SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography

Kotzen, Kevin; Charlton, Peter; Salabi, Sharon; Lea, Amar,; Landesberg, Amir; Behar, Joachim A.

doi:10.1109/jbhi.2022.3225363

Cited by 25 publications

(20 citation statements)

References 42 publications

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“…The profusion files per patient were already labeled with this information, which we could use to compute the total sleeping time. This information can also be estimated from other sensors like photoplethysmograms (PPG) [43] or accelerometers. Here, to provide a standalone solution, we investigated classifying sleep or awake from combinations of only the available three sensors considered in the paper.…”

Section: Methodsmentioning

confidence: 99%

Towards automatic home-based sleep apnea estimation using deep learning

Gavidia

Montanari²

2023

Preprint

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Obstructive sleep apnea (OSA) is a common respiratory condition characterized by respiratory tract obstruction and breathing disorder. Early detection and treatment of OSA can significantly reduce morbidity and mortality. OSA is often diagnosed with overnight polysomnography (PSG) monitoring; however, continuous PSG monitoring is unfeasible as it is costly and uncomfortable for patients. To circumvent these issues, we propose a detection method of OSA events, named DRIVEN, using only two signals that can be easily measured at home: abdominal movement and pulse oximetry. On test data, DRIVEN achieves an accuracy and F1-score of 88%, a reasonable trade-off between the models performance and patient comfort. We use data from three sleep studies from the National Sleep Research Resource (NSRR), the largest public repository, consisting of 10,878 recordings. DRIVEN is based on a combination of deep convolutional neural networks and a light-gradient-boost machine for classification. Since DRIVEN is simple and computationally efficient, we expect that it can be implemented for automatic detection of OSA in unsupervised long-term home monitoring systems, reducing costs to healthcare systems and improving patient care.

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Section: Methodsmentioning

confidence: 99%

Towards automatic home-based sleep apnea estimation using deep learning

Gavidia

Montanari²

2023

Preprint

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“…Kotzen et al [ 24 ] developed SleepPPG-Net, a DL model for 4-class sleep staging from the raw PPG time series. SleepPPG-Net was trained end-to-end and consists of a residual convolutional network for automatic feature extraction and a temporal convolutional network to capture long-range contextual information.…”

Section: Related Workmentioning

confidence: 99%

PPG2EMG: Estimating Upper-Arm Muscle Activities and EMG from Wrist PPG Values

Okamoto

Murao

2023

Sensors

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The electromyogram (EMG) is a waveform representation of the action potential generated by muscle cells using electrodes. EMG acquired using surface electrodes is called surface EMG (sEMG), and it is the acquisition of muscle action potentials transmitted by volume conduction from the skin. Surface electrodes require disposable conductive gel or adhesive tape to be attached to the skin, which is costly to run, and the tape is hard on the skin when it is removed. Muscle activity can be evaluated by acquiring muscle potentials and analyzing quantitative, temporal, and frequency factors. It is also possible to evaluate muscle fatigue because the frequency of the EMG becomes lower as the muscle becomes fatigued. Research on human activity recognition from EMG signals has been actively conducted and applied to systems that support arm and hand functions. This paper proposes a method for recognizing the muscle activity state of the arm using pulse wave data (PPG: Photoplethysmography) and a method for estimating EMG using pulse wave data. This paper assumes that the PPG sensor is worn on the user’s wrist to measure the heart rate. The user also attaches an elastic band to the upper arm, and when the user exerts a force on the arm, the muscles of the upper arm contract. The arteries are then constricted, and the pulse wave measured at the wrist becomes weak. From the change in the pulse wave, the muscle activity of the arm can be recognized and the number of action potentials of the muscle can be estimated. From the evaluation experiment with five subjects, three types of muscle activity were recognized with 80+%, and EMG was estimated with approximately 20% error rate.

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“…Recently, the applications of photoplethysmography-based wearables have been expanded to include atrial fibrillation detection (Perez et al 2019 ), blood pressure monitoring (Vybornova et al 2021 ), and oxygen saturation monitoring (Spaccarotella et al 2022 ). Several additional potential applications of wearable photoplethysmography devices are being researched (Charlton et al 2023 ), including sleep staging (Kotzen et al 2022 ), mental health assessment (Cakmak et al 2021 , Lyzwinski et al 2023 ), identifying obstructive sleep apnea (Behar et al 2014 , 2019 ), and detection of peripheral arterial disease (Stansby et al 2022 ). Each of these applications uses PPG signal analysis to derive physiological information from the PPG.…”

Section: Introductionmentioning

confidence: 99%

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis

Goda,

Charlton,

Behar

2024

Physiol. Meas.

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Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2,054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ~5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3,000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Based on these fiducial points, pyPPG engineered a set of 74 PPG biomarkers. Studying PPG time-series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on physiozoo.com.

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SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography

Cited by 25 publications

References 42 publications

Towards automatic home-based sleep apnea estimation using deep learning

Towards automatic home-based sleep apnea estimation using deep learning

PPG2EMG: Estimating Upper-Arm Muscle Activities and EMG from Wrist PPG Values

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis

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