Objective: Sensing-enabled neurostimulators for deep brain stimulation (DBS) therapy record neural activity directly from the stimulating electrodes in the form of local field potentials (LFPs). However, these LFPs are often contaminated with electrocardiographic (ECG) artifacts that impede the detection of physiomarkers for adaptive DBS research. This study systematically compared the ability of different ECG suppression methods to recover disease-specific electrical brain activity from ECG-contaminated LFPs. Approach: Three ECG suppression methods were evaluated: (1) QRS interpolation of the Perceive toolbox, (2) four variants of a template subtraction method, and (3) sixteen variants of a singular value decomposition (SVD) method. The performance of these methods was examined using LFPs recorded with the Medtronic PerceptTM PC system from the subthalamic nucleus in nine patients with Parkinson's disease while stimulation was turned off ("OFF-DBS"; anode disconnected) and while stimulation was turned on at 0 mA ("ON-DBS 0 mA"; anode connected). In addition, ECG-contaminated LFPs were simulated by scaling a co-recorded external ECG signal and adding it to the OFF-DBS LFPs. Main Results: ECG artifacts were present in 10 out of 18 ON-DBS 0 mA recordings. All ECG suppression methods were able to drastically reduce the percent difference of beta band (13 - 35 Hz) spectral power and at least partly recover the beta peak and beta burst dynamics. Using predetermined R-peaks improved the performance of the ECG suppression methods. Lengthening the time window around the R-peaks resulted in stronger reduction in artifact-induced beta band power but at an increased risk of flattening the beta peak and loss of beta burst dynamics. Significance: The SVD method formed the preferred trade-off between artifact cleaning and signal loss, as long as its parameter settings (time window around the R-peaks; number of components) are adequately chosen.

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