Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification
Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification
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DOI：
10.3390/a13040075
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发表时间：
2020-04-01
期刊：
影响因子：
2.3
通讯作者：
Agelli, Maurizio
中科院分区：
文献类型：
--
作者：
Saenz-Cogollo, Jose Francisco;Agelli, Maurizio
在自动心跳分类系统的开发中，寻找特征和分类器的最佳组合仍然是一个悬而未决的问题，特别是在考虑涉及资源受限设备的应用时。在这篇文章中，提出了一项新的研究，即在遵循医疗器械促进协会(AAMI)的建议和患者之间的数据集划分的同时，选择信息特征和使用随机森林分类器。在训练集上使用基于互信息排序准则的过滤方法来选择特征。结果显示，归一化的心动过速(R-R)间期和相对于室性除极波宽度的特征(QRS波群)是考虑的因素中最具区分性的。在MIT-BIH心律失常数据库上获得的最好结果是，对正常心动过速、室上性异位搏动和室性异位搏动的分类总体准确率为96.14%，F1评分分别为97.97%、73.06%和90.85%。与在类似约束下测试的其他最先进的方法相比，该工作代表了迄今为止报告的最高性能之一，同时依赖于非常小的特征向量。
Finding an optimal combination of features and classifier is still an open problem in the development of automatic heartbeat classification systems, especially when applications that involve resource-constrained devices are considered. In this paper, a novel study of the selection of informative features and the use of a random forest classifier while following the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) and an inter-patient division of datasets is presented. Features were selected using a filter method based on the mutual information ranking criterion on the training set. Results showed that normalized beat-to-beat (R-R) intervals and features relative to the width of the ventricular depolarization waves (QRS complex) are the most discriminative among those considered. The best results achieved on the MIT-BIH Arrhythmia Database were an overall accuracy of 96.14% and F1-scores of 97.97%, 73.06%, and 90.85% in the classification of normal beats, supraventricular ectopic beats, and ventricular ectopic beats, respectively. In comparison with other state-of-the-art approaches tested under similar constraints, this work represents one of the highest performances reported to date while relying on a very small feature vector.