The Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) outperformed state-of-the-art models in ECG denoising, achieving a 55% reduction of false positives in R peak detection.
Does the TCDAE model improve random mixed noise removal in ECG signals compared to baseline methods?
The proposed TCDAE model effectively suppresses random mixed noise in ECG signals, significantly reducing false positives in R peak detection and demonstrating feasibility for clinical deployment.
Effect estimate: 55% reduction
Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others. These noises often occur in combination during the actual data acquisition process, resulting in erroneous or perplexing interpretations for cardiologists. To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study. The encoder of TCDAE is composed of three stacked gated convolutional layers and a Transformer encoder block with a point-wise multi-head self-attention module. To obtain minimal distortion in both time and frequency domains, we also propose a frequency weighted Huber loss function in training phase to better approximate the original signals. The TCDAE model is trained and tested on the QT Database (QTDB) and MIT-BIH Noise Stress Test Database (NSTDB), with the training data and testing data coming from different records. All the metrics perform the most robust in overall noise and separate noise intervals for RMN removal compared with the baseline methods. We also conduct generalization tests on the Icentia11k database where the TCDAE outperforms the state-of-the-art models, with a 55% reduction of the false positives in R peak detection after denoising. The TCDAE model approximates the short-term and long-term characteristics of ECG signals and has higher stability even under extreme RMN corruption. The memory consumption and inference speed of TCDAE are also feasible for its deployment in clinical applications.
Chen et al. (Fri,) conducted a other in Electrocardiogram (ECG) random mixed noise. Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) vs. Baseline methods and state-of-the-art models was evaluated on False positives in R peak detection after denoising (55% reduction). The Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) outperformed state-of-the-art models in ECG denoising, achieving a 55% reduction of false positives in R peak detection.
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