Does the type of seizure influence heart rate variability changes?

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.

Estimation of ANT-DBS Electrodes on Target Positioning Based on a New PerceptTM PC LFP Signal Analysis.

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 PerceptTM 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.

iHandU: A Novel Quantitative Wrist Rigidity Evaluation Device for Deep Brain Stimulation Surgery.

Deep brain stimulation (DBS) surgery is the gold standard therapeutic intervention in Parkinson's disease (PD) with motor complications, notwithstanding drug therapy. In the intraoperative evaluation of DBS's efficacy, neurologists impose a passive wrist flexion movement and qualitatively describe the perceived decrease in rigidity under different stimulation parameters and electrode positions. To tackle this subjectivity, we designed a wearable device to quantitatively evaluate the wrist rigidity changes during the neurosurgery procedure, supporting physicians in decision-making when setting the stimulation parameters and reducing surgery time. This system comprises a gyroscope sensor embedded in a textile band for patient's hand, communicating to a smartphone via Bluetooth and has been evaluated on three datasets, showing an average accuracy of 80%. In this work, we present a system that has seen four iterations since 2015, improving on accuracy, usability and reliability. We aim to review the work done so far, outlining the iHandU system evolution, as well as the main challenges, lessons learned, and future steps to improve it. We also introduce the last version (iHandU 4.0), currently used in DBS surgeries at São João Hospital in Portugal.

Validation of a Single RGB-D Camera for Gait Assessment of Polyneuropathy Patients.

Motion analysis systems based on a single markerless RGB-D camera are more suitable for clinical practice than multi-camera marker-based reference systems. Nevertheless, the validity of RGB-D cameras for motor function assessment in some diseases affecting gait, such as Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP), is yet to be investigated. In this study, the agreement between the Kinect v2 and a reference system for obtaining spatiotemporal and kinematic gait parameters was evaluated in the context of TTR-FAP. 3-D body joint data provided by both systems were acquired from ten TTR-FAP symptomatic patients, while performing ten gait trials. For each gait cycle, we computed several spatiotemporal and kinematic gait parameters. We then determined, for each parameter, the Bland Altman's bias and 95% limits of agreement, as well as the Pearson's and concordance correlation coefficients, between systems. The obtained results show that an affordable, portable and non-invasive system based on an RGB-D camera can accurately obtain most of the studied gait parameters (excellent or good agreement for eleven spatiotemporal and one kinematic). This system can bring more objectivity to motor function assessment of polyneuropathy patients, potentially contributing to an improvement of TTR-FAP treatment and understanding, with great benefits to the patients' quality of life.

A diffusion-based connectivity map of the GPi for optimised stereotactic targeting in DBS.

BACKGROUND: The GPi (globus pallidus internus) is an important target nucleus for Deep Brain Stimulation (DBS) in medically refractory movement disorders, in particular dystonia and Parkinson's disease. Beneficial clinical outcome critically depends on precise electrode localization. Recent evidence indicates that not only neurons, but also axonal fibre tracts contribute to promoting the clinical effect. Thus, stereotactic planning should, in the future, also take the individual course of fibre tracts into account. OBJECTIVE: The aim of this project is to explore the GPi connectivity profile and provide a connectivity-based parcellation of the GPi. METHODS: Diffusion MRI sequences were performed in sixteen healthy, right-handed subjects. Connectivity-based parcellation of the GPi was performed applying two independent methods: 1) a hypothesis-driven, seed-to-target approach based on anatomic priors set as connectivity targets and 2) a purely data-driven approach based on k-means clustering of the GPi. RESULTS: Applying the hypothesis-driven approach, we obtained five major parcellation clusters, displaying connectivity to the prefrontal cortex, the brainstem, the GPe (globus pallidus externus), the putamen and the thalamus. Parcellation clusters obtained by both methods were similar in their connectivity profile. With the data-driven approach, we obtained three major parcellation clusters. Inter-individual variability was comparable with results obtained in thalamic parcellation. CONCLUSION: The three parcellation clusters obtained by the purely data-driven method might reflect GPi subdivision into a sensorimotor, associative and limbic portion. Clinical and physiological studies indicate greatest clinical DBS benefit for electrodes placed in the postero-ventro-lateral GPi, the region displaying connectivity to the thalamus in our study and generally attributed to the sensorimotor system. Clinical studies relating DBS electrode positions to our GPi connectivity map would be needed to complement our findings.

