基于多核超限学习机的实时心电信号分析

心电分类是一种复杂的模式识别问题。目前，大部分基于不同机器学习模型的心电分类方法都取得了很高的分类精度，但学习效率不高，因此需要一种快速的心电学习方法。文章提出了基于多种核函数的超限学习方法，利用不同的核函数将特征映射到希尔伯特空间，使心电数据在高维空间中线性可分，并在 MIT-BIH 标准库进行了该方法的实验验证。与其他方法相比，文章所提出的方法具有较高的分类准确率和更快的学习速度，对临床上动态心电图的检测与分析和个性化的实时心电监测具有重要意义。

Real-Time Electrocardiogram Analysis Based on Multi-Kernel Extreme Learning Machine

Electrocardiogram(ECG) classification is a complex pattern recognition problem. At present, most of the ECG classification methods based on different machine learning model had achieved a high classification accuracy, but the learning efficiency was low. Therefore, a fast ECG learning algorithm was necessary. In this paper, a method of extreme learning machine was presented, which mapped the original feature space into Hilbert space with different kernel functions and made the ECG date in high dimensional space linearly separable. At last, the experimental verification was carried on MIT-BIH standard library. The results show that the proposed method has higher accuracy and faster learning speed than existing methods, which may be a potential tool for detection and analysis of clinical dynamic electrocardiogram and personalized real-time ECG monitoring.