Real-time apnea-hypopnea event detection during sleep by convolutional neural networks. 2018

Sang Ho Choi, and Heenam Yoon, and Hyun Seok Kim, and Han Byul Kim, and Hyun Bin Kwon, and Sung Min Oh, and Yu Jin Lee, and Kwang Suk Park
Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, South Korea.

Sleep apnea-hypopnea event detection has been widely studied using various biosignals and algorithms. However, most minute-by-minute analysis techniques have difficulty detecting accurate event start/end positions. Furthermore, they require hand-engineered feature extraction and selection processes. In this paper, we propose a new approach for real-time apnea-hypopnea event detection using convolutional neural networks and a single-channel nasal pressure signal. From 179 polysomnographic recordings, 50 were used for training, 25 for validation, and 104 for testing. Nasal pressure signals were adaptively normalized, and then segmented by sliding a 10-s window at 1-s intervals. The convolutional neural networks were trained with the data, which consisted of class-balanced segments, and were then tested to evaluate their event detection performance. According to a segment-by-segment analysis, the proposed method exhibited performance results with a Cohen's kappa coefficient of 0.82, a sensitivity of 81.1%, a specificity of 98.5%, and an accuracy of 96.6%. In addition, the Pearson's correlation coefficient between estimated apnea-hypopnea index (AHI) and reference AHI was 0.99, and the average accuracy of sleep apnea and hypopnea syndrome (SAHS) diagnosis was 94.9% for AHI cutoff values of ≥5, 15, and 30 events/h. Our approach could potentially be used as a supportive method to reduce event detection time in sleep laboratories. In addition, it can be applied to screen SAHS severity before polysomnography.

UI MeSH Term Description Entries
D008297 Male Males
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000328 Adult A person having attained full growth or maturity. Adults are of 19 through 44 years of age. For a person between 19 and 24 years of age, YOUNG ADULT is available. Adults
D000368 Aged A person 65 years of age or older. For a person older than 79 years, AGED, 80 AND OVER is available. Elderly
D012815 Signal Processing, Computer-Assisted Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity. Digital Signal Processing,Signal Interpretation, Computer-Assisted,Signal Processing, Digital,Computer-Assisted Signal Interpretation,Computer-Assisted Signal Interpretations,Computer-Assisted Signal Processing,Interpretation, Computer-Assisted Signal,Interpretations, Computer-Assisted Signal,Signal Interpretation, Computer Assisted,Signal Interpretations, Computer-Assisted,Signal Processing, Computer Assisted
D012890 Sleep A readily reversible suspension of sensorimotor interaction with the environment, usually associated with recumbency and immobility. Sleep Habits,Sleeping Habit,Sleeping Habits,Habit, Sleep,Habit, Sleeping,Habits, Sleep,Habits, Sleeping,Sleep Habit
D012891 Sleep Apnea Syndromes Disorders characterized by multiple cessations of respirations during sleep that induce partial arousals and interfere with the maintenance of sleep. Sleep apnea syndromes are divided into central (see SLEEP APNEA, CENTRAL), obstructive (see SLEEP APNEA, OBSTRUCTIVE), and mixed central-obstructive types. Apnea, Sleep,Hypersomnia with Periodic Respiration,Sleep-Disordered Breathing,Mixed Central and Obstructive Sleep Apnea,Sleep Apnea, Mixed,Sleep Apnea, Mixed Central and Obstructive,Sleep Hypopnea,Apnea Syndrome, Sleep,Apnea Syndromes, Sleep,Apneas, Sleep,Breathing, Sleep-Disordered,Hypopnea, Sleep,Hypopneas, Sleep,Mixed Sleep Apnea,Mixed Sleep Apneas,Sleep Apnea,Sleep Apnea Syndrome,Sleep Apneas,Sleep Apneas, Mixed,Sleep Disordered Breathing,Sleep Hypopneas
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron

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