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心电图信号中病理期间的 P 波提前检测:不同心律失常情况下的评估。

Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts.

机构信息

Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology Technická 12, Brno, 616 00, Czech Republic.

Institute of Scientific Instruments, The Czech Academy of Sciences Královopolská 147, Brno, 612 64, Czech Republic.

出版信息

Sci Rep. 2019 Dec 13;9(1):19053. doi: 10.1038/s41598-019-55323-3.

Abstract

Reliable P wave detection is necessary for accurate and automatic electrocardiogram (ECG) analysis. Currently, methods for P wave detection in physiological conditions are well-described and efficient. However, methods for P wave detection during pathology are not generally found in the literature, or their performance is insufficient. This work introduces a novel method, based on a phasor transform, as well as innovative rules that improve P wave detection during pathology. These rules are based on the extraction of a heartbeats' morphological features and knowledge of heart manifestation during both physiological and pathological conditions. To properly evaluate the performance of the proposed algorithm in pathological conditions, a standard database with a sufficient number of reference P wave positions is needed. However, such a database did not exist. Thus, ECG experts annotated 12 chosen pathological records from the MIT-BIH Arrhythmia Database. These annotations are publicly available via Physionet. The algorithm performance was also validated using physiological records from the MIT-BIH Arrhythmia and QT databases. The results for physiological signals were Se = 98.42% and PP = 99.98%, which is comparable to other methods. For pathological signals, the proposed method reached Se = 96.40% and PP = 85.84%, which greatly outperforms other methods. This improvement represents a huge step towards fully automated analysis systems being respected by ECG experts. These systems are necessary, particularly in the area of long-term monitoring.

摘要

可靠的 P 波检测对于准确和自动的心电图 (ECG) 分析是必要的。目前,生理条件下 P 波检测的方法已经得到了很好的描述和有效的应用。然而,在病理条件下 P 波检测的方法在文献中一般找不到,或者它们的性能不足。本工作介绍了一种基于相量变换的新方法,以及在病理条件下提高 P 波检测性能的创新规则。这些规则基于提取心跳的形态特征以及在生理和病理条件下心脏表现的知识。为了正确评估所提出的算法在病理条件下的性能,需要一个具有足够数量参考 P 波位置的标准数据库。然而,这样的数据库并不存在。因此,ECG 专家从 MIT-BIH 心律失常数据库中注释了 12 个选定的病理记录。这些注释可通过 Physionet 公开获取。该算法的性能也使用 MIT-BIH 心律失常和 QT 数据库中的生理记录进行了验证。生理信号的结果为 Se=98.42%和 PP=99.98%,与其他方法相当。对于病理信号,所提出的方法达到了 Se=96.40%和 PP=85.84%,大大优于其他方法。这一改进代表着朝着完全自动化分析系统的方向迈出了重要一步,这些系统是 ECG 专家所需要的,特别是在长期监测领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f32c/6911105/2e98a1ff57ac/41598_2019_55323_Fig1_HTML.jpg

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