• V K Yeragani, R Nadella, B Hinze, S Yeragani, and V C Jampala.
• Wayne State University School of Medicine, Detroit, Michigan, USA. vikramyershr@yahoo.com
• Depress Anxiety. 2000 Jan 1; 12 (2): 67-77.

AbstractTime 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.

27 keywords...

hide…