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使用多延迟音调熵分析识别糖尿病心肌梗死后患者的风险增加情况。

Identifying increased risk of post-infarct people with diabetes using multi-lag Tone-Entropy analysis.

作者信息

Karmakar Chandan, Jelinek Herbert, Khandoker Ahsan, Tulppo Mikko, Makikallio Timo, Kiviniemi Antti, Huikuri Heikki, Palaniswami Marimuthu

机构信息

Electrical & Electronic Engineering Department, University of Melbourne, Parkville, Melbourne, VIC 3010, Australia.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:25-8. doi: 10.1109/EMBC.2012.6345862.

Abstract

Diabetes mellitus is associated with multi-organ system dysfunction. One of the key causative factors is the increased blood sugar level that leads to an increase in free radical activity and organ damage including the cardiovascular and nervous system. Heart rhythm is extrinsically modulated by the autonomic nervous system and cardiac autonomic neuropathy or dysautonomia has been shown to lead to sudden cardiac death in people with diabetes due to the decrease in heart rate variability (HRV). Current algorithms for determining HRV describe only beat-to-beat variation and therefore do not consider the ability of a heart beat to influence a train of succeeding beats. Therefore mortality risk analysis based on HRV has often not been able to discern the presence of an increased risk. This study used a novel innovation of the tone-entropy algorithm by incorporating increased lag intervals and found that both the sympatho-vagal balance and total activity changed at larger lag intervals. Tone-Entropy was found to be better risk identifier of cardiac mortality in people with diabetes at lags higher than one and best at lag seven.

摘要

糖尿病与多器官系统功能障碍相关。关键致病因素之一是血糖水平升高,这会导致自由基活性增加以及包括心血管和神经系统在内的器官损伤。心律由自主神经系统进行外在调节,并且已表明心脏自主神经病变或自主神经功能障碍会导致糖尿病患者因心率变异性(HRV)降低而发生心源性猝死。当前用于确定HRV的算法仅描述逐搏变化,因此未考虑心跳影响一系列后续心跳的能力。因此,基于HRV的死亡风险分析往往无法识别风险增加的情况。本研究通过纳入增加的滞后间隔对音调熵算法进行了一项新颖创新,发现交感 - 迷走神经平衡和总活动在较大滞后间隔时均发生变化。研究发现,在滞后大于1时,音调熵是糖尿病患者心脏死亡更好的风险识别指标,在滞后为7时最佳。

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