A New Approach to Predict Potential Sleep Disorder based on Fractal Analysis from Non-overlapping Single Lead ECG Using Support Vector Machine

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dc.contributor.advisorPurnomo, Mauridhi Hery
dc.contributor.authorFahruzi, Iman
dc.contributor.authorPurnama, I Ketut Eddy
dc.contributor.authorYoshimoto, Kayo
dc.contributor.authorTakahashi, Hideya
dc.contributor.authorPurnomo, Mauridhi Hery
dc.date.accessioned2023-06-12T11:58:03Z
dc.date.available2023-06-12T11:58:03Z
dc.date.issued2021-01-18
dc.descriptionPaper Internasional Bereputasien_US
dc.description.abstrakSleep disorders are challenging to diagnose. The complexity of records obtained from electrocardiogram (ECG) recordings requires manual inspection by experienced medical practitioners. Meanwhile, ECG records are still widely used to diagnose heart problems during sleep. To resolve the issue, the fractal analysis is a promising means to help identify the characteristics of non-overlapping apnea and non-apnea events based on signal scaling behaviour and QRS wave morphologies. Therefore, we propose a new approach to develop automatic sleep disorder classification to minimalize visual inspection and manual scoring. We employed the monofractal and the multifractal analyses to generate new features such as alpha1, residue1, alpha2, residue2, Dqmin, Dqmax, hqmin, hqmid, hqmax, and hqmaxhqmin. To improve the proposed method's performance, we used the ten new features that have been extracted, which are eventually being used as inputs space to a support vector machine (SVM). Through examining the feature set, we designed an optimum SVM model classifier to explore the usability of patterns to predict potential sleep disorder corresponding to apnea and non-apnea events. Hence, our approach through SVM with radial basis function (RBF) kernel is achieved to have accuracy, sensitivity, specificity of 92.16%, 88.24%, 94.12% respectivelyen_US
dc.identifier.issn2185-3118
dc.identifier.urihttps://repository.polibatam.ac.id/xmlui/handle/123456789/1681
dc.language.isoenen_US
dc.publisherThe Intelligent Networks and Systems Societyen_US
dc.subjectFractal analysisen_US
dc.subjectMultifractalen_US
dc.subjectSleep disorderen_US
dc.subjectSVMen_US
dc.subjectFractal scaling behaviouren_US
dc.titleA New Approach to Predict Potential Sleep Disorder based on Fractal Analysis from Non-overlapping Single Lead ECG Using Support Vector Machineen_US
dc.typeArticleen_US

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