Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators
Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators
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DOI：
10.1016/j.cmpb.2007.09.005
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发表时间：
2007-12-01
影响因子：
6.1
通讯作者：
Dreyfus, Gerard
中科院分区：
文献类型：
--
作者：
Dubois, Remi;Maison-Blanche, Pierre;Dreyfus, Gerard
本文将一种新的机器学习算法--广义正交正向回归算法与一种特殊的参数函数--高斯台面函数相结合，实现了心电信号P、Q、R、S和T波的自动提取。GOFR将心跳信号分解成高斯梅萨函数，这样每个波都由一个GMF建模；这样产生的模型很容易被医生解释。GOFR是经过一些简单的预处理后定位R波，提取每个心跳的特征形状，通过自动分类分配P，Q，R，S和T标签，区分正常心搏(NB)和异常心跳(AB)，并提取用于诊断的特征的全局过程中的重要组成部分。在MIT和AHA数据库上评估了QRS波群的检测效率，以及NB和AB的区分效率；在QTDB数据库上验证了P波和T波的标记。(C)2007爱思唯尔爱尔兰有限公司。保留所有权利。
This paper describes the automatic extraction of the P, Q, R, S and T waves of electrocardiographic recordings (ECGs), through the combined use of a new machine-learning algorithm termed generalized orthogonal forward regression (GOFR) and of a specific parameterized function termed Gaussian mesa function (GMF). GOFR breaks up the heartbeat signal into Gaussian mesa functions, in such a way that each wave is modeled by a single GMF; the model thus generated is easily interpretable by the physician. GOFR is an essential ingredient in a global procedure that locates the R wave after some simple pre-processing, extracts the characteristic shape of each heart beat, assigns P, Q, R, S and T labels through automatic classification, discriminates normal beats (NB) from abnormal beats (AB), and extracts features for diagnosis. The efficiency of the detection of the QRS complex, and of the discrimination of NB from AB, is assessed on the MIT and AHA databases; the labeling of the P and T wave is validated on the QTDB database. (C) 2007 Elsevier Ireland Ltd. All rights reserved.