Artificial neural networks for breathing and snoring episode detection in sleep sounds. 2012

Takahiro Emoto, and Udantha R Abeyratne, and Yongjian Chen, and Ikuji Kawata, and Masatake Akutagawa, and Yohsuke Kinouchi
Institute of Technology and Science, The University of Tokushima, Tokushima, Japan. emoto@ee.tokushima-u.ac.jp

Obstructive sleep apnea (OSA) is a serious disorder characterized by intermittent events of upper airway collapse during sleep. Snoring is the most common nocturnal symptom of OSA. Almost all OSA patients snore, but not all snorers have the disease. Recently, researchers have attempted to develop automated snore analysis technology for the purpose of OSA diagnosis. These technologies commonly require, as the first step, the automated identification of snore/breathing episodes (SBE) in sleep sound recordings. Snore intensity may occupy a wide dynamic range (> 95 dB) spanning from the barely audible to loud sounds. Low-intensity SBE sounds are sometimes seen buried within the background noise floor, even in high-fidelity sound recordings made within a sleep laboratory. The complexity of SBE sounds makes it a challenging task to develop automated snore segmentation algorithms, especially in the presence of background noise. In this paper, we propose a fundamentally novel approach based on artificial neural network (ANN) technology to detect SBEs. Working on clinical data, we show that the proposed method can detect SBE at a sensitivity and specificity exceeding 0.892 and 0.874 respectively, even when the signal is completely buried in background noise (SNR < 0 dB). We compare the performance of the proposed technology with those of the existing methods (short-term energy, zero-crossing rates) and illustrate that the proposed method vastly outperforms conventional techniques.

UI MeSH Term Description Entries
D012119 Respiration The act of breathing with the LUNGS, consisting of INHALATION, or the taking into the lungs of the ambient air, and of EXHALATION, or the expelling of the modified air which contains more CARBON DIOXIDE than the air taken in (Blakiston's Gould Medical Dictionary, 4th ed.). This does not include tissue respiration ( Breathing
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D012372 ROC Curve A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli. ROC Analysis,Receiver Operating Characteristic,Analysis, ROC,Analyses, ROC,Characteristic, Receiver Operating,Characteristics, Receiver Operating,Curve, ROC,Curves, ROC,ROC Analyses,ROC Curves,Receiver Operating Characteristics
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
D012913 Snoring Rough, noisy breathing during sleep, due to vibration of the uvula and soft palate. Snorings
D013016 Sound A type of non-ionizing radiation in which energy is transmitted through solid, liquid, or gas as compression waves. Sound (acoustic or sonic) radiation with frequencies above the audible range is classified as ultrasonic. Sound radiation below the audible range is classified as infrasonic. Acoustic Waves,Elastic Waves,Sonic Radiation,Sound Waves,Acoustic Wave,Elastic Wave,Radiation, Sonic,Radiations, Sonic,Sonic Radiations,Sound Wave,Sounds,Wave, Acoustic,Wave, Elastic,Wave, Sound,Waves, Acoustic,Waves, Elastic,Waves, Sound
D016208 Databases, Factual Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references. Databanks, Factual,Data Banks, Factual,Data Bases, Factual,Data Bank, Factual,Data Base, Factual,Databank, Factual,Database, Factual,Factual Data Bank,Factual Data Banks,Factual Data Base,Factual Data Bases,Factual Databank,Factual Databanks,Factual Database,Factual Databases
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
D020181 Sleep Apnea, Obstructive A disorder characterized by recurrent apneas during sleep despite persistent respiratory efforts. It is due to upper airway obstruction. The respiratory pauses may induce HYPERCAPNIA or HYPOXIA. Cardiac arrhythmias and elevation of systemic and pulmonary arterial pressures may occur. Frequent partial arousals occur throughout sleep, resulting in relative SLEEP DEPRIVATION and daytime tiredness. Associated conditions include OBESITY; ACROMEGALY; MYXEDEMA; micrognathia; MYOTONIC DYSTROPHY; adenotonsilar dystrophy; and NEUROMUSCULAR DISEASES. (From Adams et al., Principles of Neurology, 6th ed, p395) Obstructive Sleep Apnea,Upper Airway Resistance Sleep Apnea Syndrome,Apnea, Obstructive Sleep,OSAHS,Obstructive Sleep Apnea Syndrome,Sleep Apnea Hypopnea Syndrome,Sleep Apnea Syndrome, Obstructive,Syndrome, Obstructive Sleep Apnea,Syndrome, Sleep Apnea, Obstructive,Syndrome, Upper Airway Resistance, Sleep Apnea,Apneas, Obstructive Sleep,Obstructive Sleep Apneas,Sleep Apneas, Obstructive

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