Abstract
Wearable devices have been showing promising results in a large range of applications: since industry, to entertainment and, in particular, healthcare. In the scope of movement disorders, wearable devices are being widely implemented for motor symptoms objective assessment. Currently, clinicians evaluate patients' motor symptoms resorting to subjective scales and visual perception, such as in Parkinson's Disease. The possibility to make use of wearable devices to quantify this disorder motor symptoms would bring an accurate follow-up on the disease progression, leading to more efficient treatments. Here we present a novel textile embedded low-power wearable device capable to be used in any scenario of movement disorders assessment due to its seamless, comfort and versatility. Regarding our research, it has already improved the setup of a wrist rigidity quantification system for Parkinson's Disease patients: the iHandU system. The wearable comprises a hardware sensing unit integrated in a textile band with an innovative design assuring higher comfort and easiness-to-use in movement disorders assessment. It enables to collect inertial data (9-axis) and has the possibility to integrate two analog sensors. A web platform was developed for data reading, visualization and recording. To ensure inertial data reliability, validation tests for the accelerometer and gyroscope sensors were conducted by comparison with its theoretical behavior, obtaining very good results.

Abstract
Cathodal transcranial direct current stimulation (c-tDCS) is a non-invasive option for treatment of refractory epilepsy. However, it is still unknown whether this therapy has a positive stabilizing effect on the vegetative function of these patients. Heart Rate Variability (HRV) is considered an efficient tool to monitor the cardiac autonomic system, which has been correlated with the risk of Sudden Unexpected Death in Epilepsy (SUDEP). In this study, changes in HRV are investigated after c-tDCS of six patients (34.50 +/- 11.10 years) with refractory epilepsy, which have been selected at the University Hospital, LMU Munich. Patients were categorized as responders (n=2), non-responders (n=3) and uncategorized (n=1). We analyzed 24 hours of electrophysiological data recorded before and after treatment, and computed HRV metrics (AVNN, SDNN, RMSD, pNN20, pNN50, LH/HF, 0V, 1V, 2LV, 2UV, SD1 and SD2). All patients revealed a change in almost all HRV metrics post stimulation. Grouped all patients, there was a significant (p<0.05) change in RMSSD, pNN50, SD1 and LH/HF. For responders there was an increase in all time domain and nonlinear metrics, which was not seen for non-responders. These results suggest that tDCS exerts significant changes in cardiovascular autonomic system in patients with refractory epilepsy. HRV metrics may also serve as biomarkers of the response to tDCS stimulation. A larger dataset is being gathered for further analysis.

Abstract
Adaptive Deep Brain Stimulation has recently emerged to tackle conventional DBS limitations by measuring disease fluctuations and to adapt stimulation parameter accordingly. In early 2020, Medtronic launched in the European Union the first certified DBS neurostimulator capable of simultaneously stimulate and read signals from the deep brain structures, the PerceptTMPC. In epilepsy, the most common target brain structure is the Anterior Nucleus of Thalamus and the Local Field Potentials analysis requires prior synchronization of data recorded from the Percept PC with video-Electroencephalography (vEEG) equipment. Fine-grained synchronization (sub-second resolution) is mandatory for multimodal neurodata analysis and may be achieved by aligning artefacts perceived in both systems. In this work we study two methods aiming for neurodata streams clock synchronization: one based on DBS stimulation artefacts and another on tapping maneuver artefacts. For this purpose, we studied the data collected from the first epileptic patient that underwent 1-week vEEG-PerceptTMPC monitoring at a Hospital monitoring unit. We found that tapping maneuver-based methodology allowed a more accurate synchronization in relation to the stimulation artefact-based method (0.56s vs. 2.07s absolute average uncertainty). This method was also more complete one since tapping timestamps can be determined by video timeframes and do not require a prior identification of artefacts in EEG data by clinicians.

Abstract
Deep brain stimulation of the Anterior Nucleus of the Thalamus (ANT-DBS) is an effective therapy in epilepsy. Poorer surgical outcomes are related to deviations of the lead from the ANT-target. The target identification relies on the visualization of anatomical structures by medical imaging, which presents some disadvantages. This study aims to research whether ANT-LFPs recorded with the Percept (TM) PC neurostimulator can be an asset in the identification of the DBS-target. For this purpose, 17 features were extracted from LFPs recorded from a single patient, who stayed at an Epilepsy Monitoring Unit for a 5-day period. Features were then integrated into two machine learning (ML)-based methodologies, according to different LFP bipolar montages: Pass1 (nonadjacent channels) and Pass2 (adjacent channels). We obtained an accuracy of 76.6% for the Pass1-classifier and 83.33% for the Pass2-classifier in distinguishing locations completely inserted in the target and completely outside. Then, both classifiers were used to predict the target percentage of all combinations, and we found that contacts 3 (left hemisphere) and 2 and 3 (right hemisphere) presented higher signatures of the ANT-target, which agreed with the medical images. This result opens a new window of opportunity for the use of LFPs in the guidance of DBS target identification.