Department of Electronic and Electrical Engineering, Yonsei University, Seoul, Korea.
Biomed Eng Online. 2011 Jun 27;10:56. doi: 10.1186/1475-925X-10-56.
Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia.
In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm.
A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%.
The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.
在过去几十年中，已经进行了许多关于心跳分类算法的研究。然而，由于生物信号在个体之间存在大量变化，许多算法也被研究以获得稳健的性能。已经提出了各种方法来减少来自个人特征的差异，但这些方法会扩大由心律失常引起的差异。
在本文中，提出了一种使用适用于个体的专用小波的心律失常分类算法。我们使用专用小波来减少性能变化，就像在个体的心电图形态中一样。所提出的算法利用形态滤波和具有专用小波的连续小波变换。主成分分析和线性判别分析用于压缩专用小波变换的形态数据。在提出的算法中，使用极限学习机作为分类器。
使用 MIT-BIH 心律失常数据库进行了性能评估。结果表明，敏感性为 97.51%，特异性为 85.07%，准确性为 97.94%，阳性预测值为 97.26%。
与其他最先进的算法相比，该算法在训练和评估数据集之间没有个体间差异，准确性更高。它还显著减少了医生所需的干预量。
