AIMC Topic: Polysomnography

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Automatic Detection of Respiratory Effort Related Arousals With Deep Neural Networks From Polysomnographic Recordings.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Sleep disorders have become more common due to the modern lifestyle and stress. The most severe case of sleep disorders called apnea is characterized by a complete breaking block, leading to awakening and subsequent sleep disturbances. The automatic ...

Predicting Age with Deep Neural Networks from Polysomnograms.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The aim of this study was to design a new deep learning framework for end-to-end processing of polysomnograms. This framework can be trained to analyze whole-night polysomnograms without the limitations of and bias towards clinical scoring guidelines...

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...

Predicting Nondiagnostic Home Sleep Apnea Tests Using Machine Learning.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: Home sleep apnea testing (HSAT) is an efficient and cost-effective method of diagnosing obstructive sleep apnea (OSA). However, nondiagnostic HSAT necessitates additional tests that erode these benefits, delaying diagnoses and incre...

Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.

Sleep
STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. ...

Automatic Sleep Stages Classification Combining Semantic Representation and Dynamic Expert System.

Studies in health technology and informatics
Interest in sleep has been growing in the last decades, considering its benefits for well-being, but also to diagnose sleep troubles. The gold standard to monitor sleep consists of recording the course of many physiological parameters during a whole ...

Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: Portable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resulting in underestimations. Tracheal sound (TS), which can be visualized as a spectrogram, carries inf...

Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children's health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagno...

Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Novel approaches are needed to accurately classify and monitor sleep patterns in older adults, particularly those with cognitive impairment and non-normative sleep. Traditional methods ignore underlying sleep architecture in these patient populations...

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. ...