Protocol of the SOMNIA project: an observational study to create a neurophysiological database for advanced clinical sleep monitoring

Gilst, Merel M van; Dijk, Hans van; Krijn, Roy; Hoondert, Bertram; Fonseca, Pedro; Sloun, Ruud J. G. van; Arsenali, Bruno; Vandenbussche, Nele; Pillen, Sigrid; Maass, Henning; Heuvel, Leonie van den; Haakma, Reinder; Leufkens, Tim; Lauwerijssen, Coen; Bergmans, J.W.M.; Pevernagie, Dirk; Overeem, Sebastiaan

doi:10.1136/bmjopen-2019-030996

Cited by 45 publications

(27 citation statements)

References 50 publications

(55 reference statements)

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“…PPG and accelerometer data were obtained with a CE-marked wrist-worn sensor (Philips Research, Eindhoven, the Netherlands), containing a three-axial accelerometer (sampling frequency: 128 Hz) and a PPG sensor with a light source consisting of two green light LEDs (sampling frequency: 32 Hz). 19 Participants wore the device on their non-dominant wrist, with the sensor facing the skin on the dorsal side of the hand, above the ulnar styloid process.…”

Section: Methodsmentioning

confidence: 99%

“…Sleep Disordered Patients - The training dataset included all 422 sleep disordered patients (416 adults and 6 children/adolescents) available in the Sleep and Obstructive Sleep Apnea Measuring with Non-Invasive Applications (SOMNIA) cohort by August 15th 2018. 19 The SOMNIA dataset is created by Kempenhaeghe, and includes data from patients with a wide range of sleep disorders, including sleep-related breathing disorders, insomnia, sleep-related movement disorders and parasomnias. The primary sleep diagnosis was coded according to the International Classification of Sleep Disorders (ICSD) criteria.…”

Section: Methodsmentioning

confidence: 99%

“…The study protocols were described in detail by Fonseca et al 23 The remaining 40 recordings were acquired from the HealthBed dataset, collected at the Sleep Medicine Center Kempenhaeghe, using the same protocol as the SOMNIA database. 19 This set included all recordings that were available up to 15 October 2018. The HealthBed dataset comprises healthy adults without sleep disorders or other medical or psychiatric comorbidity.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

It is All in the Wrist: Wearable Sleep Staging in a Clinical Population versus Reference Polysomnography

Wulterkens¹,

Fonseca²,

Hermans³

et al. 2021

NSS

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Purpose There is great interest in unobtrusive long-term sleep measurements using wearable devices based on reflective photoplethysmography (PPG). Unfortunately, consumer devices are not validated in patient populations and therefore not suitable for clinical use. Several sleep staging algorithms have been developed and validated based on ECG-signals. However, translation from these techniques to data derived by wearable PPG is not trivial, and requires the differences between sensing modalities to be integrated in the algorithm, or having the model trained directly with data obtained with the target sensor. Either way, validation of PPG-based sleep staging algorithms requires a large dataset containing both gold standard measurements and PPG-sensor in the applicable clinical population. Here, we take these important steps towards unobtrusive, long-term sleep monitoring. Methods We developed and trained an algorithm based on wrist-worn PPG and accelerometry. The method was validated against reference polysomnography in an independent clinical population comprising 244 adults and 48 children (age: 3 to 82 years) with a wide variety of sleep disorders. Results The classifier achieved substantial agreement on four-class sleep staging with an average Cohen’s kappa of 0.62 and accuracy of 76.4%. For children/adolescents, it achieved even higher agreement with an average kappa of 0.66 and accuracy of 77.9%. Performance was significantly higher in non-REM parasomnias (kappa = 0.69, accuracy = 80.1%) and significantly lower in REM parasomnias (kappa = 0.55, accuracy = 72.3%). A weak correlation was found between age and kappa ( ρ = −0.30, p<0.001) and age and accuracy ( ρ = −0.22, p<0.001). Conclusion This study shows the feasibility of automatic wearable sleep staging in patients with a broad variety of sleep disorders and a wide age range. Results demonstrate the potential for ambulatory long-term monitoring of clinical populations, which may improve diagnosis, estimation of severity and follow up in both sleep medicine and research.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

It is All in the Wrist: Wearable Sleep Staging in a Clinical Population versus Reference Polysomnography

Wulterkens¹,

Fonseca²,

Hermans³

et al. 2021

NSS

Self Cite

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“…We employed the SOMNIA and HealthBed datasets collected at the Kempenhaeghe Sleep Center 28 . We used the first 469 participants included in the SOMNIA database, which consists of simultaneous PSG recordings, clinical information and unobtrusive sensors recordings (e.g.…”

Section: Methodsmentioning

confidence: 99%

Wearable monitoring of sleep-disordered breathing: estimation of the apnea–hypopnea index using wrist-worn reflective photoplethysmography

Papini

Fonseca

Gilst

et al. 2020

Sci Rep

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A large part of the worldwide population suffers from obstructive sleep apnea (OSA), a disorder impairing the restorative function of sleep and constituting a risk factor for several cardiovascular pathologies. The standard diagnostic metric to define OSA is the apnea–hypopnea index (AHI), typically obtained by manually annotating polysomnographic recordings. However, this clinical procedure cannot be employed for screening and for long-term monitoring of OSA due to its obtrusiveness and cost. Here, we propose an automatic unobtrusive AHI estimation method fully based on wrist-worn reflective photoplethysmography (rPPG), employing a deep learning model exploiting cardiorespiratory and sleep information extracted from the rPPG signal trained with 250 recordings. We tested our method with an independent set of 188 heterogeneously disordered clinical recordings and we found it estimates the AHI with a good agreement to the gold standard polysomnography reference (correlation = 0.61, estimation error = 3±10 events/h). The estimated AHI was shown to reliably assess OSA severity (weighted Cohen’s kappa = 0.51) and screen for OSA (ROC–AUC = 0.84/0.86/0.85 for mild/moderate/severe OSA). These findings suggest that wrist-worn rPPG measurements that can be implemented in wearables such as smartwatches, have the potential to complement standard OSA diagnostic techniques by allowing unobtrusive sleep and respiratory monitoring.

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“…The patient underwent a full-night polysomnography within the SOMNIA project (Sleep and OSAMeasuring with Non-Invasive Applications), which collected data from patients with a wide range of sleep disorders [ 38 ]. Two monopolar Al/AgCl electrodes were positioned on the 6th intercostal space (Compumedics Grael, sampling frequency = 512 Hz).…”

Section: Methodsmentioning

confidence: 99%

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram

Peri

Ciccarelli

et al. 2021

Sensors

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A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.

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Protocol of the SOMNIA project: an observational study to create a neurophysiological database for advanced clinical sleep monitoring

Cited by 45 publications

References 50 publications

It is All in the Wrist: Wearable Sleep Staging in a Clinical Population versus Reference Polysomnography

It is All in the Wrist: Wearable Sleep Staging in a Clinical Population versus Reference Polysomnography

Wearable monitoring of sleep-disordered breathing: estimation of the apnea–hypopnea index using wrist-worn reflective photoplethysmography

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram

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