M-ECG: Extracting Heart Signals with a Novel Computational Analysis of Magnetoencephalography Data

Abstract

Magnetoencephalography (MEG) measures the magnetic fields generated by neural activity with high temporal and spatial resolution. Because of its focus on brain activity, other biopotentials, including muscle artifacts and heart signals, are typically filtered or rejected. In this study, the feasibility of extracting cardiac signals from MEG data, which is termed magnetoencephalographic electrocardiogram (M-ECG; in contrast to the electrocardiogram or ECG) is explored. Using the publicly available Brainstorm MEG auditory dataset CTF and OMEGA resting-state sample dataset, a novel algorithm is developed that utilizes either independent component analysis (ICA) or MEG reference sensors to extract M-ECG signals and compute heart rate variability (HRV) from MEG data reliably and accurately. Signal processing methods in the time, frequency, and time-frequency domains along with statistical tests such as Spearman correlation, root mean square error, mean absolute error, Bland-Altman mean difference, and Mann-Whitney U Test are employed to assess the similarities across the signals. The results indicate a significant alignment of temporal and frequency spectral power characteristics between M-ECG HRV and ECG HRV signals, suggesting a promising degree of similarity and correspondence. The findings highlight the feasibility of extracting M-ECG and computing HRV directly from raw MEG data. These insights hold the potential to enhance multimodal neuroimaging methodologies and further elucidate the intricate interplay between brain activity and cardiovascular function. The potential of HRV as a biomarker for brain disorders could improve diagnostic accuracy, prognostic assessment, and therapeutic strategies, particularly in neurological disorders with centrally mediated autonomic dysfunction.