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

Yashar Maali; Adel Al-Jumaily

doi:10.1109/HIS.2011.6122171

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

Yashar Maali, Adel Al-Jumaily

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

17 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS)
Editors	Ajith Abraham, Mohamed Kamel, Ronald Yager, Albert Zomaya, Azah Kamilah Muda, Tzung-Pei Hong, Choo Yun Huoy
Place of Publication	United States
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	590-594
Number of pages	5
ISBN (Print)	9781457721502
DOIs	https://doi.org/10.1109/HIS.2011.6122171
Publication status	Published - 2011
Externally published	Yes
Event	International Conference on Hybrid Intelligent Systems (11th : 2011) - Malacca, Malaysia Duration: 5 Dec 2011 → 8 Dec 2011

Conference

Conference	International Conference on Hybrid Intelligent Systems (11th : 2011)
City	Malacca, Malaysia
Period	5/12/11 → 8/12/11

Keywords

genetic algorithm
sleep apnea
support vector machine

Access to Document

10.1109/HIS.2011.6122171

Cite this

Maali, Y., & Al-Jumaily, A. (2011). Automated detecting sleep apnea syndrome: a novel system based on genetic SVM. In A. Abraham, M. Kamel, R. Yager, A. Zomaya, A. K. Muda, T.-P. Hong, & C. Y. Huoy (Eds.), Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS) (pp. 590-594). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/HIS.2011.6122171

Maali, Yashar ; Al-Jumaily, Adel. / Automated detecting sleep apnea syndrome : a novel system based on genetic SVM. Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS). editor / Ajith Abraham ; Mohamed Kamel ; Ronald Yager ; Albert Zomaya ; Azah Kamilah Muda ; Tzung-Pei Hong ; Choo Yun Huoy. United States : Institute of Electrical and Electronics Engineers (IEEE), 2011. pp. 590-594

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title = "Automated detecting sleep apnea syndrome: a novel system based on genetic SVM",

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.",

keywords = "genetic algorithm, sleep apnea, support vector machine",

author = "Yashar Maali and Adel Al-Jumaily",

year = "2011",

doi = "10.1109/HIS.2011.6122171",

language = "English",

isbn = "9781457721502",

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editor = "Ajith Abraham and Mohamed Kamel and Ronald Yager and Albert Zomaya and Muda, {Azah Kamilah} and Tzung-Pei Hong and Huoy, {Choo Yun}",

booktitle = "Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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note = "International Conference on Hybrid Intelligent Systems (11th : 2011) ; Conference date: 05-12-2011 Through 08-12-2011",

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Maali, Y & Al-Jumaily, A 2011, Automated detecting sleep apnea syndrome: a novel system based on genetic SVM. in A Abraham, M Kamel, R Yager, A Zomaya, AK Muda, T-P Hong & CY Huoy (eds), Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS). Institute of Electrical and Electronics Engineers (IEEE), United States, pp. 590-594, International Conference on Hybrid Intelligent Systems (11th : 2011), Malacca, Malaysia, 5/12/11. https://doi.org/10.1109/HIS.2011.6122171

Automated detecting sleep apnea syndrome: a novel system based on genetic SVM. / Maali, Yashar; Al-Jumaily, Adel.
Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS). ed. / Ajith Abraham; Mohamed Kamel; Ronald Yager; Albert Zomaya; Azah Kamilah Muda; Tzung-Pei Hong; Choo Yun Huoy. United States: Institute of Electrical and Electronics Engineers (IEEE), 2011. p. 590-594.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Automated detecting sleep apnea syndrome

T2 - International Conference on Hybrid Intelligent Systems (11th : 2011)

AU - Maali, Yashar

AU - Al-Jumaily, Adel

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - genetic algorithm

KW - sleep apnea

KW - support vector machine

U2 - 10.1109/HIS.2011.6122171

DO - 10.1109/HIS.2011.6122171

M3 - Conference proceeding contribution

SN - 9781457721502

SP - 590

EP - 594

BT - Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS)

A2 - Abraham, Ajith

A2 - Kamel, Mohamed

A2 - Yager, Ronald

A2 - Zomaya, Albert

A2 - Muda, Azah Kamilah

A2 - Hong, Tzung-Pei

A2 - Huoy, Choo Yun

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - United States

Y2 - 5 December 2011 through 8 December 2011

ER -

Maali Y, Al-Jumaily A. Automated detecting sleep apnea syndrome: a novel system based on genetic SVM. In Abraham A, Kamel M, Yager R, Zomaya A, Muda AK, Hong TP, Huoy CY, editors, Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS). United States: Institute of Electrical and Electronics Engineers (IEEE). 2011. p. 590-594 doi: 10.1109/HIS.2011.6122171

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

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Keywords

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