关键词: 心电图, 多元对应分析, 监督分类, 机器学习

Abstract: There exist plenty uncorrelated features in the high dimensional ECG data, so, it is difficult for the classifier based on supervised learning to perform well in both sensitivity and specificity. Pre-processed by baseline wander removing, high-frequency span removing and polynomial fitting, an auto heartbeat classification model is proposed based on supervised MCA dimension reducing. The sequence ECG data is discretized; supervised MCA dimension reducing technology is employed to extract the key features; the ECG data is classified with the common classifiers. The experiment on the PTB database shows, compared with supervised learning method, this approach combining with different classifiers has a better performance on both sensitivity and specificity.

Key words: electrocardiogram, multiple correspondence analysis, supervised classification, machine learning