Arif M, Aziz W
Department of Computer & Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad.
Physiol Meas. 2005 Oct;26(5):653-65. doi: 10.1088/0967-3334/26/5/007. Epub 2005 Jun 13.
Heart rate variability (HRV) analysis is a non-invasive and reliable means to assess an autonomic nervous system (ANS) function. Heart rate is a non-stationary signal that may contain indicators of current diseases and sometimes warnings about impending diseases. In this paper, we have proposed the threshold-based acceleration change index (TACI) for HRV analysis, which is calculated from the sign of differences of RR time series characterizing the dynamics of threshold crossings. It was found that TACI is robust in classifying various groups under different physiological and pathological conditions. We have studied the behavior of TACI for simulated time series (uncorrelated random data, sinusoidal time series and logistics map time series) and its robustness in the presence of artifacts for RR time series. The performance of TACI is evaluated for classifying normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation (AF). An unpaired Student's t-test was used to check significant differences between these groups and the degree of separation between these groups was quantified by using the area of a receiver operator curve.
心率变异性（HRV）分析是评估自主神经系统（ANS）功能的一种非侵入性且可靠的方法。心率是一个非平稳信号，可能包含当前疾病的指标，有时还能对即将发生的疾病发出警告。在本文中，我们提出了用于HRV分析的基于阈值的加速度变化指数（TACI），它是根据表征阈值穿越动态的RR时间序列差异的符号来计算的。研究发现，TACI在对不同生理和病理条件下的各类群体进行分类时具有稳健性。我们研究了TACI在模拟时间序列（不相关随机数据、正弦时间序列和逻辑斯谛映射时间序列）中的行为及其在RR时间序列存在伪迹情况下的稳健性。对TACI在区分正常窦性心律（NSR）、充血性心力衰竭（CHF）和心房颤动（AF）方面的性能进行了评估。使用不成对学生t检验来检查这些组之间的显著差异，并通过使用接收者操作曲线的面积来量化这些组之间的分离程度。
