Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance

Gilst, Merel M van; Wulterkens, Bernice M; Fonseca, Pedro; Radha, Mustafa; Ross, Marco; Moreau, Arnaud; Cerny, Andreas; Anderer, P.; Long, Xi; Dijk, Hans van; Overeem, Sebastiaan

doi:10.1186/s13104-020-05355-0

Cited by 11 publications

(7 citation statements)

References 18 publications

(34 reference statements)

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“…This will involve creating more low-complexity and higher accuracy algorithms for signals acquired from the different sensing modalities. However, algorithms developed for similar signals obtained from different sensing modalities may not be compatible with each other [ 164 ]. Further, the limited availability of accessible data sets makes it difficult to develop and benchmark such algorithms [ 165 ].…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Imtiaz

2021

Sensors

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Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

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Section: Resultsmentioning

confidence: 99%

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Imtiaz

2021

Sensors

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“…Recently, Korkalainen et al 43 performed sleep staging from PPG data obtained with a finger pulse oximeter instead of a wrist-worn device and achieved a slightly higher kappa of 0.54 and a similar accuracy of 68.5% in patients with suspected sleep apnoea. In our earlier work, we reported Cohen's kappa of 0.56 using a clinical data set with a model trained on ECG data 44 , and it has been concluded that the direct application of an ECG-based model on PPG (without transfer learning) decreases the performance. The combined retrain transfer approach presented here outperformed all these works considerably with an average Cohen's kappa of 0.65 and accuracy of 76.36%.…”

Section: Discussionmentioning

confidence: 96%

A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

Radha

Fonseca

Moreau³

et al. 2021

npj Digit. Med.

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Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen’s kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.

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“… 13 , 14 These factors may be some of the reasons that ECG-derived algorithms cannot be used on PPG-based HRV without a significantly negative impact on performance. 15 Accordingly, the translation from an ECG-based model to a clinically applicable PPG-based model is a non-trivial step and requires either the integration, in the model, of knowledge about the differences between the modalities, or alternatively, that the model is trained with data acquired with the target sensor, ie, PPG. Either approach requires a sufficient amount of data containing both gold standard PSG and PPG measurements from a clinical population.…”

Section: Introductionmentioning

confidence: 99%

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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Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance

Cited by 11 publications

References 18 publications

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

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

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