Linear and nonlinear measures of blood pressure variability: increased chaos of blood pressure time series in patients with panic disorder.

Arterial blood pressure (BP) variability increases progressively with the development of hypertension and an increase in BP variability is associated with end organ damage and cardiovascular morbidity. On the other hand, a decrease in heart rate (HR) variability is associated with significant cardiovascular mortality. There is a strong association between cardiovascular mortality and anxiety. Several previous studies have shown decreased HR variability in patients with anxiety. In this study, we investigated beat-to-beat variability of systolic and diastolic BP (SBP and DBP) in normal controls and patients with panic disorder during normal breathing and controlled breathing at 12, and 20 breaths per minute using linear as well as nonlinear techniques. Finger BP signal was obtained noninvasively using Finapres. Standing SBPvi and DBP BPvi (log value of BP variance corrected for mean BP divided by HR variance corrected for mean HR) were significantly higher in patients compared to controls. Largest Lyapunov exponent (LLE) of SBP and DBP, a measure of chaos, was significantly higher in patients in supine as well as standing postures. The ratios of LLE (SBP/HR) and LLE (DBP/HR) were also significantly higher (P<.001) in patients compared to controls. These findings further suggest dissociation between HR and BP variability and a possible relative increase in sympathetic function in anxiety. This increase in BP variability may partly explain the increase in cardiovascular mortality in this group of patients.

Nonlinear measures of respiration: respiratory irregularity and increased chaos of respiration in patients with panic disorder.

BACKGROUND: Respiratory irregularity has been previously reported in patients with panic disorder using time domain measures. However, the respiratory signal is not entirely linear and a few previous studies used approximate entropy (APEN), a measure of regularity of time series. We have been studying APEN and other nonlinear measures including a measure of chaos, the largest Lyapunov exponent (LLE) of heart rate time series, in some detail. In this study, we used these measures of respiration to compare normal controls (n = 18) and patients with panic disorder (n = 22) in addition to the traditional time domain measures of respiratory rate and tidal volume. METHODS: Respiratory signal was obtained by the Respitrace system using a thoracic and an abdominal belt, which was digitized at 500 Hz. Later, the time series were constructed at 4 Hz, as the highest frequency in this signal is limited to 0.5 Hz. We used 256 s of data (1024 points) during supine and standing postures under normal breathing and controlled breathing at 12 breaths/min. RESULTS: APEN was significantly higher in patients in standing posture during normal as well as controlled breathing (p = 0.002 and 0.02, respectively). LLE was also significantly higher in standing posture during normal breathing (p = 0.009). Similarly, the time domain measures of standard deviations and the coefficient of variation (COV) of tidal volume (TV) were significantly higher in the patient group (p = 0.02 and 0.004, respectively). The frequency of sighs was also higher in the patient group in standing posture (p = 0.02). In standing posture, LLE (p < 0.05) as well as APEN (p < 0.01) contributed significantly toward the separation of the two groups over and beyond the linear measure, i.e. the COV of TV. CONCLUSION: These findings support the previously described respiratory irregularity in patients with panic disorder and also illustrate the utility of nonlinear measures such as APEN and LLE as additional measures toward a better understanding of the abnormalities of respiratory physiology in similar patient populations as the correlation between LLE, APEN and some of the time domain measures only explained up to 50-60% of the variation.