Exploring total cardiac variability in healthy and pathophysiological subjects using improved refined multiscale entropy.

Multiscale entropy (MSE) and refined multiscale entropy (RMSE) techniques are being widely used to evaluate the complexity of a time series across multiple time scales 't'. Both these techniques, at certain time scales (sometimes for the entire time scales, in the case of RMSE), assign higher entrop...

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Publicado en:Medical & Biological Engineering & Computing Vol. 55; no. 2; pp. 191 - 206
Autores principales: Marwaha, Puneeta, Sunkaria, Ramesh, Sunkaria, Ramesh Kumar
Formato: Journal Article
Publicado: Springer Nature Feb2017
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Exploring total cardiac variability in healthy and pathophysiological subjects using improved refined multiscale entropy.
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          Marwaha, Puneeta
          Sunkaria, Ramesh
          Sunkaria, Ramesh Kumar
        affil: Department of Electronics and Communication Engineering , Dr. B. R. Ambedkar National Institute of Technology , Jalandhar 144011 India
      sug:
        subj:
          Signal Processing, Computer Assisted
          Heart Diseases Physiopathology
          Electrocardiography Methods
          Data Analysis, Statistical
          Female
          Physics
          Adult
          Middle Age
          Heart Failure Physiopathology
          Male
          Analysis of Variance
          Death, Sudden, Cardiac
          Atrial Fibrillation Physiopathology
          Resource Databases
          Age Factors
          Aged
          Clinical Assessment Tools
          Scales
          Short Portable Mental Status Questionnaire
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Aged: 65+ years
          Female
          Male
      ab: Multiscale entropy (MSE) and refined multiscale entropy (RMSE) techniques are being widely used to evaluate the complexity of a time series across multiple time scales 't'. Both these techniques, at certain time scales (sometimes for the entire time scales, in the case of RMSE), assign higher entropy to the HRV time series of certain pathologies than that of healthy subjects, and to their corresponding randomized surrogate time series. This incorrect assessment of signal complexity may be due to the fact that these techniques suffer from the following limitations: (1) threshold value 'r' is updated as a function of long-term standard deviation and hence unable to explore the short-term variability as well as substantial variability inherited in beat-to-beat fluctuations of long-term HRV time series. (2) In RMSE, entropy values assigned to different filtered scaled time series are the result of changes in variance, but do not completely reflect the real structural organization inherited in original time series. In the present work, we propose an improved RMSE (I-RMSE) technique by introducing a new procedure to set the threshold value by taking into account the period-to-period variability inherited in a signal and evaluated it on simulated and real HRV database. The proposed I-RMSE assigns higher entropy to the age-matched healthy subjects than that of patients suffering from atrial fibrillation, congestive heart failure, sudden cardiac death and diabetes mellitus, for the entire time scales. The results strongly support the reduction in complexity of HRV time series in female group, old-aged, patients suffering from severe cardiovascular and non-cardiovascular diseases, and in their corresponding surrogate time series.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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