DeepSleep

Rao, Shashank; Ali, Abdallah El; César, Pablo

doi:10.1145/3341162.3343758

Cited by 5 publications

(2 citation statements)

References 12 publications

(4 reference statements)

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“…Various sensors have been investigated for sleep staging. Relevant measurements which have proven promising in recent years for sleep-wake analysis are actigraphy [17,15,27], accelerometers body-worn and on the bed [37,21], PPG [36], ballistocardiography [29], and cameras [18]. Classifying sleep from wake using accelerometers is generally performing well, as movement is known as one of the main factors to distinguish them.…”

Section: Related Workmentioning

confidence: 99%

“…Home-monitoring sensors provide an unbiased data source, as data are collected in a natural environment and the number of body-attached sensors is minimal, therefore, less likely to influence sleep behavior compared to PSG [26]. In this context, a number of sensors such as actigraphy [17,15,27], photoplethysmography (PPG) [36], ballistocardiography [29], and non-contact microphones [7] have been applied to detect sleep-wake patterns and some progress has been made. The limitations of current methods are: (1) The use of an one-model-fits-all approach, trained and tested on (2) too small and (3) non-diverse datasets, (4) extracting too many features and (5) many factors influence sleep but have not been included in sleep stage detection.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced multi-source data analysis for personalized sleep-wake pattern recognition and sleep parameter extraction

Fallmann

Chen

2020

Pers Ubiquit Comput

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Sleep behavior is traditionally monitored with polysomnography, and sleep stage patterns are a key marker for sleep quality used to detect anomalies and diagnose diseases. With the growing demand for personalized healthcare and the prevalence of the Internet of Things, there is a trend to use everyday technologies for sleep behavior analysis at home, having the potential to eliminate expensive in-hospital monitoring. In this paper, we conceived a multi-source data mining approach to personalized sleep-wake pattern recognition which uses physiological data and personal information to facilitate fine-grained detection. Physiological data includes actigraphy and heart rate variability and personal data makes use of gender, health status and race information which are known influence factors. Moreover, we developed a personalized sleep parameter extraction technique fused with the sleep-wake approach, achieving personalized instead of static thresholds for decision-making. Results show that the proposed approach improves the accuracy of sleep and wake stage recognition, therefore, offers a new solution for personalized sleep-based health monitoring.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced multi-source data analysis for personalized sleep-wake pattern recognition and sleep parameter extraction

Fallmann

Chen

2020

Pers Ubiquit Comput

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Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research

2021

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Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that can handle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.

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Supervised Classification with Short-Term Memory of Sleep Stages using Cardio-respiratory and Body Movement Variables

Gasmi

Augusto

Beaudet³

et al. 2021

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

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In the context of the Internet of Things (IoT) healthcare, biophysical features collected during sleep needs robust analysis methods to be efficiently used to detect sleep disorders. In this paper, analysis methods using a limited number of input variables (cardiac, respiratory, and body movement) have been used to perform the classification of sleep stages. The efficiency of each classification method has been compared to a reference method that combines a large number of biophysical features referred to as PolySomnoGraphy (PSG). Five classical machine learning methods were evaluated by testing their accuracy on the same collected data. Finally, using a neural network with a short memory method, the classification task fitted 91.34% of the PSG classification.Sleep stages, machine learning, supervised classification, sleep architecture, polysomnography 1) Wake stage, characterised by slow muscle activity and slow eye movement. It represents less than 2% of the total sleep duration in standard sleep conditions.

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DeepSleep

Cited by 5 publications

References 12 publications

Enhanced multi-source data analysis for personalized sleep-wake pattern recognition and sleep parameter extraction

Enhanced multi-source data analysis for personalized sleep-wake pattern recognition and sleep parameter extraction

Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research

Supervised Classification with Short-Term Memory of Sleep Stages using Cardio-respiratory and Body Movement Variables

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