alexa A Within-Subject Normal-Mixture Model with Mixed-Effects for Analyzing Heart Rate Variability
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

A Within-Subject Normal-Mixture Model with Mixed-Effects for Analyzing Heart Rate Variability

Jessica M Ketchum1*, Al M Best2 and Viswanathan Ramakrishnan3

1Virginia Commonwealth University, Assistant Professor, Department of Biostatistics, Director of Statistical Services, VCU-Center for Rehabilitation Science and Engineering, USA

2Virginia Commonwealth University, Associate Professor, School of Dentistry, USA

3Medical College of South Carolina, Professor, Division of Biostatistics and Epidemiology, USA

*Corresponding Author:
Jessica M. Ketchum
Virginia Commonwealth University
Assistant Professor
Department of Biostatistics
Director of Statistical Services
VCU-Center for Rehabilitation Science and Engineering, USA
Tel: 804-986-8219
Fax: 804-828-8900
E-mail: [email protected]

Received date: March 19, 2012; Accepted date: April 24, 2012; Published date: April 25, 2012

Citation: Ketchum JM, Best AM, Ramakrishnan V (2012) A Within-Subject Normal-Mixture Model with Mixed-Effects for Analyzing Heart Rate Variability. J Biomet Biostat S7:013.doi:10.4172/2155-6180.S7-013

Copyright: © 2012 Ketchum JM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



 Data on Heart Rate Variability (HRV) have been used extensively to indirectly assess the autonomic control of the heart. The distributions of HRV measures, such as the RR-interval, are not necessarily normally distributed and current methodology does not typically incorporate this characteristic. In this article, a mixed-effects modeling approach under the assumption of a two-component normal-mixture distribution for the within-subject observations has been proposed. Estimation of the parameters of the model was performed through an application of the EM algorithm, which is different from the traditional EM application for the normal-mixture methods. An application of this method was illustrated and the results from a simulation study were discussed. Differences among other methods were also reviewed.


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