Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic.
Institute of Scientific Instruments, The Czech Academy of Sciences, Královopolská 147, Brno, 612 64, Czech Republic.
Sci Rep. 2017 Sep 11;7(1):11239. doi: 10.1038/s41598-017-10942-6.
Accurate detection of cardiac pathological events is an important part of electrocardiogram (ECG) evaluation and subsequent correct treatment of the patient. The paper introduces the results of a complex study, where various aspects of automatic classification of various heartbeat types have been addressed. Particularly, non-ischemic, ischemic (of two different grades) and subsequent ventricular premature beats were classified in this combination for the first time. ECGs recorded in rabbit isolated hearts under non-ischemic and ischemic conditions were used for analysis. Various morphological and spectral features (both commonly used and newly proposed) as well as classification models were tested on the same data set. It was found that: a) morphological features are generally more suitable than spectral ones; b) successful results (accuracy up to 98.3% and 96.2% for morphological and spectral features, respectively) can be achieved using features calculated without time-consuming delineation of QRS-T segment; c) use of reduced number of features (3 to 14 features) for model training allows achieving similar or even better performance as compared to the whole feature sets (10 to 29 features); d) k-nearest neighbours and support vector machine seem to be the most appropriate models (accuracy up to 98.6% and 93.5%, respectively).
准确检测心脏病理事件是心电图（ECG）评估和后续正确治疗患者的重要组成部分。本文介绍了一项复杂研究的结果，其中涉及到自动分类各种心跳类型的各个方面。特别是，首次在这种组合中对非缺血性、缺血性（两种不同等级）和随后的室性早搏进行了分类。在非缺血和缺血条件下记录的兔离体心脏的心电图用于分析。在同一数据集上测试了各种形态和频谱特征（包括常用和新提出的特征）以及分类模型。结果发现：a）形态特征通常比频谱特征更适合；b）使用无需耗时勾画 QRS-T 段即可计算的特征，可以获得成功的结果（形态和频谱特征的准确率分别高达 98.3%和 96.2%）；c）使用较少数量的特征（3 到 14 个特征）进行模型训练可以实现与全特征集（10 到 29 个特征）相似甚至更好的性能；d）k-最近邻和支持向量机似乎是最合适的模型（准确率分别高达 98.6%和 93.5%）。
