The feasibility of atrial and ventricular arrhythmias recognition using metrics of signal complexity for heartbeat intervals
The feasibility of atrial and ventricular arrhythmias recognition using metrics of signal complexity for heartbeat intervals
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使用心跳间隔信号复杂性指标识别房性和室性心律失常的可行性
DOI：
10.1101/612002
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Shigehiko Kanaya
中科院分区：
文献类型：
--
作者：
Ming Huang;Koshiro Kido;Naoaki Ono;Md Altaf-UI-Amin;Toshiyo Tamura;Shigehiko Kanaya
众所周知，心脏系统是由复杂的非线性自我调节控制的，心率变异性（HRV）是自主调节的一个独立指标。假设心房颤动(A)和室性异位心律失常(V)的内在差异可以通过基于信号复杂性的适当方法揭示，我们研究了使用心跳间隔（HRI）复杂性度量来检测这些不同病理来源的心律失常的可行性。具体来说，正常窦性心律(N)、A型和V型被用作目标心跳类型。通过从不同长度的HRI中提取基于熵的特征，即从300次心跳到1000次心跳，我们检验了这3种类型心跳的可区分性。将这些特征应用到随机森林模型中，可以利用600心跳长度的HRI信号完全检测出A和V，即100%的类型查全率和准确率。更重要的是，这种方法对相应心律失常的存在很敏感。结果证实了我们关于A型和V型的内在差异的假设。进一步的研究将这种方法应用于更广泛的频谱和更精细的心律失常/心脏病分层，并可能导致在复杂性背景下的系统理解，并更好地了解其在可穿戴/无约束监测中的实际应用。
It is well known that the cardiac system is controlled by the complex nonlinear self-regulation and the heartbeat variability (HRV) is an independent indicator of the autonomic regulation. With the assumption that intrinsic differences of atrial fibrillation (A) and ventricular ectopic arrhythmias (V) can be unveiled by a proper approach based on of signal complexity, we examine the feasibility of detecting these arrhythmias of different pathological origins using metrics of complexity for heartbeat intervals (HRI). Specifically, the normal sinus rhythm (N), the A type and the V type are used as the targeted types of heartbeat. By extracting the entropy-based features from HRI of different lengths, i.e., from 300 heartbeats to 1000 heartbeats, we examined the distinguishability of these 3 types of heartbeat. By applying the features to the random forest model, the HRI signal of 600-heartbeat-length can be used to detect the A and V completely, i.e., with 100% of type-wise recall and precision. What is more, this approach is sensitive to the existence of the corresponding arrhythmias. The results substantiate our assumption about the intrinsic difference of the A and V type. A further investigation applying this approach to a wider spectrum and a finer stratification of arrhythmias/ cardiac diseases and may lead to the systematic understanding in the context of complexity and better insight for its practical use for wearable/unconstrained monitors.
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