Monkeys time their pauses of movement and not their movement-kinematics during a synchronization-continuation rhythmic task.

A critical question in tapping behavior is to understand whether the temporal control is exerted on the duration and trajectory of the downward-upward hand movement or on the pause between hand movements. In the present study, we determined the duration of both the movement execution and pauses of monkeys performing a synchronization-continuation task (SCT), using the speed profile of their tapping behavior. We found a linear increase in the variance of pause-duration as a function of interval, while the variance of the motor implementation was relatively constant across intervals. In fact, 96% of the variability of the duration of a complete tapping cycle (pause + movement) was due to the variability of the pause duration. In addition, we performed a Bayesian model selection to determine the effect of interval duration (450-1,000 ms), serial-order (1-6 produced intervals), task phase (sensory cued or internally driven), and marker modality (auditory or visual) on the duration of the movement-pause and tapping movement. The results showed that the most important parameter used to successfully perform the SCT was the control of the pause duration. We also found that the kinematics of the tapping movements was concordant with a stereotyped ballistic control of the hand pressing the push-button. The present findings support the idea that monkeys used an explicit timing strategy to perform the SCT, where a dedicated timing mechanism controlled the duration of the pauses of movement, while also triggered the execution of fixed movements across each interval of the rhythmic sequence.

Developmental dissociation of visual dorsal stream parvo and magnocellular representations and the functional impact of negative retinotopic BOLD responses.

Localized neurodevelopmental defects provide an opportunity to study structure-function correlations in the human nervous system. This unique multimodal case report of epileptogenic dysplasia in the visual cortex allowed exploring visual function across distinct pathways in retinotopic regions and the dorsal stream, in relation to fMRI retinotopic mapping and spike triggered BOLD responses. Pre-surgical EEG/video monitoring, MRI/DTI, EEG/fMRI, PET and SPECT were performed to characterize structure/function correlations in this patient with a very early lesion onset. In addition, we included psychophysical methods (assessing parvo/konio and magnocellular pathways) and retinotopic mapping. We could identify dorsal stream impairment (with extended contrast sensitivity deficits within the input magno system contrasting with more confined parvocellular deficits) with disrupted active visual field input representations in regions neighboring the lesion. Simultaneous EEG/fMRI identified perilesional and retinotopic bilaterally symmetric BOLD deactivation triggered by interictal spikes, which matched the contralateral spread of magnocellular dysfunction revealed in the psychophysical tests. Topographic changes in retinotopic organization further suggested long term functional effects of abnormal electrical discharges during brain development. We conclude that fMRI based visual field cortical mapping shows evidence for retinotopic dissociation between magno and parvocellular function well beyond striate cortex, identifiable in high level dorsal visual representations around visual area V3A which is consistent with the effects of epileptic spike triggered negative BOLD.

pHealth and wearable technologies: a permanent challenge.

Wearable technologies have been evolving towards daily usage and are a major player in the personalized health challenge. In this paper we present a personal view of their evolution, how one of them developed within our lab went to the international market and how this type of technology is being used in pHealth projects for first responder professionals and public transportation drivers.

Novel 3D video action recognition deep learning approach for near real time epileptic seizure classification.

Seizure semiology is a well-established method to classify epileptic seizure types, but requires a significant amount of resources as long-term Video-EEG monitoring needs to be visually analyzed. Therefore, computer vision based diagnosis support tools are a promising approach. In this article, we utilize infrared (IR) and depth (3D) videos to show the feasibility of a 24/7 novel object and action recognition based deep learning (DL) monitoring system to differentiate between epileptic seizures in frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE) and non-epileptic events. Based on the largest 3Dvideo-EEG database in the world (115 seizures/+680,000 video-frames/427GB), we achieved a promising cross-subject validation f1-score of 0.833±0.061 for the 2 class (FLE vs. TLE) and 0.763 ± 0.083 for the 3 class (FLE vs. TLE vs. non-epileptic) case, from 2 s samples, with an automated semi-specialized depth (Acc.95.65%) and Mask R-CNN (Acc.96.52%) based cropping pipeline to pre-process the videos, enabling a near-real-time seizure type detection and classification tool. Our results demonstrate the feasibility of our novel DL approach to support 24/7 epilepsy monitoring, outperforming all previously published methods.

