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Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram
Much attention has been given to automatic sleep staging algorithms in p...
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Automated Respiratory Event Detection Using Deep Neural Networks
The gold standard to assess respiration during sleep is polysomnography;...
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Comparing sleep studies in terms of the Apnea-Hypopnea Index
The Apnea-Hypopnea Index (AHI) is one of the most-used parameters from t...
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A Clinical Evaluation of a Low-Cost Strain Gauge Respiration Belt and Machine Learning to Detect Sleep Apnea
Sleep apnea is a serious and severely under-diagnosed sleep-related resp...
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Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals
The obstructive sleep apnea-hypopnea (OSAH) syndrome is a very common an...
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Sensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data
Sleep apnea is a common respiratory disorder characterized by breathing ...
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Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Objective: The aim of this study is to develop an automated classificati...
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MSED: a multi-modal sleep event detection model for clinical sleep analysis
Study objective: Clinical sleep analysis require manual analysis of sleep patterns for correct diagnosis of sleep disorders. Several studies show significant variability in scoring discrete sleep events. We wished to investigate, whether an automatic method could be used for detection of arousals (Ar), leg movements (LM) and sleep disordered breathing (SDB) events, and if the joint detection of these events performed better than having three separate models. Methods: We designed a single deep neural network architecture to jointly detect sleep events in a polysomnogram. We trained the model on 1653 recordings of individuals, and tested the optimized model on 1000 separate recordings. The performance of the model was quantified by F1, precision, and recall scores, and by correlating index values to clinical values using Pearson's correlation coefficient. Results: F1 scores for the optimized model was 0.70, 0.63, and 0.62 for Ar, LM, and SDB, respectively. The performance was higher, when detecting events jointly compared to corresponding single-event models. Index values computed from detected events correlated well with manual annotations (r^2 = 0.73, r^2 = 0.77, r^2 = 0.78, respectively). Conclusion: Detecting arousals, leg movements and sleep disordered breathing events jointly is possible, and the computed index values correlates well with human annotations.
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