论文标题
ECG在稀疏字典上击败了快速分类基础
ECG Beats Fast Classification Base on Sparse Dictionaries
论文作者
论文摘要
特征提取在心电图（ECG）BEATS分类系统中起重要作用。与其他流行的方法相比，VQ方法在从ECG中提取功能方面表现良好，并具有降低维度的优势。在VQ方法中，训练了一组与ECG Beats段相对应的字典，并且使用VQ代码来表示每个心跳。但是，实际上，由K-均值或K-均值++优化的VQ代码存在很大的量化误差，这导致VQ代码相同类型的两个心跳非常不同。因此，不同类型的心跳之间的基本差异不能很好地代表性。另一方面，VQ在代码簿构建过程中使用了太多数据，这限制了字典学习的速度。在本文中，我们提出了一种提高VQ方法速度和准确性的新方法。为了减少代码簿构造的计算，构建了一组与ECG Beats波段相对应的稀疏字典。初始化后，稀疏字典通过功能 - 符号和拉格朗日双重算法有效地更新。基于这些词典，可以计算一组代码来表示原始的ECG节拍。经验结果表明，通过我们的方法从ECG提取的功能更有效，更可分离。我们方法的准确性比其他方法更高，而特征提取的时间更少
Feature extraction plays an important role in Electrocardiogram (ECG) Beats classification system. Compared to other popular methods, VQ method performs well in feature extraction from ECG with advantages of dimensionality reduction. In VQ method, a set of dictionaries corresponding to segments of ECG beats is trained, and VQ codes are used to represent each heartbeat. However, in practice, VQ codes optimized by k-means or k-means++ exist large quantization errors, which results in VQ codes for two heartbeats of the same type being very different. So the essential differences between different types of heartbeats cannot be representative well. On the other hand, VQ uses too much data during codebook construction, which limits the speed of dictionary learning. In this paper, we propose a new method to improve the speed and accuracy of VQ method. To reduce the computation of codebook construction, a set of sparse dictionaries corresponding to wave segments of ECG beats is constructed. After initialized, sparse dictionaries are updated efficiently by Feature-sign and Lagrange dual algorithm. Based on those dictionaries, a set of codes can be computed to represent original ECG beats.Experimental results show that features extracted from ECG by our method are more efficient and separable. The accuracy of our method is higher than other methods with less time consumption of feature extraction
