一维卷积神经网络对心电图和 BCG 信号心率指标的影响

IF 1.6 4区 医学 Q4 ENGINEERING, BIOMEDICAL
Juan Pablo Moreno, Miguel A. Sepúlveda, Esteban J. Pino
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引用次数: 0

摘要

目的心脏信号中运动伪影(MA)的存在会对心率(HR)等高层次信息的可靠性产生负面影响,从而影响病理诊断的正确性。本文提出了一种基于一维卷积神经网络(1D CNN)的运动伪影检测方法,该方法可将信号中的噪声区域标记为不可靠区域,从而在度量计算中避免使用这些区域。该网络从 1 秒钟的数据片段中提取特征,然后将其分类为干净或噪声。此外,我们还利用半合成的球心电图(BCG)信号对该模型的调整版本进行了训练。结果心电图分类的准确率为 95.9%,BCG 分类的准确率为 91.1%。这两种分类器都被整合到了心搏检测系统中,对于信噪比为 0 dB 的污染信号,心电图检测算法的灵敏度从 75% 提高到了 98.5%,而 BCG 的灵敏度则从 72.1% 提高到了 94.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

1D Convolutional Neural Network Impact on Heart Rate Metrics for ECG and BCG Signals

1D Convolutional Neural Network Impact on Heart Rate Metrics for ECG and BCG Signals

Purpose

The presence of motion artifacts (MA) in cardiac signals negatively impacts the reliability of higher-level information such as the Heart Rate (HR), and therefore the correct diagnosis of pathologies. This paper proposes an MA detection method, based on One-Dimensional Convolutional Neural Networks (1D CNN), to label noisy zones of signals as unreliable, and subsequently avoid them for metric calculations.

Methods

To validate the concept, we first design a CNN to detect MAs in electrocardiogram (ECG) recordings from MIT–BIH Arrhythmia and Noise Stress Test Databases. This network extracts features from 1 s data segments, and then classifies them as clean or noisy. Also, we then train a tuned version of the model with semi-synthetic ballistocardiogram (BCG) signals.

Results

The classification in ECG achieves an accuracy of 95.9% and the BCG classification obtains an accuracy of 91.1%. Both classifiers are incorporated into beat detection systems, which produce an increase in the sensitivity of the detection algorithms from 75 to 98.5% in the ECG case, and from 72.1 to 94.5% in the case of BCG, for signals contaminated at 0 dB of SNR.

Conclusion

We propose that this method will improve accuracy of any processing algorithm on BCG signals by identifying useful segments where a high accuracy can be achieved.

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来源期刊
CiteScore
4.30
自引率
5.00%
发文量
81
审稿时长
3 months
期刊介绍: The purpose of Journal of Medical and Biological Engineering, JMBE, is committed to encouraging and providing the standard of biomedical engineering. The journal is devoted to publishing papers related to clinical engineering, biomedical signals, medical imaging, bio-informatics, tissue engineering, and so on. Other than the above articles, any contributions regarding hot issues and technological developments that help reach the purpose are also included.
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