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一种稳健且实时可行的基于包络的心音分类算法:不同生理条件下的验证。

A Robust and Real-Time Capable Envelope-Based Algorithm for Heart Sound Classification: Validation under Different Physiological Conditions.

机构信息

Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany.

出版信息

Sensors (Basel). 2020 Feb 11;20(4):972. doi: 10.3390/s20040972.

Abstract

This paper proposes a robust and real-time capable algorithm for classification of the firstand second heart sounds. The classification algorithm is based on the evaluation of the envelope curveof the phonocardiogram. For the evaluation, in contrast to other studies, measurements on twelveprobands were conducted in different physiological conditions. Moreover, for each measurement theauscultation point, posture and physical stress were varied. The proposed envelope-based algorithmis tested with two different methods for envelope curve extraction: the Hilbert transform andthe short-time Fourier transform. The performance of the classification of the first heart soundsis evaluated by using a reference electrocardiogram. Overall, by using the Hilbert transform,the algorithm has a better performance regarding the F-score and computational effort. Theproposed algorithm achieves for the S classification an F-score up to 95.7% and in average 90.5 %.The algorithm is robust against the age, BMI, posture, heart rate and auscultation point (exceptmeasurements on the back) of the subjects. The ECG and PCG records are available from the authors.

摘要

本文提出了一种用于第一心音和第二心音分类的稳健且实时的算法。该分类算法基于心音图包络曲线的评估。与其他研究相比,评估过程在 12 名被试者的不同生理条件下进行。此外,对于每次测量,听诊点、姿势和身体应激都有所不同。所提出的基于包络的算法使用两种不同的包络曲线提取方法进行测试:希尔伯特变换和短时傅里叶变换。通过使用参考心电图来评估第一心音分类的性能。总的来说,使用希尔伯特变换,该算法在 F 分数和计算工作量方面具有更好的性能。对于 S 分类,所提出的算法的 F 分数高达 95.7%,平均为 90.5%。该算法对受试者的年龄、BMI、姿势、心率和听诊点(背部测量除外)具有鲁棒性。ECG 和 PCG 记录可从作者处获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06eb/7070375/71c9ba68f487/sensors-20-00972-g001.jpg

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