Nonlinear measures of heart period variability: Decreased measures of symbolic dynamics in patients with panic disorder

Abstract

Time series of heart period are not linear and recent studies illustrated the importance of using nonlinear methods to quantify the complexity of these time series. We compared different techniques to quantify the nonlinear complexity of these time series in patients with panic disorder and normal controls and correlated these measures with spectral powers in different bands of interest. Twenty‐four hour ECG was recorded in 23 normal controls and 29 patients with panic disorder by using Holter records. Time series of heart period were analyzed by using approximate entropies, slopes of 1/f scaling, two algorithms to calculate fractal dimension, and word sequences using symbolic dynamics. Measures using symbolic dynamics, especially word count (WC‐100), showed highly significant differences between the two groups similar to some of the frequency domain (spectral) measures, while the other techniques were relatively ineffective to distinguish between the two groups. Different nonlinear techniques may relate to different aspects of nonlinear complexity of the time series. These nonlinear techniques were also not uniform in showing the differences between awake and sleep periods. Some correlate with the measures of respiratory sinus arrhythmia and some measures obtained from symbolic dynamics may reflect not only the nonlinear complexity of the time series but also the total variability in the 24 hr HP time series, especially power in the ultra‐low frequency band (<0.0033 Hz). However, word count (WC‐100) had only weak correlations with other measures and discriminated best between the two groups and showed that this nonlinear measure was of additional value to the linear measures in classifying the two groups. Depression and Anxiety 12:67–77, 2000. © 2000 Wiley‐Liss, Inc.

Nonlinear broad band dynamics are less complex in major depression

“…Most importantly, the power law slope has been shown to be predictive for cardiac mortality in other diseases. Additionally, the power law slope was not altered in patients with panic disorders, suggesting its specificity for depression (52). Therefore, long-term prospective studies in depressed patients with large group sizes are needed to investigate the actual predictive value of this parameter for cardiac mortality.…”

QRS Amplitude of ECG in Normal Humans: Effects of Orthostatic Challenge on Linear and Nonlinear Measures of Beat-to-Beat Variability

“…The findings of increased APEN during standing posture are also important in view of the recent emphasis on the utility of nonlinear measures in addition to the classical moment statistical measures (Glenny et al, 1991;Goldberger and West, 1987;Makikallio et al, 1999;Pincus, 2000;Pincus et al, 1993Pincus et al, , 1999Voss et al, 1995;Yeragani et al, 1993bYeragani et al, , 2000aYeragani et al, , 2002aYeragani et al, , 2002bYeragani et al, , 2003.…”

Association of TMEM132D, COMT, and GABRA6 genotypes with cingulate, frontal cortex and hippocampal emotional processing in panic and major depressive disorder

“…Panic disorder (PD) is a common disabling disorder characterized by recurrent episodes of sudden and intense anxiety. Th ese episodes are accompanied by cardiorespiratory, gastrointestinal, neurological, and other autonomic symptoms, as well as subsequent worry and phobic avoidance (Dratcu 2000;Yeragani et al 2000;Preter and Klein 2008). PD has a lifetime prevalence of approximately 3 -5% (Grant et al 2006;Kessler et al 2006).…”