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Stroke rehabilitation with mechanical assistance improves outcomes by facilitating repetition and relieving the care burden of therapy staff. Here, we tested the Medical Care Pit (MCP) walking assistance training device in the rehabilitation of eight acute stroke patients (median age 60.7 ± 16.3 years) who had recently suffered ischemic (three) or hemorrhagic (five) stroke (14.1 ± 6.5 days). Patients received standard rehabilitation approximately 5 days per week (weekdays only), plus MCP therapy twice a week, totaling four MCP sessions over 2 weeks. Fugl–Meyer Assessment-Lower Extremities (FMA-LE), Functional Ambulation Category (FAC), and other gait-associated parameters were measured. Over the 10.5 ± 1.6 days of therapy, MCP qualitatively assisted in gait analysis and real-time patient feedback while independent walking scores significantly improved (FAC 2.2 ± 0.8 to 3.1 ± 1.3, p = 0.020). FMA-LE scores also slightly improved but not to significance (p = 0.106). Objective burden on patients, as measured by modified Borg scale, was significantly improved (2.7 ± 1.6 to 2.0 ± 1.6, p = 0.014). In terms of questionnaires, anxiety scores for the physical therapist regarding gait training and falling with MCP significantly decreased (3.8 ± 2.3 to 1.0 ± 1.6; p = 0.027 and 3.1 ± 2.2 to 0.8 ± 1.3; p = 0.045) from the first to fourth sessions. Taken together, MCP, in addition to the usual rehabilitation program, was effective in gait rehabilitation for independent walking and relieved burdens on the patients. Such walking support systems may be an important part of acute stroke rehabilitation. [ABSTRACT FROM AUTHOR]

In myoelectrical pattern recognition (PR), the feature extraction methods for stroke-oriented applications are challenging and remain discordant due to a lack of hemiplegic data and limited knowledge of skeletomuscular function. Additionally, technical and clinical barriers create the need for robust, subject-independent feature generation while using supervised learning (SL). To the best of our knowledge, we are the first study to investigate the brute-force analysis of individual and combinational feature vectors for acute stroke gesture recognition using surface electromyography (EMG) of 19 patients. Moreover, post-brute-force singular vectors were concatenated via a Fibonacci-like spiral net ranking as a novel, broadly applicable concept for feature selection. This semi-brute-force navigated amalgamation in linkage (SNAiL) of EMG features revealed an explicit classification rate performance advantage of 10–17% compared to canonical feature sets, which can drastically extend PR capabilities in biosignal processing. [ABSTRACT FROM AUTHOR]

In clinical practice, acute post-stroke paresis of the extremities fundamentally complicates timely rehabilitation of motor functions; however, recently, residual and distorted musculoskeletal signals have been used to initiate feedback-driven solutions for establishing motor rehabilitation. Here, we investigate the possibilities of basic hand gesture recognition in acute stroke patients with hand paresis using a novel, acute stroke, four-component multidomain feature set (ASF-4) with feature vector weight additions (ASF-14NP, ASF-24P) and supervised learning algorithms trained only by surface electromyography (sEMG). A total of 19 (65.9 ± 12.4 years old; 12 men, seven women) acute stroke survivors (12.4 ± 6.3 days since onset) with hand paresis (Brunnstrom stage 4 ± 1/4 ± 1, SIAS 3 ± 1/3 ± 2, FMA-UE 40 ± 20) performed 10 repetitive hand movements reflecting basic activities of daily living (ADLs): rest, fist, pinch, wrist flexion, wrist extension, finger spread, and thumb up. Signals were recorded using an eight-channel, portable sEMG device with electrode placement on the forearms and thenar areas of both limbs (four sensors on each extremity). Using data preprocessing, semi-automatic segmentation, and a set of extracted feature vectors, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbors (k-NN) classifiers for statistical comparison and validity (paired t-tests, p-value < 0.05), we were able to discriminate myoelectrical patterns for each gesture on both paretic and non-paretic sides. Despite any post-stroke conditions, the evaluated total accuracy rate by the 10-fold cross-validation using SVM among four-, five-, six-, and seven-gesture models were 96.62%, 94.20%, 94.45%, and 95.57% for non-paretic and 90.37%, 88.48%, 88.60%, and 89.75% for paretic limbs, respectively. LDA had competitive results using PCA whereas k-NN was a less efficient classifier in gesture prediction. Thus, we demonstrate partial efficacy of the combination of sEMG and supervised learning for upper-limb rehabilitation procedures for early acute stroke motor recovery and various treatment applications. [ABSTRACT FROM AUTHOR]

Introduction: We hypothesized that an endovascular electroencephalogram (eEEG) can detect subdural electrode (SDE)-detectable, scalp EEG-undetectable epileptiform discharges. The purpose of this study is, therefore, to measure SDE-detectable, scalp EEG-undetectable epileptiform discharges by an eEEG on a pig. Methods: A pig under general anesthesia was utilized to measure an artificially generated epileptic field by an eEEG that was able to be detected by an SDE, but not a scalp EEG as a primary outcome. We also compared the phase lag of each epileptiform discharge that was detected by the eEEG and SDE as a secondary outcome. Results: The eEEG electrode detected 113 (97%) epileptiform discharges (97% sensitivity). Epileptiform discharges that were localized within the three contacts (contacts two, three and four), but not spread to other parts, were detected by the eEEG with a 92% sensitivity. The latency between peaks of the eEEG and right SDE earliest epileptiform discharge ranged from 0 to 48 ms (mean, 13.3 ms; median, 11 ms; standard deviation, 9.0 ms). Conclusion: In a pig, an eEEG could detect epileptiform discharges that an SDE could detect, but that a scalp EEG could not. [ABSTRACT FROM AUTHOR]