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RISS 인기검색어
- 검색키워드
- 저자 : Danilo P. Mandic (검색결과 2 건)
- 무료
- 기관 내 무료
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Kim, Youngjoo,Ryu, Jiwoo,Kim, Ko Keun,Took, Clive C.,Mandic, Danilo P.,Park, Cheolsoo Hindawi Publishing Corporation 2016 Computational intelligence and neuroscience Vol.2016 No.-
<P>Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP) algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.</P>
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A physiology based model of heart rate variability
Wilhelm von Rosenberg,Marc-Oscar Hoting,Danilo P. Mandic 대한의용생체공학회 2019 Biomedical Engineering Letters (BMEL) Vol.9 No.4
Heart rate variability (HRV) is governed by the autonomic nervous system (ANS) and is routinely used to estimate thestate of body and mind. At the same time, recorded HRV features can vary substantially between people. A model for HRVthat (1) correctly simulates observed HRV, (2) reliably functions for multiple scenarios, and (3) can be personalised usinga manageable set of parameters, would be a signifi cant step forward toward understanding individual responses to externalinfl uences, such as physical and physiological stress. Current HRV models attempt to reproduce HRV characteristics bymimicking the statistical properties of measured HRV signals. The model presented here for the simulation of HRV followsa radically diff erent approach, as it is based on an approximation of the physiology behind the triggering of a heart beat andthe biophysics mechanisms of how the triggering process—and thereby the HRV—is governed by the ANS. The model takesinto account the metabolisation rates of neurotransmitters and the change in membrane potential depending on transmitterand ion concentrations. It produces an HRV time series that not only exhibits the features observed in real data, but alsoexplains a reduction of low frequency band-power for physically or psychologically high intensity scenarios. Furthermore,the proposed model enables the personalisation of input parameters to the physiology of diff erent people, a unique featurenot present in existing methods. All these aspects are crucial for the understanding and application of future wearable health.
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