Key result
A hybrid signal denoising methodology outperformed state-of-the-art automated methods in removing cardiac artifacts from sleep EEG signals across synthetic and clinical datasets.
Why the study?
Sleep EEG signals are vulnerable to ECG cardiac artifacts, which can severely affect data needed to diagnose sleep-related neurological disorders.
A novel hybrid denoising model effectively removes cardiac (ECG) artifacts from sleep EEG signals with minimal data loss, outperforming existing automated methods.
Sleep is one of the prime natural activities for human well-being in physical, emotional, and mental aspects. The assessment of sleep Electroencephalography (EEG) signals is required to diagnose sleep-related neurological disorders. It is found that sleep EEG signals are extremely vulnerable to highly energetic electrocardiogram (ECG) signals. The intermixing of ECG into EEG, commonly known as cardiac artifacts, might severely affect the sleep EEG data. In order to have artifact-free EEG signal, a hybrid signal denoising methodology which includes empirical wavelet transforms (EWT), adaptive threshold-based nonlinear Teager-Kaiser energy operator (TEO), and customized morphological filter in accompanying with modified ensemble average subtraction (MEAS) is proposed for automatic detection and suppression of cardiac artifact from a single-channel EEG. The efficacy of the proposed methodology presented in the paper has been evaluated over standard public datasets such as CinC Challenge 2014 dataset (synthetic), and MIT-BIH polysomnography data (clinical). It has been observed that the proposed method outperforms other state-of-the-art automated EEG artifact elimination methods in terms of few popular denoising performance indexes such as signal to artifact ratio, percentage root mean square difference, percentage distortion in power spectral density, structural similarity index measure, and execution time. The proposed method is robust, time-efficient, and preserves the majority of EEG data with minimal loss, making it suitable for neuro clinical EEG analysis.
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Ranjan et al. (2022) studied Sleep EEG signals with cardiac artifacts. Hybrid signal denoising methodology vs. State-of-the-art automated EEG artifact elimination methods was evaluated on Denoising performance indexes (signal to artifact ratio, percentage root mean square difference, percentage distortion in power spectral density, structural similarity index measure, and execution time). A hybrid signal denoising methodology outperformed state-of-the-art automated methods in removing cardiac artifacts from sleep EEG signals across synthetic and clinical datasets.
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