Towards a Closed-loop Neuro-Robotic Approach to DBS Electrode Implantation based on Real-Time Wrist Rigidity Evaluation.

The iHandU system is a wearable device that quantitatively evaluates changes in wrist rigidity during Deep Brain Stimulation (DBS) surgery, allowing clinicians to find optimal stimulation settings that reduce patient symptoms. Robotic accuracy is also especially relevant in DBS surgery, as accurate electrode placement is required to increase effectiveness and reduce side effects. The main goal of this work is to integrate the advantages of each system in a closed-loop system between an industrial robot and the iHandU system. For this purpose, a comparative analysis of a Leksell stereotactic frame and neuro-robotic system accuracies was performed using a lab-made phantom. The neuro-robotic system reached 90% of trajectories, while the stereotactic frame reached all trajectories. There are significant differences in accuracy errors between these trajectories (p < 0.0001), which can be explained by the high correlation between the neuro-robotic system errors and the distance from the trajectory to the origin of the Leksell coordinate system (ρ = 0.72). Overall accuracy is comparable to existing neuro-robotic systems, achieving a deviation of (1.0 ± 0.5) mm at the target point. The accuracy of DBS electrode positioning and stimulation parameters choice leads to better long-term clinical outcomes in Parkinson's disease patients. Our neuro-robotic system combines real-time feedback assessment of the patient's symptomatic response and automatic positioning of the DBS electrode in a specific brain area.

Gait Characterization and Analysis of Hereditary Amyloidosis Associated with Transthyretin Patients: A Case Series.

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.

Corrigendum: Clinical 3-D gait assessment of patients with polyneuropathy associated with hereditary transthyretin amyloidosis.

[This corrects the article DOI: 10.3389/fneur.2020.605282.].

Supporting the Assessment of Hereditary Transthyretin Amyloidosis Patients Based On 3-D Gait Analysis and Machine Learning.

Hereditary Transthyretin Amyloidosis (vATTR-V30M) is a rare and highly incapacitating sensorimotor neuropathy caused by an inherited mutation (Val30Met), which typically affects gait, among other symptoms. In this context, we investigated the possibility of using machine learning (ML) techniques to build a model(s) that can be used to support the detection of the Val30Met mutation (possibility of developing the disease), as well as symptom onset detection for the disease, given the gait characteristics of a person. These characteristics correspond to 24 gait parameters computed from 3-D body data, provided by a Kinect v2 camera, acquired from a person while walking towards the camera. To build the model(s), different ML algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines (SVM), and multilayer perceptron. For a dataset corresponding to 66 subjects (25 healthy controls, 14 asymptomatic mutation carriers, and 27 patients) and several gait cycles per subject, we were able to obtain a model that distinguishes between controls and vATTR-V30M mutation carriers (with or without symptoms) with a mean accuracy of 92% (SVM). We also obtained a model that distinguishes between asymptomatic and symptomatic carriers with a mean accuracy of 98% (SVM). These results are very relevant, since this is the first study that proposes a ML approach to support vATTR-V30M patient assessment based on gait, being a promising foundation for the development of a computer-aided diagnosis tool to help clinicians in the identification and follow-up of this disease. Furthermore, the proposed method may also be used for other neuropathies.

Clinical 3-D Gait Assessment of Patients With Polyneuropathy Associated With Hereditary Transthyretin Amyloidosis.

