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Sleep Wake Disorders

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Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Sleep staging, a process of identifying the sleep stages associated with polysomnography (PSG) epochs, plays an important role in sleep monitoring and diagnosing sleep disorders. We present in this work a model fusion approach to automate this task. ...

Sleep heart rate variability assists the automatic prediction of long-term cardiovascular outcomes.

Sleep medicine
OBJECTIVE: We aimed to investigate the association between sleep HRV and long-term cardiovascular disease (CVD) outcomes, and further explore whether HRV features can assist the automatic CVD prediction.

Prognostic factors of Rapid symptoms progression in patients with newly diagnosed parkinson's disease.

Artificial intelligence in medicine
Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly different phenotypes, forcing clinicians to follow a multi-parametric approach in patient evaluation, lo...

Deep learning approaches for sleep disorder prediction in an asthma cohort.

The Journal of asthma : official journal of the Association for the Care of Asthma
OBJECTIVE: Sleep is a natural activity of humans that affects physical and mental health; therefore, sleep disturbance may lead to fatigue and lower productivity. This study examined 1 million samples included in the Taiwan National Health Insurance ...

Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.

Sensors (Basel, Switzerland)
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) hav...

Artificial intelligence in sleep medicine: an American Academy of Sleep Medicine position statement.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial application in the sleep disorders center is the assisted (or enhanced) scoring of sleep and associ...

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

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics
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 f...

Identifying Symptom Information in Clinical Notes Using Natural Language Processing.

Nursing research
BACKGROUND: Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used ...

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.

Sleep & breathing = Schlaf & Atmung
BACKGROUND: The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human ...

Self-Organizing Maps for Contrastive Embeddings of Sleep Recordings.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Nowadays, high amounts of data can be acquired in various applications, spurring the need for interpretable data representations that provide actionable insights. Algorithms that yield such representations ideally require as little a priori knowledge...