说明：  心电图是一种经胸腔的以时间为单位记录心脏的电生理活动，并通过皮肤上的电极捕捉并记录下来的诊疗技术，能警示人体心脏健康状况。我们对十种不同心脏状况的心电图（ECG）数据进行实验，在对原始数据进行了一系列预处理后，利用PCA和ICA进行特征提取降维得到新的特征空间，并利用支持向量机进行训练，最后对未分类心电数据进行分类标记并评价其性能。
(Electrocardiography (ECG) is a transthoracic interpretation of the electrical activity of the heart over a period of time, which can reflect the physical condition of a heart. We carry a series of experiments based on ten sets of different ECG data. First, we pre-process the raw data with data processing technology, and then we use the feature extraction technology of PCA and ICA to get a new feature space. At last we use support vector machine to train a model which can and finally predict and classify the unlabeled ECG data. We validate and optimize the model by evaluating its performance when training it.)