Hereditary amyloidosis associated with transthyretin V30M (ATTRv V30M) is a rare and inherited multisystemic disease, with a variable presentation and a challenging diagnosis, follow-up and treatment. This condition entails a definitive and progressive motor impairment that compromises walking ability from near onset. The detection of the latter is key for the disease's diagnosis. The aim of this work is to perform quantitative 3-D gait analysis in ATTRv V30M patients, at different disease stages, and explore the potential of the obtained gait information for supporting early diagnosis and/or stage distinction during follow-up. Sixty-six subjects (25 healthy controls, 14 asymptomatic ATTRv V30M carriers, and 27 symptomatic patients) were included in this case-control study. All subjects were asked to walk back and forth for 2 min, in front of a Kinect v2 camera prepared for body motion tracking. We then used our own software to extract gait-related parameters from the camera's 3-D body data. For each parameter, the main subject groups and symptomatic patient subgroups were statistically compared. Most of the explored gait parameters can potentially be used to distinguish between the considered group pairs. Despite of statistically significant differences being found, most of them were undetected to the naked eye. Our Kinect camera-based system is easy to use in clinical settings and provides quantitative gait information that can be useful for supporting clinical assessment during ATTRv V30M onset detection and follow-up, as well as developing more objective and fine-grained rating scales to further support the clinical decisions.

Full-body motion assessment: Concurrent validation of two body tracking depth sensors versus a gold standard system during gait.

RGB-D cameras provide 3-D body joint data in a low-cost, portable and non-intrusive way, when compared with reference motion capture systems used in laboratory settings. In this contribution, we evaluate the validity of both Microsoft Kinect versions (v1 and v2) for motion analysis against a Qualisys system in a simultaneous protocol. Two different walking directions in relation to the Kinect (towards - WT, and away - WA) were explored. For each gait trial, measures related with all body parts were computed: velocity of all joints, distance between symmetrical joints, and angle at some joints. For each measure, we compared each Kinect version and Qualisys by obtaining the mean true error and mean absolute error, Pearson's correlation coefficient, and optical-to-depth ratio. Although both Kinect v1 and v2 and/or WT and WA data present similar accuracy for some measures, better results were achieved, overall, when using WT data provided by the Kinect v2, especially for velocity measures. Moreover, the velocity and distance presented better results than angle measures. Our results show that both Kinect versions can be an alternative to more expensive systems such as Qualisys, for obtaining distance and velocity measures as well as some angles metrics (namely the knee angles). This conclusion is important towards the off-lab non-intrusive assessment of motor function in different areas, including sports and healthcare.

Automated and objective measures of gait dynamics in camptocormia Parkinson's Disease subthalamic deep brain stimulation.

OBJECTIVE: Axial motor features are common in Parkinson's disease (PD). These include gait impairment and postural abnormalities, such as camptocormia. The response of these symptoms to deep brain stimulation (DBS) is variable and difficult to assess objectively. For the first time, this study analyzes the treatment outcomes of two PD patients with camptocormia that underwent bilateral subthalamic nucleus (STN)-DBS evaluated with disruptive technologies. PATIENTS AND METHODS: Two patients with PD and camptocormia who underwent STN-DBS were included. Gait parameters were quantitatively assessed before and after surgery by using the NeuroKinect system and the camptocormia angle was measured using the camptoapp. RESULTS: After surgery, patient 1 improved 29 points in the UPDRS-III. His camptocormia angle was 68° before and 38° after surgery. Arm and knee angular amplitudes (117.32 ±â€¯7.47 vs 134.77 ±â€¯2.70°; 144.51 ±â€¯7.47 vs 169.08 ±â€¯3.27°) and arm swing (3.59 ±â€¯2.66 vs 5.40 ±â€¯1.76 cm) improved when compared with his preoperative measurements. Patient 2 improved 22 points in the UPDRS-III after surgery. Her camptocormia mostly resolved (47° before to 9° after surgery). Gait analysis revealed improvement of stride length (0.29 ±â€¯0.03 vs 0.35 ±â€¯0.03 m), stride width (18.25 ±â€¯1.16 vs 17.9 ±â€¯0.84 cm), step velocity (0.91 ±â€¯0.57 vs 1.33 ±â€¯0.48 m/s), arm swing (4.51 ±â€¯1.01 vs 7.38 ±â€¯2.71 cm) and arm and hip angular amplitudes (131.57 ±â€¯2.45° vs 137.75 ±â€¯3.18; 100.51 ±â€¯1.56 vs 102.18 ±â€¯1.77°) compared with her preoperative results. CONCLUSION: The gait parameters and camptocormia of both patients objectively improved after surgery, as assessed by the two quantitative measurement systems. STN-DBS might have a beneficial effect on controlling axial posturing and gait, being a potential surgical treatment for camptocormia in patients with PD. However, further studies are needed to derive adequate selection criteria for this patient population.

