Removal of Electrocardiogram Artifacts From Local Field Potentials Recorded by Sensing-Enabled Neurostimulator

Abstract

Sensing-enabled neurostimulators are an advanced technology for chronic observation of brain activities, and show great potential for closed-loop neuromodulation and as implantable brain-computer interfaces. However, local field potentials (LFPs) recorded by sensing-enabled neurostimulators can be contaminated by electrocardiogram (ECG) signals due to complex recording conditions and limited common-mode-rejection-ratio (CMRR). In this study, we propose a solution for removing such ECG artifacts from local field potentials (LFPs) recorded by a sensing-enabled neurostimulator. A synchronized monopolar channel was added as an ECG reference, and two pre-existing methods, i.e., template subtraction and adaptive filtering, were then applied. ECG artifacts were successfully removed and the performance of the method was insensitive to residual stimulation artifacts. This approach to removal of ECG artifacts broadens the range of applications of sensing-enabled neurostimulators.

The sensitivity of ECG contamination to surgical implantation site in brain computer interfaces

“…One or more of these might be adopted in future systems. Finally, given the characteristics of the high amplitude QRS component, post-hoc processing can restore a significant portion (~80%) underlying neural activity in many contaminated signals; this approach could prove possible in realtime if achievable with acceptable power consumption [35].…”

A comparison of methods to suppress electrocardiographic artifacts in local field potential recordings

“…Moreover, the occurrence of an R-peak in the external ECG signal does not necessarily serve as ground truth of the occurrence of an R-peak of the ECG artifact in the LFP signal. Whereas the current study ensured ECG artifact suppression by predetermining the R-peaks of the ECG artifact in the LFP signal using an external ECG signal, another solution is suggested by Chen and colleagues (2021). They demonstrated the feasibility of recording an ECG reference signal using the monopolar montage (between the IPG and remaining contact point) of a sensing-enabled neurostimulator.…”

“…To adequately deal with this issue, robust ECG artifact suppression methods must be developed. Previous studies have adopted various methods to attenuate ECG artifacts in LFP signals (Canessa et al, 2020; Chen et al, 2021; Thenaisie et al, 2021). The performance of different approaches have recently been compared, which all were found to successfully remove ECG artifacts (Hammer et al, 2022).…”

Proceedings of the 10th annual deep brain stimulation think tank: Advances in cutting edge technologies, artificial intelligence, neuromodulation, neuroethics, interventional psychiatry, and women in neuromodulation

“…Therefore, closed-loop DBS will become more important and could shed light on the chronic DBS mechanism. To accurately describe the information from the deep brain target, it will be necessary to remove the artifacts, for which a lot of preliminary preparations have been made ( Qian et al, 2017a , b ; Chen et al, 2021b , c ). Based on 6 months recordings of STN-LFP signal, Dr. Li and colleagues recently reported the potential characteristics of chronic neuromodulation effects in the stimulated target ( Chen Y. et al, 2020 ).…”