Department of Computer Science & Information Technology, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan.
Department of Sociology & Rural Development, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan.
PLoS One. 2020 Dec 17;15(12):e0243441. doi: 10.1371/journal.pone.0243441. eCollection 2020.
Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy.
加速度变化指数 (ACI) 是一种快速且易于理解的心率变异性 (HRV) 分析方法，用于评估神经系统的心脏自主控制。神经系统的心脏自主控制是在多个时间尺度上运行的高度集成系统的一个例子。传统的基于单尺度的 ACI 没有考虑多个时间尺度，并且对正常和病理受试者的分类能力有限。在这项研究中，通过结合多个时间尺度，提出了一种新的多尺度 ACI (MACI) 方法，以提高 ACI 的分类能力。我们评估了 MACI 对正常窦性节律 (NSR)、充血性心力衰竭 (CHF) 和心房颤动受试者进行分类的性能。研究结果表明，MACI 与 ACI 相比，在健康和病理受试者之间的分类提供了更好的性能。我们还将 MACI 与其他基于尺度的技术（如多尺度熵、多尺度排列熵 (MPE)、多尺度归一化校正香农熵 (MNCSE) 和多尺度排列熵 (IMPE)）进行了比较。初步结果表明，MACI 值比 IMPE 和 MNCSE 更稳定和可靠。结果表明，基于 MACI 的特征可实现更高的分类准确性。
