Prevalence of distinct types of hardware failures related to deep brain stimulation.

Deep brain stimulation (DBS) is an effective treatment of several types of neurological conditions, including Parkinson's disease, essential tremor, dystonia, and epilepsy. Despite technological progress in the past 10 years, the number of studies reporting side effects of DBS has increased, mainly due to hardware failures. This review investigated studies published between 2017 and 2021 to identify the prevalence of distinct types of hardware failures related to DBS. In total, fifteen studies were selected for the estimate of the prevalence of five distinct types of hardware failures: high impedance, fracture or failure of the lead or other parts of the implant, skin erosion and infection, lead malposition or migration, and implantable pulse generator (IPG) malfunction. The quality evaluation of the studies suggests a need to report results including populations from distinct regions of the world so that results can be generalized. The objective analysis of the prevalence of hardware failures showed that skin erosion and infection presented the highest prevalence in relation to other hardware failures. Despite the sophistication of the surgical technique of DBS over time, there is a considerable complication rate, about 7 per 100 individuals ([Formula: see text], in which CI is the confidence interval). Future research can also include correlation analysis with the aim of understanding the correlation between distinct hardware failures and variables such as gender, type of disorder, and age.

On the Effect of Vibrotactile Stimulation in Essential Tremor.

(1) Background: Vibrotactile stimulation has been studied for tremor, but there is little evidence for Essential Tremor (ET). (2) Methods: This research employed a dataset from a previous study, with data collected from 18 individuals subjected to four vibratory stimuli. To characterise tremor changes before, during, and after stimuli, time and frequency domain features were estimated from the signals. Correlation and regression analyses verified the relationship between features and clinical tremor scores. (3) Results: Individuals responded differently to vibrotactile stimulation. The 250 Hz stimulus was the only one that reduced tremor amplitude after stimulation. Compared to the baseline, the 250 Hz and random frequency stimulation reduced tremor peak power. The clinical scores and amplitude-based features were highly correlated, yielding accurate regression models (mean squared error of 0.09). (4) Conclusions: The stimulation frequency of 250 Hz has the greatest potential to reduce tremors in ET. The accurate regression model and high correlation between estimated features and clinical scales suggest that prediction models can automatically evaluate and control stimulus-induced tremor. A limitation of this research is the relatively reduced sample size.

Nonlinear EEG Analysis During Motor and Cognitive Tasks in Patients With Long COVID: A Dynamic Systems Approach.

Introduction. Nonlinear EEG provides information about dynamic properties of the brain. This study aimed to compare nonlinear EEG parameters estimated from patients with Long COVID in different cognitive and motor tasks. Materials and Methods. This 12-month prospective cohort study included 83 patients with Long COVID: 53 symptomatic and 30 asymptomatic. Brain electrical activity was evaluated by EEG in 4 situations: (1) at rest, (2) during the Trail Making Test Part A (TMT-A), (3) during the TMT Part B (TMT-B), and (4) during a coordination task: the Box and Blocks Test (BBT). Nonlinear EEG parameters were estimated in the time domain (activity and complexity). Assessments were made at 0 to 3, 3 to 6, and 6 to 12 months after inclusion. Results. There was a decrease in activity and complexity during the TMT-A and TMT-B, and an increase of these parameters during the BBT in both groups. There was an increase in activity at rest and during the TMT-A in the COVID-19 group at 0 to 3 months compared to the control, an increase in activity in the TMT-B in the COVID-19 group at 3 to 6 months compared to the control, and reduced activity and complexity at rest and during the TMT-A at 6 to 12 months compared to the control. Conclusion. The tasks followed a pattern of increased activity and complexity in cognitive tasks, which decreased during the coordination task. It was also observed that an increase in activity at rest and during cognitive tasks in the early stages, and reduced activity and complexity at rest and during cognitive tasks in the late phases of Long COVID.

Wrist Movement Variability Assessment in Individuals with Parkinson's Disease.

(1) Background: Parkinson's disease (PD) is a neurodegenerative disorder represented by the progressive loss of dopamine-producing neurons, it decreases the individual's motor functions and affects the execution of movements. There is a real need to include quantitative techniques and reliable methods to assess the evolution of PD. (2) Methods: This cross-sectional study assessed the variability of wrist RUD (radial and ulnar deviation) and FE (flexion and extension) movements measured by two pairs of capacitive sensors (PS25454 EPIC). The hypothesis was that PD patients have less variability in wrist movement execution than healthy individuals. The data was collected from 29 participants (age: 62.13 ± 9.7) with PD and 29 healthy individuals (60.70 ± 8). Subjects performed the experimental tasks at normal and fast speeds. Six features that captured the amplitude of the hand movements around two axes were estimated from the collected signals. (3) Results: The movement variability was greater for healthy individuals than for PD patients (p < 0.05). (4) Conclusion: The low variability seen in the PD group may indicate they execute wrist RUD and FE in a more restricted way. The variability analysis proposed here could be used as an indicator of patient progress in therapeutic programs and required changes in medication dosage.

Identification and Characterization of Short-Term Motor Patterns in Rest Tremor of Individuals with Parkinson's Disease.

(1) Background: The dynamics of hand tremors involve nonrandom and short-term motor patterns (STMPs). This study aimed to (i) identify STMPs in Parkinson's disease (PD) and physiological resting tremor and (ii) characterize STMPs by amplitude, persistence, and regularity. (2) Methods: This study included healthy (N = 12, 60.1 ± 5.9 years old) and PD (N = 14, 65 ± 11.54 years old) participants. The signals were collected using a triaxial gyroscope on the dorsal side of the hand during a resting condition. Data were preprocessed and seven features were extracted from each 1 s window with 50% overlap. The STMPs were identified using the clustering technique k-means applied to the data in the two-dimensional space given by t-Distributed Stochastic Neighbor Embedding (t-SNE). The frequency, transition probability, and duration of the STMPs for each group were assessed. All STMP features were averaged across groups. (3) Results: Three STMPs were identified in tremor signals (p < 0.05). STMP 1 was prevalent in the healthy control (HC) subjects, STMP 2 in both groups, and STMP3 in PD. Only the coefficient of variation and complexity differed significantly between groups. (4) Conclusion: These results can help professionals characterize and evaluate tremor severity and treatment efficacy.

Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography.

Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.