Abstract
The electrocardiogram (ECG) signal suffers greatly due to various non-Gaussian noise sources, such as baseline wander, muscle artifact, and impulsive-related signal disruptions due to electrode movement. More conservative denoising algorithms, like adaptive filtering, wavelet transforms or empirical mode decomposition, are weak in preserving the fine morphology of ECG waveforms in such cases, and tend to distort diagnostically meaningful ECG waveform components (such as the QRS complex and P-T waves). As the need to deploy robust and high-fidelity noise suppression has grown in clinical and wearable healthcare settings, more sophisticated noise suppression techniques have become necessary.
To solve this problem, we introduce an Advanced Dense Convolutional Network (ADCD-Net) to denoise ECGs in non-Gaussian noise conditions. It is an encoder-decoder design and a broad band of connections that can effectively go through noise cancellation and contains useful cardiac changes. A hybrid loss of time domain reconstruction, spectral similarity are used to train the network to incorporate identity artifact at both the impulsive and broadband scales. Benchmark ECG experimental assessments show that ADCD-Net outperforms classical and more recent deep learning techniques and yields increasing SNR, PRD, and QRS detection performance. It will provide a valid and highly accessible solution on real-time monitoring of ECG on the telemedicine and wearable health care system.
