A chaos-based visual encryption mechanism for clinical EEG signals.
In this study, we have developed a chaos-based visual encryption mechanism that can be applied for clinical electroencephalography (EEG) signals. In comparison with other types of random sequences, chaos sequences were mainly used to increase unpredictability. We used a 1D chaotic scrambler and a pe...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 47; no. 7; pp. 757 - 763 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jul2009
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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=104906630&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104906630 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2009 vid: 47 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104906630 NLM19221821 2010304519 10.1007/s11517-009-0458-8 NLM19221821 104906630 ppf: 757 ppct: 6 formats: fmt: @attributes: type: P tig: atl: A chaos-based visual encryption mechanism for clinical EEG signals. aug: au: Lin CF Chung CH Lin JH Lin, Chin-Feng Chung, Cheng-Hsing Lin, Jia-Hui affil: Department of Electrical Engineering, National Taiwan-Ocean University, Pei-Ning Road, Keelung, Taiwan, ROC sug: subj: Chaos Theory Data Security Electroencephalography Human Information Retrieval Methods ab: In this study, we have developed a chaos-based visual encryption mechanism that can be applied for clinical electroencephalography (EEG) signals. In comparison with other types of random sequences, chaos sequences were mainly used to increase unpredictability. We used a 1D chaotic scrambler and a permutation scheme to achieve EEG visual encryption. One approach of realizing the visual encryption mechanism is to scramble the signal values of the input EEG signal by multiplying a 1D chaotic signal to randomize the EEG signal values. We then applied a chaotic address scanning order encryption to the randomized reference values. Simulation results show that when the correct deciphering parameters are entered, the signal is completely recovered, and the percent root-mean-square difference (PRD) values for control and alcoholic clinical EEG signals are 4.33 x 10(-15) and 4.11 x 10(-15)%, respectively. As long as there is an input parameter error, with an initial point error of 0.00000001% as an example, thereby making these clinical EEG signals unrecoverable. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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