Adaptive Deep Brain Stimulation (aDBS) offers the potential for personalized stimulation strategies for patients with Parkinson's Disease (PD). The closed loop characteristic of this system requires the incorporation of PD relevant biomarkers that determine the patient's need. In order to obtain high quality LFP (Local Field Potential) input signals, the ECG (electrocardiogram) noise should be suppressed. The aim of this project is to study the performance of various algorithmic ECG noise suppression methods. Out of the ADAPT-PD trial, we have taken 9 LFP channels with consistent ECG artefacts for exploring the performance of ECG noise suppression models. As a reference point for filtering performance, we have used survey data (DBS-OFF). Using an externally measured ECG as reference, we have implemented two Adaptive NLMS (Normalized Least Mean Squared) Noise Cancellation algorithms. For the first version, we have used stimulation ramping alone for synchronization of the data sets. The second version includes an extension that aims to improve only the data synchronization feature. Furthermore, we have explored the ECG noise suppression performance of a proposed template subtraction method, using 11 different variations of epoch length. For improved analysis, we have used three data sets, namely personal (#1), patient group (#9) and simulated (#5346) data, using the Perceive toolbox ECG filtering method as the benchmark. Simulated LFPs are based on survey data combined with 9 external ECGs in 11 levels of contamination (100-1100 %). We have conducted analysis in both the time and frequency domain (beta-range 13-30 Hz), in order to estimate the absolute difference from the reference survey. Outcomes in the frequency domain show that, for personal performance, template subtraction tweaking provides an improvement up to 37.6 % over the Perceive toolbox. Furthermore, the outcomes show that, for both the patient and simulation group, optimal performance is obtained using the Perceive Toolbox with 20.7 % accuracy for the patient group and 4.7 % for the simulations. It can be concluded that the survey LFPs can be used for personal calibration of ECG noise suppression. This contradicts the aim to find one universal LFP ECG noise suppression method. There is a need for a reliable data synchronization method between the Percept LFPs and other biometric data. Reliable synchronization would improve the usability of the external ECG as reference signal in adaptive filters. Furthermore, reliable synchronization would accelerate the discovery of linked physical symptoms for PD biomarkers.

Deep brain stimulation (DBS) is used to treat a variety of movement disorders. In current DBS therapy, the stimulation parameters are manually adjusted based on a subjective assessment of patient’s symptoms. State-of-the-art DBS research focuses on recording brain activity to create a patient-specific neuronal profile that correlates with the patient’s symptom-severity. The neuronal profile could then be used as feedback signal for an adaptive DBS system that automatically adjusts the stimulation. The Medtronic PerceptTM is the first commercially available, fully implanted neurostimulator with sensing technology. In this study, the neuronal activity in the form of local field potentials (LFPs) of eleven patients were recorded with Percept during both stimulation off- and on. To obtain a reliable neuronal profile from the LFPs, the signals should be free from electrocardiography (ECG) artefact and should be synchronized with externally recorded data that informs us about the patient’s symptom-severity. A template subtraction method and a method based on independent component analysis were developed to suppress ECG artefact. Both methods were compared to the ECG removal method of the Perceive Toolbox. Three methods of synchronization were explored: externally tapping, using the stimulation artefact, and by ECG cross-correlation. Results show the template subtraction method to most effectively suppress ECG artefacts while preserving LFP frequency content. To save effort but assure accuracy of the synchronization, the ECG cross-correlation method should be applied though validated by confirming accurate alignment of the stimulation artefact. The development and evaluation of the ECG artefact suppression- and external data synchronization methods contribute to research focused on finding a reliable feedback signal for adaptive DBS.