An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks.
Epilepsy is one of the most common neurological disorders which is characterized by the spontaneous and unforeseeable occurrence of seizures. An automatic prediction of seizure can protect the patients from accidents and save their life. In this article, we proposed a mobile-based framework that aut...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 11; pp. 1 - 19 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2016
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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=118120060&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118120060 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Nov2016 vid: 40 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 118120060 118120060 118120060 10.1007/s10916-016-0579-1 118120060 ppf: 1 ppct: 18 formats: fmt: @attributes: type: P tig: atl: An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks. aug: au: Sareen, Sanjay Sood, Sandeep Gupta, Sunil affil: Department of Computer Science and Engineering , Guru Nanak Dev University , Regional Campus Gurdaspur India sug: subj: Seizures Forecasting Cloud Computing Electroencephalography Telehealth Human Experimental Studies Mobile Applications Wearable Sensors Algorithms Geographic Information Systems Data Analysis Software ab: Epilepsy is one of the most common neurological disorders which is characterized by the spontaneous and unforeseeable occurrence of seizures. An automatic prediction of seizure can protect the patients from accidents and save their life. In this article, we proposed a mobile-based framework that automatically predict seizures using the information contained in electroencephalography (EEG) signals. The wireless sensor technology is used to capture the EEG signals of patients. The cloud-based services are used to collect and analyze the EEG data from the patient's mobile phone. The features from the EEG signal are extracted using the fast Walsh-Hadamard transform (FWHT). The Higher Order Spectral Analysis (HOSA) is applied to FWHT coefficients in order to select the features set relevant to normal, preictal and ictal states of seizure. We subsequently exploit the selected features as input to a k-means classifier to detect epileptic seizure states in a reasonable time. The performance of the proposed model is tested on Amazon EC2 cloud and compared in terms of execution time and accuracy. The findings show that with selected HOS based features, we were able to achieve a classification accuracy of 94.6 %. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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