Proceedings ArticleDOI
Automated detecting sleep apnea syndrome: A novel system based on genetic SVM
Yashar Maali,Adel Al-Jumaily +1 more
- pp 590-594
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TLDR
A novel automatic system for detecting Apnea events by using just few of bio-signals that are related to breathe defect using only Air flow, thoracic and abdominal respiratory movement as inputs for the system.Abstract:
Sleep Apnea (SA) is one of the common symptoms and important part of sleep disorders. It has consequences that affect all daily life activities and present danger to the patient and/or others. The common diagnose procedure is based on an overnight sleep test. The test is usually including recording of several bio-signals that used to detect this syndrome. The conventional approach of detecting the sleep apnea uses a manual analysis of most of bio-signals to achieve reasonable accuracy. The manual process is highly cost and time-consuming. This paper presents a novel automatic system for detecting Apnea events by using just few of bio-signals that are related to breathe defect. This work use only Air flow, thoracic and abdominal respiratory movement as inputs for the system. The proposed technique consists of three main parts which are signal segmentation, feature generation and classification based on genetic SVM. Results show efficiency of this system and its superiority versus previous methods with more bio-signals as input.read more
Citations
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Journal ArticleDOI
A Review of Obstructive Sleep Apnea Detection Approaches
Fábio Mendonça,Sheikh Shanawaz Mostafa,Antonio G. Ravelo-García,Fernando Morgado-Dias,Thomas Penzel +4 more
TL;DR: The objective of this review is to analyze already existing algorithms that have not been implemented on hardware but have had their performance verified by at least one experiment that aims to detect obstructive sleep apnea to predict trends.
Book ChapterDOI
Sleep Apnea Event Detection from Nasal Airflow Using Convolutional Neural Networks.
TL;DR: This study proposes a new approach for the detection of apnea-hypopnea events from the raw signal data of nasal airflow using convolutional neural networks, a prominent type of deep neural networks known for their ability to automatically learn features from high dimensional data without manual feature engineering.
Book ChapterDOI
Detecting Hypopnea and Obstructive Apnea Events Using Convolutional Neural Networks on Wavelet Spectrograms of Nasal Airflow
TL;DR: The higher accuracy and the less complex architecture of the 2-D CNN show that converting biological signals into spectrograms and using them in conjunction with CNNs is a promising method for sleep apnea recognition.
Proceedings ArticleDOI
Convolutional Neural Networks on Multiple Respiratory Channels to Detect Hypopnea and Obstructive Apnea Events
TL;DR: The use of nasal airflow, thoracic and abdominal channels with a convolutional neural network was beneficial in detecting sleep apnea events, and the combined use of the three channels outperformed all single and pair combinations of channels, achieving accuracy of 83.5%, which is sufficiently high for practical applications.
Journal ArticleDOI
Data Mining for Patient Friendly Apnea Detection
Stein Kristiansen,Mari Sonsteby Hugaas,Vera Goebel,Thomas Plagemann,Konstantinos Nikolaidis,Knut Liestøl +5 more
TL;DR: It is concluded that one signal might be sufficient to detect disrupted breathing, if the data set is of sufficient quality and size, and that respiration from the abdomen is the preferable choice when considering both classification performance and patient comfort.
References
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Journal Article
Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research
W. Ward Flemons,Daniel J. Buysse,Susan Redline,A. Oack,Kingman P. Strohl,John R. Wheatley,Terry Young,Neil J. Douglas,Patrick Levy,W. McNicolas,John A. Fleetham,David P. White,W. Schmidt-Nowarra,D. Carley,J. Romaniuk +14 more
Journal ArticleDOI
Travel-time prediction with support vector regression
TL;DR: The feasibility of applying SVR in travel-time prediction is demonstrated and it is proved that SVR is applicable and performs well for traffic data analysis.
Journal ArticleDOI
Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings.
TL;DR: Assessment of the ability of an overnight ECG recording to distinguish between patients with and without apnoea and the best algorithms made use of frequency-domain features to estimate changes in heart rate and the effect of respiration on the ECG waveform.
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