Spectral Heart Rate Variability analysis using the heart timing signal for the screening of the Sleep Apnea-Hypopnea Syndrome

Comput Biol Med. 2016 Apr 1:71:14-23. doi: 10.1016/j.compbiomed.2016.01.023. Epub 2016 Feb 2.

Abstract

Some approaches have been published in the past using Heart Rate Variability (HRV) spectral features for the screening of Sleep Apnea-Hypopnea Syndrome (SAHS) patients. However there is a big variability among these methods regarding the selection of the source signal and the specific spectral components relevant to the analysis. In this study we investigate the use of the Heart Timing (HT) as the source signal in comparison to the classical approaches of Heart Rate (HR) and Heart Period (HP). This signal has the theoretical advantage of being optimal under the Integral Pulse Frequency Modulation (IPFM) model assumption. Only spectral bands defined as standard for the study of HRV are considered, and for each method the so-called LF/HF and VLFn features are derived. A comparative statistical analysis between the different resulting methods is performed, and subject classification is investigated by means of ROC analysis and a Naïve-Bayes classifier. The standard Apnea-ECG database is used for validation purposes. Our results show statistical differences between SAHS patients and controls for all the derived features. In the subject classification task the best performance in the testing set was obtained using the LF/HF ratio derived from the HR signal (Area under ROC curve=0.88). Only slight differences are obtained due to the effect of changing the source signal. The impact of using the HT signal in this domain is therefore limited, and has not shown relevant differences with respect to the use of the classical approaches of HR or HP.

Keywords: Heart Rate Variability; Heart timing signal; IPFM model; Sleep Apnea–Hypopnea Syndrome; Spectral analysis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Electrocardiography / methods*
  • Female
  • Heart Rate*
  • Humans
  • Male
  • Signal Processing, Computer-Assisted*
  • Sleep Apnea, Obstructive / physiopathology*