Ardashev Andrey, Loskutov Alexander, Passman Rod, Zhelyakov Evgeny, Rytkin Eric, Efimov Igor
Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
, 303 E Superior Street, SQBRC Bldg, Room 11-532, Chicago, IL, 60611, USA.
Cardiovasc Eng Technol. 2025 Apr;16(2):190-201. doi: 10.1007/s13239-024-00766-7. Epub 2025 Jan 6.
This study explores the use of heart rate variability (HRV) analysis, a noninvasive technique for assessing the autonomic nervous system, by applying nonlinear dynamics and chaos theory to detect chaotic behavior in RR intervals and assess cardiovascular health.
Employing the "System Analysis of Heart Rate Dynamics" (SADR) program, this research combines chaos analysis with the short-time Fourier transform to assess nonlinear dynamic parameters in HRV. It includes constructing phase portraits in Takens space and calculating measures of chaos to identify deterministic chaos indicators.
The analysis identifies distinct chaos indicators in the cardiac rhythm of healthy volunteers compared to tachyarrhythmia patients, both before and after catheter treatment. Post-radiofrequency ablation (RFA) analysis shows promise as a predictive tool for arrhythmia recurrence.
The findings suggest that HRV analysis, through nonlinear dynamics, can be an effective noninvasive method for predicting arrhythmia recurrence following treatments like catheter ablation. This approach has the potential for early and precise detection of arrhythmia, pending further validation.
本研究通过应用非线性动力学和混沌理论,探索心率变异性(HRV)分析的应用,这是一种用于评估自主神经系统的非侵入性技术,以检测RR间期的混沌行为并评估心血管健康状况。
本研究采用“心率动力学系统分析”(SADR)程序,将混沌分析与短时傅里叶变换相结合,以评估HRV中的非线性动力学参数。这包括在Takens空间中构建相图并计算混沌度量,以识别确定性混沌指标。
与快速心律失常患者相比,该分析在健康志愿者的心律中识别出了不同的混沌指标,无论是在导管治疗前后。射频消融(RFA)术后分析显示有望作为心律失常复发的预测工具。
研究结果表明,通过非线性动力学进行的HRV分析可以成为预测导管消融等治疗后心律失常复发的有效非侵入性方法。这种方法有潜力早期精确检测心律失常,有待进一步验证。