A Novel ECG Signal Denoising Filter Selection Algorithm Based on Conventional Neural Networks
A Novel ECG Signal Denoising Filter Selection Algorithm Based on Conventional Neural Networks
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DOI：
10.1109/icmla51294.2020.00176
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发表时间：
2020-12
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影响因子：
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通讯作者：
Chandresh Pravin;Varun Ojha
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作者：
Chandresh Pravin;Varun Ojha
我们提出了一种新颖的基于深度学习的去噪滤波器选择算法，用于噪声心电图 (ECG) 信号预处理。在临床条件下测量的心电图信号，例如在医院中使用皮肤接触设备获取的心电图信号，通常包含基线信号干扰和不需要的伪影；事实上，对于在临床环境之外获得的信号，例如使用非接触式雷达系统记录的心率特征，测量结果包含比在临床条件下获得的信号更高水平的噪声。在本文中，我们重点关注使用非接触式雷达系统获取的心率信号，用于辅助生活环境。此类信号比在临床条件下测量的信号包含更多的噪声，因此需要一种能够适应输入信号变化的新颖的信号噪声消除方法。目前从此类波形中去除噪声的最常见方法是使用滤波器；其中最流行的滤波方法是小波滤波器。然而，在某些情况下，使用不同的滤波方法可能会导致波形具有更高的信噪比 (SNR)；在本文中，我们研究了小波和椭圆滤波方法，以减少使用辅助技术获取的心电图信号中的噪声。我们提出的卷积神经网络架构根据噪声信号的预期 SNR 值对噪声信号的最佳滤波方法进行分类（准确度为 92.8%）。
We propose a novel deep learning based denoising filter selection algorithm for noisy Electrocardiograph (ECG) signal preprocessing. ECG signals measured under clinical conditions, such as those acquired using skin contact devices in hospitals, often contain baseline signal disturbances and unwanted artefacts; indeed for signals obtained outside of a clinical environment, such as heart rate signatures recorded using non-contact radar systems, the measurements contain greater levels of noise than those acquired under clinical conditions. In this paper we focus on heart rate signals acquired using noncontact radar systems for use in assisted living environments. Such signals contain more nose than those measured under clinical conditions, and thus require a novel signal noise removal method capable of adapting to variations in the input signals. Currently the most common method of removing noise from such a waveform is through the use of filters; the most popular filtering method amongst which is the wavelet filter. There are, however, circumstances in which using a different filtering method may result in higher signal-to-noise-ratios (SNR) for a waveform; in this paper, we investigate the wavelet and elliptical filtering methods for the task of reducing noise in ECG signals acquired using assistive technologies. Our proposed convolutional neural network architecture classifies (with 92.8% accuracy) the optimum filtering method for noisy signal based on its expected SNR value.