INTRODUCTION OF LOW TO HIGH FREQUENCIES BISPECTRUM RATE FEATURE FOR DEEPSLEEP DETECTION FROM AWAKENING BY ELECTROENCEPHALOGRAM

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MOHAMMADI EHSAN; KERMANI SAEED; AMRA BABAK

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Journal: Tehran University Medical Journal Year:2018 | Volume:76 | Issue:5 Page(s): 326-330

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Title

INTRODUCTION OF LOW TO HIGH FREQUENCIES BISPECTRUM RATE FEATURE FOR DEEPSLEEP DETECTION FROM AWAKENING BY ELECTROENCEPHALOGRAM

Author(s)

MOHAMMADI EHSAN | KERMANI SAEED | AMRA BABAK | Issue Writer Certificate

Keywords

CROSS-SECTIONAL STUDIESQ1

ENERGYQ3

SIGNAL PROCESSINGQ1

SLEEP STAGESQ1

Abstract

Background: Accurate detection of deep sleep (Due to the low frequency of the brain signalin this part of sleep, it is also called slow-wave sleep) from awakening increases the sleepstaging accuracy as an important factor in medicine. Depending on the time and cost ofmanually determining the depth of sleep, we can automatically determine the depth of sleepby electroencephalogram (EEG) SIGNAL PROCESSING. In this paper a new EEG bispectrumbased feature is introduced for deep sleep discrimination. Methods: This cross-sectional study was conducted at Isfahan University of MedicalSciences, Faculty of Advanced Technologies in Medicine, from February to October 2017. In this study a gray scale image was made of electroencephalogram bispectrum amounts andconverted to binary image with Otsu’ s Thresholding. Then the ratio of white bits in theabove of the secondary diagonal to white bits in the down of secondary diagonal (low tohigh frequencies bispectrum rate) is extracted as a new feature. This feature is an effectivemethod for detecting deep sleep from awakening. Results: One of the important methods in biomedical SIGNAL PROCESSING is the use of the powerspectrum or signal ENERGY. In sleep studies, ENERGY-related features have also been used todetermine the depth of sleep. Low to high frequencies bispectrum rate is able to separate deepsleep from awakening by accuracy of 99. 50 percent, while ENERGY-based features as one of themost important approaches to sleep classification do not have this ability. Conclusion: In this study we show that “ Low to high frequencies bispectrum rate" featurehas this capability to use in sleep staging. It is not used in previous works. The accuracyobtained in deep sleep separation from the awakening with the introduced feature (99. 50percent) is greater than the accuracy obtained by all the ENERGY-based features (Thesimultaneous use of the 6 bands ENERGY leads to 99. 42 percent accuracy). This featureindicates the ratio of the phase coupling at low frequencies to high frequencies and can beused in all cases where the bispectrum is used (such as determining the depth of anesthesia).

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APA: Copy

MOHAMMADI, EHSAN, KERMANI, SAEED, & AMRA, BABAK. (2018). INTRODUCTION OF LOW TO HIGH FREQUENCIES BISPECTRUM RATE FEATURE FOR DEEPSLEEP DETECTION FROM AWAKENING BY ELECTROENCEPHALOGRAM. TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ), 76(5 ), 326-330. SID. https://sid.ir/paper/38219/en

Vancouver: Copy

MOHAMMADI EHSAN, KERMANI SAEED, AMRA BABAK. INTRODUCTION OF LOW TO HIGH FREQUENCIES BISPECTRUM RATE FEATURE FOR DEEPSLEEP DETECTION FROM AWAKENING BY ELECTROENCEPHALOGRAM. TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ)[Internet]. 2018;76(5 ):326-330. Available from: https://sid.ir/paper/38219/en

IEEE: Copy

EHSAN MOHAMMADI, SAEED KERMANI, and BABAK AMRA, “INTRODUCTION OF LOW TO HIGH FREQUENCIES BISPECTRUM RATE FEATURE FOR DEEPSLEEP DETECTION FROM AWAKENING BY ELECTROENCEPHALOGRAM,” TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ), vol. 76, no. 5 , pp. 326–330, 2018, [Online]. Available: https://sid.ir/paper/38219/en

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