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A novel ECG signal denoising filter selection algorithm based on conventional neural networks

Pravin, C. and Ojha, V. ORCID: (2021) A novel ECG signal denoising filter selection algorithm based on conventional neural networks. In: 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020), 14-17 Dec 2020, Miami, Florida,

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To link to this item DOI: 10.1109/ICMLA51294.2020.00176


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 non-contact 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 adaptive determining filters. 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.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:94588


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