Multivariate functional clustering for the morphological analysis of electrocardiograph curves
Multivariate functional clustering for the morphological analysis of electrocardiograph curves
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DOI：
10.1111/j.1467-9876.2012.01062.x
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发表时间：
2013-01-01
影响因子：
1.6
通讯作者：
Vitelli, Valeria
中科院分区：
文献类型：
--
作者：
Ieva, Francesca;Paganoni, Anna M.;Vitelli, Valeria
。心血管缺血性疾病是世界范围内主要的死亡原因之一。在这类病理中，快速诊断对于再灌注治疗的良好预后至关重要。特别是，基于对远程传输的心电轨迹的统计分析的自动分类过程将非常有助于早期诊断。这项工作提供了对12导联院前心电已由生命支持人员发送到米兰118调度中心的患者的心电痕迹的分析，无论是生理的还是病理的。统计分析以预处理步骤开始，在该步骤中，从噪声观测中重建函数数据，并通过非线性配准过程去除生物变异性。然后，对重构和配准的心电及其一阶导数进行多元泛函k-均值聚类。为此，提出了一种完全基于心电形态的半自动诊断方法对心电轨迹进行分类，并对该分类方法的性能进行了评价。
. Cardiovascular ischaemic diseases are one of the main causes of death all over the world. In this class of pathologies, a quick diagnosis is essential for a good prognosis in reperfusive treatment. In particular, an automatic classification procedure based on statistical analysis of teletransmitted electrocardiograph (ECG') traces would be very helpful for an early diagnosis. This work presents an analysis of ECG traces, either physiological or pathological, of patients whose 12-lead prehospital ECG has been sent to the 118 Dispatch Center in Milan by life-support personnel. The statistical analysis starts with a preprocessing step, where functional data are reconstructed from noisy observations and biological variability is removed by a non-linear registration procedure. Then, a multivariate functional k-means clustering procedure is carried out on reconstructed and registered ECGs and their first derivatives. Hence, a new semi-automatic diagnostic procedure, based solely on the ECG morphology, is proposed to classify ECG traces; finally, the performance of this classification method is evaluated.