Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection

Maali, Yashar; Al-Jumaily, Adel

doi:10.7763/ijmlc.2012.v2.215

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…The use of fuzzy inference techniques for the detection of apneic events can also be found in other works. For example, based on the previous work of Álvarez-Estévez and Moret-Bonillo [ 64 ], Maali and Al-Jumaily have proposed the use of genetic algorithms as a mechanism to generate fuzzy rules directly from data [ 65 ]. In a recent work by Otero et al, fuzzy detection methods are also used for marking apneic intervals in the respiratory signals.…”

Section: State Of the Art In The Computer-based Diagnosis Of Sahsmentioning

confidence: 99%

Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review

Álvarez-Estévez

Moret‐Bonillo

2015

Sleep Disorders

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Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of computer-assisted diagnosis of SAHS has been performed comprising the last 15 years of research in the field. Screening approaches, methods for the detection and classification of respiratory events, comprehensive diagnostic systems, and an outline of current commercial approaches are reviewed. An overview of the different methods is presented together with validation analysis and critical discussion of the current state of the art.

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Section: State Of the Art In The Computer-based Diagnosis Of Sahsmentioning

confidence: 99%

Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review

Álvarez-Estévez

Moret‐Bonillo

2015

Sleep Disorders

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“…Thus several afford has been done to develop systems that can automatically review and analysis the recorded data [14][15][16]. Many techniques used in building the algorithms of these systems, that include Artificial Intelligent algorithms techniques like fuzzy rule based system that proposed in our previous work [17]. While proposed fuzzy rule based system achieved good results for some samples, but it still need to improve its performance in all samples of recording data.…”

Section: Introductionmentioning

confidence: 97%

Automated detecting sleep apnea syndrome: A novel system based on genetic SVM

Maali

Al-Jumaily

2011

2011 11th International Conference on Hybrid Intelligent Systems (HIS)

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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 timeconsuming. 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 biosignals as input.

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“…Therefore, many efforts have been done to develop systems that analyse recording singles automatically [13][14][15]. For this reason several Artificial Intelligent algorithms are used in this area, like as fuzzy rule based system or genetic-SVM algorithms that proposed by authors previously [16].…”

Section: Introductionmentioning

confidence: 99%

A novel partially connected cooperative parallel PSO-SVM algorithm: Study based on sleep apnea detection

Maali

Al-Jumaily

2012

2012 IEEE Congress on Evolutionary Computation

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Sleep disorders are common in a general population. It effect one in 5 adults and has several short term and long term bad side effects on health. Sleep apnea (SA) is the most important and common component of sleep disorders. This paper presents an automatic approach for detecting apnea events by using few bio-singles that are related to breathe defect. This work uses only air flow, thoracic and abdominal respiratory movement as input. The proposed algorithm consists of three main parts which are signal segmentation, feature generation and classification. A new proposed segmentation method intelligently segments the input signals for further classification, then features are generated for each segment by wavelet packet coefficients and also original signals. In classification phase a unique parallel PSO-SVM algorithm is investigated. PSO used to tune SVM parameters, and also data reduction. Proposed parallel structure used to help PSO to search space more efficiently, also avoiding fast convergence and local optimal results that are common problem in similar parallel algorithms. Obtained results demonstrate that the proposed method is effective and robust in sleep apnea detection and statistical tests on the results shown superiority of it versus previous methods even with more input signals, and also versus single PSO-SVM. Using fewer signals means more comfortable to subject and also, reduction of cost during recording the data.

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Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection

Cited by 6 publications

References 15 publications

Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review

Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review

Automated detecting sleep apnea syndrome: A novel system based on genetic SVM

A novel partially connected cooperative parallel PSO-SVM algorithm: Study based on sleep apnea detection

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