On the Fly Reporting of Human Body Movement based on Kinect v2.

Human gait analysis is of utmost importance in understanding several aspects of human movement. In clinical practice, characterizing movement in order to obtain accurate and reliable information is a major challenge, and physicians usually rely on direct observation in order to evaluate a patient's motor abilities. In this contribution, a system that can objectively analyze the patients gait and generate an on the fly, targeted and optimized gait analysis report is presented. It is an extension to an existing system that could be used without interfering with the healthcare environment, which did not provide any on the fly feedback to physicians. Patient data are acquired using Kinect v2, followed by data processing, gait specific feature extraction, ending with the generation of a quantitative on the fly report. To the best of our knowledge, the complete system fills the gap as a proper gait analysis system, i.e., a low-cost tool that can be applied without interfering with the healthcare environment, provide quantitative gait information and on the fly feedback to physicians through a motion quantification report that can be useful in multiple areas.

System for automatic gait analysis based on a single RGB-D camera.

Human gait analysis provides valuable information regarding the way of walking of a given subject. Low-cost RGB-D cameras, such as the Microsoft Kinect, are able to estimate the 3-D position of several body joints without requiring the use of markers. This 3-D information can be used to perform objective gait analysis in an affordable, portable, and non-intrusive way. In this contribution, we present a system for fully automatic gait analysis using a single RGB-D camera, namely the second version of the Kinect. Our system does not require any manual intervention (except for starting/stopping the data acquisition), since it firstly recognizes whether the subject is walking or not, and identifies the different gait cycles only when walking is detected. For each gait cycle, it then computes several gait parameters, which can provide useful information in various contexts, such as sports, healthcare, and biometric identification. The activity recognition is performed by a predictive model that distinguishes between three activities (walking, standing and marching), and between two postures of the subject (facing the sensor, and facing away from it). The model was built using a multilayer perceptron algorithm and several measures extracted from 3-D joint data, achieving an overall accuracy and F1 score of 98%. For gait cycle detection, we implemented an algorithm that estimates the instants corresponding to left and right heel strikes, relying on the distance between ankles, and the velocity of left and right ankles. The algorithm achieved errors for heel strike instant and stride duration estimation of 15 ± 25 ms and 1 ± 29 ms (walking towards the sensor), and 12 ± 23 ms and 2 ± 24 ms (walking away from the sensor). Our gait cycle detection solution can be used with any other RGB-D camera that provides the 3-D position of the main body joints.

A Grid framework for non-linear brain FMRI analysis.

Functional magnetic resonance imaging (fMRI) is an imaging technique that can be used to characterize brain physiological activity, usually presented as 3D volumes in function of time. In the context of our previous work in nonlinear association studies in electroencephalogram (EEG) time series, we were able to identify clinical relevant features useful in clinical diagnosis. The use of a similar approach in fMRI, now adapted for 3D time series, is both appealing and new. Such time series analysis imposes challenging requirements regarding computational power and medical image management. In this paper we propose a grid architecture framework to support the typical analysis protocol of association analysis applied to fMRI. The system, implemented using the gLite middleware, provides the necessary support to manage brain images and run non-linear fMRI analysis methods.

Movement Quantification in Neurological Diseases: Methods and Applications.

The movement of the human body offers neurologists important clues for the diagnosis and follow-up of many neurological diseases. The typical diagnosis approach is accomplished through simple observation of movements of interest (MOI) associated with a specific neurological disease. This approach is highly subjective because it is mainly based on qualitative evaluation of MOIs. Quantitative movement techniques are then obvious diagnosis-aid systems to approach these cases. Nevertheless, the use of motion quantification techniques in these pathologies is still relatively rare. In this paper, we intend to review this area and provide a clear picture of the current state of the art, both in the methods used and their applications to the main movement-related neurological diseases. We approach some historic aspects and the current state of the motion capture techniques and present the results of a survey to the literature that includes 82 papers, since 2006, covering the usage of these techniques in neurological diseases. Furthermore, we discuss the pros and cons of using quantitative approaches in these clinical scenarios. Finally, we present some conclusions and discuss the trends we foresee for the future.