1 / 14

Introduction

Introduction. The aim of this work is investigating the differences of Heart Rate Variability (HRV) features between normal subjects and patients suffering from Congestive Heart Failure (CHF) at several levels of NYHA scale. CHF is …. Introduction.

june
Download Presentation

Introduction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Introduction The aim of this work is investigating the differences of Heart Rate Variability (HRV) features between normal subjects and patients suffering from Congestive Heart Failure (CHF) at several levels of NYHA scale. CHF is …

  2. Introduction Only a few studies have been focused on using HRV measures for diagnosis purpose in CHF and these studies proposed binary classification for identify normal and CHF patient without considering NYHA class. One study* investigate the discrimination power of long term HRV measures; the other** proposed a classifier based on short term HRV measures but does not provide any information about NYHA class. Consequently, we investigate the differences of Heart Rate Variability (HRV) features between normal subjects and patients suffering from Congestive Heart Failure (CHF) at several levels of NYHA scale. *M.H. Asyali, Discrimination power of long-term heart rate variability measures, in: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, September 17–21, 2003. **Y. Isler, and M. Kuntalp, “Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure,” Computers in Biology and Medicine, vol. 37, no. 10, pp. 1502-1510, Oct, 2007.

  3. Methods We performed a retrospective analysis of two public RR interval databases, to compare values for HRV measure in normal middle-aged subjects and in patients who suffered from chronic heart failure, with NYHA I, II and III. We calculated statistical measures from 5-minute RR interval data using standard methods. Moreover, we estimated short-term frequency domain measures using the Lomb periodogram.

  4. Methods For each selected feature and for each NYHA scale, histogram distribution has been computed. Finally, we analyzed how the correlation matrix between features of NN series changes according to the severity of CHF.

  5. Results We show the mean and standard deviation of each measure according to NYHA class.

  6. Results We show the histogram distribution of each measure according to NYHA class.

  7. Results We show the histogram distribution of each measure according to NYHA class.

  8. Results We show the histogram distribution of each measure according to NYHA class.

  9. Results We show the histogram distribution of each measure according to NYHA class.

  10. Results We show the histogram distribution of each measure according to NYHA class.

  11. Results We show the histogram distribution of each measure according to NYHA class.

  12. Results We show the histogram distribution of each measure according to NYHA class.

  13. Results Correlation between NN Measures in Normal Subjects We show the correlation matrix between HRV measures

  14. Discussion SDNN, RMSSD and TOTAL POWER has higher values in healthy subjects than in CHF patients. Moreover, VLF, LF and HF seem to be depressed in CHF patient. The other parameters, such as AVNN, did not appear to be different between normal and CHF. The correlations between some features of NN series increase according to the severity of CHF. From the shown results it is possible conclude that there is a variation in HRV features, according to NYHA classification. We suppose that this variation may drive the research of a hierarchic classifier in order to distinguish not only normal versus CHF patient but also mild versus severe CHF.

More Related