In this research paper we present designing and evaluating the electrocardiography (ECG) and Myoelectric signal (EMG) pattern recognition methods based on the adaptive machine learning. For this theoretical model to describe how the Boundary Misclassification Risk (BMR) changes along parameters including, the adaptive learning times, the adaptive learning frequencies, the generalization ability of the predictive model, and the ratio of samples without supervised information during the adaptive learning were proposed. The models are built up based on the formulated adaptive learning process of the myoelectric signal recognition, and the classification from the measured electrocardiogram (ECG) pattern. The theoretical model can be regarded as the extensions of current statistical learning theory and domain adaption theory. In the experiment, the maximum error rate (MER), and the average error rate (AER) of the RCS is employed as the approximation of the BMR. During the experiment, MER and AER change tendency matches the theoretical BMR change tendency. For different learning time interval AER is presented, from the result tendency match with the experimental and theoretical evaluated value is confirmed. Hence, the proposed theoretical model can be used for ECG and EMG pattern matching.

中文翻译：

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自适应机器学习算法采用统计信号处理对心电信号和肌电信号进行分类

在本研究论文中，我们介绍了基于自适应机器学习的心电图 (ECG) 和肌电信号 (EMG) 模式识别方法的设计和评估。对于该理论模型来描述边界误分类风险 (BMR) 如何随参数变化，包括自适应学习次数、自适应学习频率、预测模型的泛化能力以及自适应学习期间没有监督信息的样本比例。建议的。这些模型是基于已制定的肌电信号识别自适应学习过程以及来自测量的心电图 (ECG) 模式的分类建立的。该理论模型可以看作是当前统计学习理论和领域适应理论的延伸。在实验中，RCS 的最大错误率 (MER) 和平均错误率 (AER) 用作 BMR 的近似值。实验过程中，MER和AER变化趋势与理论BMR变化趋势相匹配。给出了不同学习时间间隔的AER，从结果趋势与实验和理论评估值的匹配得到证实。因此，所提出的理论模型可用于 ECG 和 EMG 模式匹配。