A deformable CNN architecture for predicting clinical acceptability of ECG signal

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biocybernetics and Biomedical Engineering Pub Date : 2023-01-01 DOI:10.1016/j.bbe.2023.01.006
Jaya Prakash Allam , Saunak Samantray , Suraj Prakash Sahoo , Samit Ari
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引用次数: 2

Abstract

The degraded quality of the electrocardiogram (ECG) signals is the main source of false alarms in critical care units. Therefore, a preliminary analysis of the ECG signal is required to decide its clinical acceptability. In conventional techniques, different handcrafted features are extracted from the ECG signal based on signal quality indices (SQIs) to predict clinical acceptability. A one-dimensional deformable convolutional neural network (1D-DCNN) is proposed in this work to extract features automatically, without manual interference, to detect the clinical acceptability of ECG signals efficiently. In order to create DCNN, the deformable convolution and pooling layers are merged into the regular convolutional neural network (CNN) architecture. In DCNN, the equidistant sampling locations of a regular CNN are replaced with adaptive sampling locations, which improves the network’s ability to learn based on the input. Deformable convolution layers concentrate more on significant segments of the ECG signals rather than giving equal attention to all segments. The proposed method is able to detect acceptable and unacceptable ECG signals with an accuracy of 99.50%, recall of 99.78%, specificity of 99.60%, precision of 99.47%, and F-score of 0.999. Experimental results show that the proposed method performs better than earlier state-of-the-art techniques.

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用于预测ECG信号临床可接受性的可变形CNN结构
心电信号质量的下降是重症监护病房虚警的主要来源。因此,需要对心电信号进行初步分析,以确定其临床可接受性。在传统技术中,基于信号质量指数(SQIs)从心电信号中提取不同的手工特征来预测临床可接受性。本文提出了一种一维可变形卷积神经网络(1D-DCNN),在不受人工干扰的情况下自动提取特征,有效地检测心电信号的临床可接受性。为了创建DCNN,将可变形卷积层和池化层合并到规则卷积神经网络(CNN)架构中。在DCNN中,将常规CNN的等距采样位置替换为自适应采样位置,提高了网络基于输入的学习能力。可变形卷积层更多地关注心电信号的重要部分,而不是对所有部分给予同等的关注。该方法能够检测出可接受和不可接受的心电信号,准确率为99.50%,召回率为99.78%,特异性为99.60%,精密度为99.47%,f值为0.999。实验结果表明,该方法的性能优于早期的先进技术。
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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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