Abstract
Occupational stress is a complex process affecting health and performance. Air Traffic Control is a complex and demanding profession. The current study demonstrates the concept of using a biomonitoring wearable platform (BWP), that combines self-report measures with biomarkers, to track stress among Air Traffic Controllers. A wearable ECG device was used to gather continuously medical-grade ECG data along with a mobile app for daily stress perception, symptoms and events annotation. A total of 256 hours of data from 32 routine work shifts and 5 days-off, from 5 ATCs was recorded with 35 tagged events using Heart Rate Variability metrics- AVNN, RMSSD, pNN50 and LF/HF were computed from ECG data and analyzed during a) shifts vs days off; b) events vs non-events and c) before and after working pauses. ATCs showed low levels of chronic stress using self-reports. Results showed that stress symptomatology slightly increase from the beginning to the end of the shift (Md=1 to Md=2; p<0.05). Statistical significant physiological changes were found between shifts and days off for AVNN and LF/HF (p<0.05), showing higher physiological activation during shifts. A significant reduction of physiological arousal was verified after working pauses, particularly for AVNN and LF/IIF (p<0.001). Self-reported data also suggests the same trend (p<0.005). Findings reinforced the discriminatory power of AVNN and LF/HF for short-term stress classification using HRV measurements. Results suggest that the rotating working system, with pause/resting periods included, effective acted as a recovery period.

Abstract
Objective: Heart rate variability (HRV), an index of the autonomic cardiac activity, is decreased in patients with epilepsy, and a low HRV is associated with a higher risk of sudden death. Generalized tonic-clonic seizures are one of the most consistent risk factors for SUDEP, but the influence (and relative risk) of each type of seizure on cardiac function is still unknown. Our objective was to assess the impact of the type of seizure (focal to bilateral tonic-clonic seizure - FBTCS - versus non-FBTCS) on periictal HRV, in a group of patients with refractory epilepsy and both types of seizures. Methods: We performed a 48-hour Holter recording on 121 patients consecutively admitted to our Epilepsy Monitoring Unit. We only included patients with both FBTCS and non-FBTCS on the Holter recording and selected the first seizure of each type to analyze. To evaluate HRV parameters (AVNN, SDNN, RMSSD, pNN20, LF, HF, and LF/HF), we chose 5-min epochs pre-and postictally. Results: We included 14 patients, with a median age of 36 (min-max, 16-55) years and 64% were female. Thirty-six percent had cardiovascular risk factors, but no previously known cardiac disease. In the preictal period, there were no statistically significant differences in HRV parameters, between FBTCS and non-FBTCS. In the postictal period, AVNN, RMSSD, pNN20, LF, and HF were significantly lower, and LF/HF and HR were significantly higher in FBTCS. From preictal to postictal periods, FBTCS elicited a statistically significant rise in HR and LF/HF, and a statistically significant fall in AVNN, RMSSD, pNN20, and HF. Non-FBTCS only caused statistically significant changes in HR (decrease) and AVNN (increase). Significance/conclusion: This work emphasizes the greater effect of FBTCS in autonomic cardiac function in patients with refractory epilepsy, compared to other types of seizures, with a significant reduction in vagal tonus, which may be associated with an increased risk of SUDEP.

Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that impairs people's mobility. Due to its erratic nature and complexity, the progression of the disease differs from person to person, making it difficult to keep track of the patient's progress. These factors, together with the limited number of annual clinical appointments, create the need to have a tool that can help patients and healthcare professionals better manage Parkinson's outside of the clinical environment. PDapp strives to address this need combining mHealth features with the capabilities of the iHandU appcessory, a novel and seamless wearable device designed to measure wrist rigidity, bradykinesia (slow movement), and tremor, thus enabling continuous effective follow-up, while connecting patients and clinicians remotely. The PDapp system is comprised of a mobile application where patients can manage their medication, self-perform various symptom tests, and maintain clinicians informed of relevant events; a specialized web dashboard for clinicians to monitor all their patient's history and recent events; and a cloud database that exhibits existing data in real-time. The first prototype integrates all these components and provides a promising proof-of-concept that, with a few additions, can be a system that brings value to Parkinson's management. This application design and functionalities were developed jointly with clinicians, addressing their problems and needs. The collected feedback was very positive stating that its usability and simplicity is completely suitable for patients to use. PDapp will introduce a complete and innovative methodology to follow-up PD patient's disease progression and support clinicians during appointments and patients at home, guiding medication adjustment for better disease management. This system is intended as one more step to the PD mHealth ecosystem, improving follow-up and disease therapy yet reducing clinicians' workload.

Abstract

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.

Abstract
Hereditary amyloidosis associated with transthyretin (ATTRv), is a rare autosomal dominant disease characterized by length-dependent symmetric polyneuropathy that has gait impairment as one of its consequences. The gait pattern of V30M ATTRv amyloidosis patients has been described as similar to that of diabetic neuropathy, associated with steppage, but has never been quantitatively characterized. In this study we aim to characterize the gait pattern of patients with V30M ATTRv amyloidosis, thus providing information for a better understanding and potential for supporting diagnosis and disease progression evaluation. We present a case series in which we conducted two gait analyses, 18 months apart, of five V30M ATTRv amyloidosis patients using a 12-camera, marker based, optical system as well as six force platforms. Linear kinematics, ground reaction forces, and angular kinematics results are analyzed for all patients. All patients, except one, showed a delayed toe-off in the second assessment, as well as excessive pelvic rotation, hip extension and external transverse rotation and knee flexion (in stance and swing phases), along with reduced vertical and mediolateral ground reaction forces. The described gait anomalies are not clinically quantified; thus, gait analysis may contribute to the assessment of possible disease progression along with the clinical evaluation.