DeepFilter: An ECG baseline wander removal filter using deep learning techniques

• Published in: Biomedical signal processing and control 2021-09, Vol.70, p.102992, Article 102992
• Main Authors: Romero, Francisco P., Piñol, David C., Vázquez-Seisdedos, Carlos R.
• Format: Article
• Language: English
• Subjects: Baseline wander; Deep learning; ECG; Noise filtering
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.102992

According to the World Health Organization, around 36% of the annual deaths are associated with cardiovascular diseases and 90% of heart attacks are preventable. Electrocardiogram signal, acquired whether during exercise stress test or resting conditions, allows cardiovascular disease diagnosis. However, during the acquisition, there is a variety of noises that may damage the signal quality thereby compromising their diagnostic potential. The baseline wander is one of the most undesirable noises. In this work, we propose a novel algorithm for BLW noise filtering using deep learning techniques. The model performance was validated using the QT Database and the MIT-BIH Noise Stress Test Database from Physionet. In addition, several comparative experiments were performed against state-of-the-art methods using traditional filtering as well as deep learning techniques. The proposed approach yields the best results on four similarity metrics, namely: the sum of squared distance, maximum absolute square, percentage of root distance, and cosine similarity with 5.20±7.96 au, 0.39±0.28 au, 50.45±29.60 au and, 0.89±0.1 au, respectively. The source codes of the proposed model as well as the implementation of related techniques are freely available on Github.