Event recognition, separation and classification from ECG recordings

Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286) Pub Date : 1998-10-29 DOI:10.1109/IEMBS.1998.745883

S. Szilágyi

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引用次数: 16

Abstract

This paper presents a new event classification method. First a pre-filtering is carried out, followed by a (ECG) beat detector and classifier. When it is possible, the characteristic points (P,Q,R,S,J,T,U) are recognized and classified. After that an event recognition method is performed, which can extract the most important information helping doctors to build up a quick and reliable diagnosis. Tested with MIT/BIH database, we observed (ECG) beat detection rate above 99.85%, but the beat classification algorithm needs more development for both methods (parametrical and transformation). To evaluate the performance of the characteristic points detection algorithm, we used our evaluated samples (16-bit resolution and 500 Hz sampling rate). The main result of this work is, that although in many cases the ECG signal contains in itself enough information to build up a diagnosis and the program can determine many useful information for the doctor, the developed algorithm is not able to realize by itself a safe diagnosis.

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心电记录的事件识别、分离和分类

本文提出了一种新的事件分类方法。首先进行预滤波，然后是(心电)心跳检测器和分类器。在可能的情况下，对特征点(P、Q、R、S、J、T、U)进行识别和分类。然后进行事件识别方法，提取最重要的信息，帮助医生建立快速可靠的诊断。在MIT/BIH数据库中，我们观察到(ECG)的心跳检测率在99.85%以上，但无论是参数化方法还是变换方法，心跳分类算法都需要进一步的发展。为了评估特征点检测算法的性能，我们使用了我们评估的样本(16位分辨率和500 Hz采样率)。这项工作的主要结果是，尽管在许多情况下，心电信号本身包含足够的信息来建立诊断，并且程序可以为医生确定许多有用的信息，但所开发的算法本身并不能实现安全的诊断。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286)

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