An Intelligent Sleep Apnea Classification System Based on EEG Signals.
Sleep Apnea is a sleep disorder which causes stop in breathing for a short duration of time that happens to human beings and animals during sleep. Electroencephalogram (EEG) plays a vital role in detecting the sleep apnea by sensing and recording the brain's activities. The EEG signal dataset is sub...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 2; pp. 1 - 2 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
Feb2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134561913&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134561913 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Feb2019 vid: 43 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134561913 134561913 134561913 10.1007/s10916-018-1146-8 134561913 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Intelligent Sleep Apnea Classification System Based on EEG Signals. aug: au: Vimala, V. Ramar, K. Ettappan, M. affil: Department of Computer Science and Engineering, Chennai Institute of Technology, Kundrathur, Chennai, Tamil Nadu, India sug: subj: Sleep Apnea Syndromes Classification Electroencephalography Utilization Signal Processing, Computer Assisted Human gamma Rays Machine Learning Neural Networks (Computer) Sensitivity and Specificity Diagnosis, Computer Assisted Artificial Intelligence Male Adult Middle Age Body Weight Validity Descriptive Statistics Adult: 19-44 years Middle Aged: 45-64 years Male ab: Sleep Apnea is a sleep disorder which causes stop in breathing for a short duration of time that happens to human beings and animals during sleep. Electroencephalogram (EEG) plays a vital role in detecting the sleep apnea by sensing and recording the brain's activities. The EEG signal dataset is subjected to filtering by using Infinite Impulse Response Butterworth Band Pass Filter and Hilbert Huang Transform. After pre-processing, the filtered EEG signal is manipulated for sub-band separation and it is fissioned into five frequency bands such as Gamma, Beta, Alpha, Theta, and Delta. This work employs features such as energy, entropy, and variance which are computed for each frequency band obtained from the decomposed EEG signals. The selected features are imported for the classification process by using machine learning classifiers including Support Vector Machine (SVM) with Kernel Functions, K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). The performance measures such as accuracy, sensitivity, and specificity are computed and analyzed for each classifier and it is inferred that the Support Vector Machine based classification of sleep apnea produces promising results. pubtype: Academic Journal doctype: equations & formulas research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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