Yeragani Vikram Kumar, Radhakrishna Rao K A, Tancer Manuel, Uhde Thomas
Department of Psychiatry, Wayne State University School of Medicine, Detroit, Mich, USA.
Neuropsychobiology. 2002;46(3):111-20. doi: 10.1159/000066388.
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.
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.
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.
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.
先前已有研究使用时域测量方法报道过惊恐障碍患者存在呼吸不规律的情况。然而,呼吸信号并非完全呈线性,此前少数研究使用了近似熵(APEN),这是一种衡量时间序列规律性的指标。我们一直在详细研究APEN以及其他非线性测量方法,包括心率时间序列的混沌测量指标——最大Lyapunov指数(LLE)。在本研究中,除了传统的呼吸频率和潮气量时域测量方法外,我们还使用这些呼吸测量指标对正常对照组(n = 18)和惊恐障碍患者(n = 22)进行了比较。
通过Respitrace系统使用胸部和腹部束带来获取呼吸信号,该信号以500 Hz的频率进行数字化处理。随后,由于该信号的最高频率限制为0.5 Hz,因此以4 Hz构建时间序列。我们在正常呼吸以及12次/分钟的控制呼吸条件下,采集了仰卧位和站立位时256秒的数据(1024个点)。
在正常呼吸和控制呼吸时,患者站立位的APEN均显著更高(分别为p = 0.002和0.02)。在正常呼吸时,患者站立位的LLE也显著更高(p = 0.009)。同样,患者组潮气量(TV)的标准差和变异系数(COV)等时域测量指标也显著更高(分别为p = 0.02和0.004)。患者组站立位时叹息频率也更高(p = 0.02)。在站立位时,LLE(p < 0.05)以及APEN(p < 0.01)对两组的区分贡献显著,超过了线性测量指标,即TV的COV。
这些发现支持了先前关于惊恐障碍患者呼吸不规律的描述,同时也说明了诸如APEN和LLE等非线性测量方法在更好地理解类似患者群体呼吸生理异常方面的作用,因为LLE、APEN与一些时域测量指标之间的相关性仅解释了高达50 - 60%的变异。