Automated extraction of heart rate variability from magnetoencephalography signals
Ryan C. Godwin,
William C. Flood,
Jeremy P. Hudson,
Marc D. Benayoun,
Michael E. Zapadka,
Ryan L. Melvin,
Christopher T. Whitlow
Affiliations
Ryan C. Godwin
Department of Anesthesiology and Perioperative Medicine, Heersink School of Medicine, University of Alabama, Birmingham, Birmingham, AL, USA; Department of Radiology, Heersink School of Medicine, University of Alabama, Birmingham, Birmingham, AL, USA; Corresponding author. Department of Anesthesiology and Perioperative Medicine, Heersink School of Medicine, University of Alabama, Birmingham, Birmingham, AL, USA.
William C. Flood
Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest University School of Medicine, Winston Salem, NC, USA
Jeremy P. Hudson
Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest University School of Medicine, Winston Salem, NC, USA
Marc D. Benayoun
Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA
Michael E. Zapadka
Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest University School of Medicine, Winston Salem, NC, USA
Ryan L. Melvin
Department of Anesthesiology and Perioperative Medicine, Heersink School of Medicine, University of Alabama, Birmingham, Birmingham, AL, USA
Christopher T. Whitlow
Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest University School of Medicine, Winston Salem, NC, USA
Magnetoencephalography (MEG) measures magnetic fluctuations in the brain generated by neural processes, some of which, such as cardiac signals, are generally removed as artifacts and discarded. However, heart rate variability (HRV) has long been regarded as a biomarker related to autonomic function, suggesting the cardiac signal in MEG contains valuable information that can provide supplemental health information about a patient. To enable access to these ancillary HRV data, we created an automated extraction tool capable of capturing HRV directly from raw MEG data with artificial intelligence. Five scans were conducted with simultaneous MEG and electrocardiogram (ECG) acquisition, which provides a ground truth metric for assessing our algorithms and data processing pipeline. In addition to directly comparing R-peaks between the MEG and ECG signals, this work explores the variation of the corresponding HRV output in time, frequency, and non-linear domains. After removing outlier intervals and aligning the ECG and derived cardiac MEG signals, the RMSE between the RR-intervals of each was RMSE1 = 2 ms, RMSE2 = 2 ms, RMSE3 = 8 ms, RMSE4 = 4 ms, RMSE5 = 13 ms. The findings indicate that cardiac artifacts from MEG data carry sufficient signal to approximate an individual's HRV metrics.
