake prediction was tested on these preprocessed ccECG and ccBioZ data. Finally, two indices derived from this prediction detected patients at risk. The data included 187 PSG recordings of suspected OSA patients, 36 (dataset “Test”) of which were recorded simultaneously with PSG, ccECG, and ccBioZ. As a result, two improvements were made compared to prior studies. First, the ccBioZ signal coverage increased significantly due to adaptation of the acquisition system. Secondly, the utility of the sleep–wake classifier increased as it became a unimodal network only requiring respiratory input. This was achieved by using data augmentation during training. Sleep–wake prediction on “Test” using PSG respiration resulted in a Cohen’s kappa (κ) of 0.39 and using ccBioZ in κ = 0.23. The OSA risk model identified severe OSA patients with a κ of 0.61 for PSG respiration and κ of 0.39 using ccBioZ (accuracy of 80.6% and 69.4%, respectively). This study is one of the first to perform sleep–wake staging on capacitively-coupled respiratory signals in suspected OSA patients and to detect high risk OSA patients based on ccBioZ. The technology and the proposed framework could be applied in multi-night follow-up of OSA patients.

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“…In a recent proof of principle study, a sufficient match with polysomnography was found with further developed evaluation algorithms using artificial intelligence based on actigraphy and respiratory excursion data. Thus, with a significantly simplified recording technique, a greatly simplified diagnosis of sleep-related respiratory disorders could be established [31]. Drowsiness is a common symptom of OSA that can resolve with successful treatment.…”

Section: Increasing Recognition Of Actigraphy In the Field Of Sleep Medicinementioning

confidence: 99%

Wrist actigraphic approach in primary, secondary and tertiary care based on the principles of predictive, preventive and personalised (3P) medicine

et al. 2021

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Sleep quality and duration as well as activity-rest-cycles at individual level are crucial for maintaining physical and mental health. Although several methods do exist to monitor these parameters, optimal approaches are still under consideration and technological development. Wrist actigraphy is a non-invasive electro-physical method validated in the field of chronobiology to record movements and to allow for monitoring human activity-rest-cycles. Based on the continuous recording of motor activity and light exposure, actigraphy provides valuable information about the quality and quantity of the sleep–wake rhythm and about the amount of motor activity at day and night that is highly relevant for predicting a potential disease and its targeted prevention as well as personalisation of medical services provided to individuals in suboptimal health conditions and patients. Being generally used in the field of sleep medicine, actigraphy demonstrates a great potential to be successfully implemented in primary, secondary and tertiary care, psychiatry, oncology, and intensive care, military and sports medicines as well as epidemiological monitoring of behavioural habits as well as well-being medical support, amongst others. Prediction of disease development and individual outcomes Activity-rest-cycles have been demonstrated to be an important predictor for many diseases including but not restricted to the development of metabolic, psychiatric and malignant pathologies. Moreover, activity-rest-cycles directly impact individual outcomes in corresponding patient cohorts. Targeted prevention Data acquired by actigraphy are instrumental for the evidence-based targeted prevention by analysing individualised patient profiles including light exposure, sleep duration and quality, activity-rest-cycles, intensity and structure of motion pattern. Personalised therapy Wrist actigraphic approach is increasingly used in clinical care. Personalised measurements of sedation/agitation rhythms are useful for ICU patients, for evaluation of motor fatigue in oncologic patients, for an individual enhancement of performance in military and sport medicine. In the framework of personalised therapy intervention, patients can be encouraged to optimise their behavioural habits improving recovery and activity patterns. This opens excellent perspectives for the sleep-inducing medication and stimulants replacement as well as for increasing the role of participatory medicine by visualising and encouraging optimal behavioural patterns of the individual.

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Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography

Cited by 13 publications

References 21 publications

Sleep Diagnostics for Home Monitoring of Sleep Apnea Patients

Sleep Diagnostics for Home Monitoring of Sleep Apnea Patients

Capacitively-Coupled ECG and Respiration for Sleep–Wake Prediction and Risk Detection in Sleep Apnea Patients

Wrist actigraphic approach in primary, secondary and tertiary care based on the principles of predictive, preventive and personalised (3P) medicine

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