摘要:在具有挑战性的临床心电图数据中自动检测心室去极化的开始

2019 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) Pub Date : 2019-09-01 DOI:10.1109/CHASE48038.2019.00010

Christopher I. Baek, Kanav Saraf, M. Wasko, Xu Zhang, Yi-yong Zheng, P. Borgström, A. Mahajan, W. Kaiser

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引用次数: 0

摘要

本文提出了一种自动检测心电图心室去极化起始的新方法。为了适应在潜在噪声的ECG信号中高度可变的ECG形态，计算了与心室去极化发作一致的因素的加权组合。重量参数优化，以最大限度地提高检测精度。该方法针对不同的数据集进行了评估，偏差为1.69 ms，标准偏差为10.55 ms，平均绝对误差为6.68 ms。

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Poster Abstract: Automated Detection of the Onset of Ventricular Depolarization in Challenging Clinical ECG Data

This paper presents a novel method for automatically detecting the onset of ventricular depolarization in electrocardiogram (ECG). In order to accommodate highly variable ECG morphologies in potentially noisy ECG signals, a weighted combination of factors that are consistent with the onset of ventricular depolarization is computed. Weight parameters are optimized to maximize the detection accuracy. The proposed method is evaluated against diverse datasets, yielding a bias of 1.69 ms, standard deviation of 10.55 ms, and mean absolute error of 6.68 ms.

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2019 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

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