Shoulder pain and concomitant hand oedema among stroke patients with pronounced arm paresis.
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ABSTRACT:
SUBMITTER: Isaksson M
PROVIDER: S-EPMC3990940 | biostudies-literature | 2013 Dec
REPOSITORIES: biostudies-literature
Publications
Shoulder pain and concomitant hand oedema among stroke patients with pronounced arm paresis.
Isaksson Mats M Johansson Lars L Olofsson Ingrid I Eurenius Eva E
European journal of physiotherapy 20131008 4
<h4>Background</h4>The aim of this prospective study was to identify clinical factors associated with the development of shoulder pain in stroke patients with pronounced arm paresis.<h4>Methods</h4>At stroke onset, 485 patients were initially assessed in 2007-2009. Sixty-three patients with pronounced arm paresis completed the study, and 21 of these developed shoulder pain. Clinical findings were recorded fortnightly by the attending physiotherapist during hospital stay.<h4>Results</h4>Hand oede ...[more]
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Project description:Recent advances in stroke treatment have provided effective tools to successfully treat ischemic stroke, but still a majority of patients are not treated due to late arrival to hospital. With modern stroke treatment, earlier arrival would greatly improve the overall treatment results. This prospective study was performed to asses the capability of bilateral accelerometers worn in bracelets 24/7 to detect unilateral arm paralysis, a hallmark symptom of stroke, early enough to receive treatment. Classical machine learning algorithms as well as state-of-the-art deep neural networks were evaluated on detection times between 15 min and 120 min. Motion data were collected using triaxial accelerometer bracelets worn on both arms for 24 h. Eighty-four stroke patients with unilateral arm motor impairment and 101 healthy subjects participated in the study. Accelerometer data were divided into data windows of different lengths and analyzed using multiple machine learning algorithms. The results show that all algorithms performed well in separating the two groups early enough to be clinically relevant, based on wrist-worn accelerometers. The two evaluated deep learning models, fully convolutional network and InceptionTime, performed better than the classical machine learning models with an AUC score between 0.947-0.957 on 15 min data windows and up to 0.993-0.994 on 120 min data windows. Window lengths longer than 90 min only marginally improved performance. The difference in performance between the deep learning models and the classical models was statistically significant according to a non-parametric Friedman test followed by a post-hoc Nemenyi test. Introduction of wearable stroke detection devices may dramatically increase the portion of stroke patients eligible for revascularization and shorten the time to treatment. Since the treatment effect is highly time-dependent, early stroke detection may dramatically improve stroke outcomes.
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Project description:The Disability of the Arm, Shoulder, and Hand (DASH) questionnaire was originally designed as a measure of disability in patients with disorders of the upper limb, but the DASH score is also affected by disability because of lower limb disorders. The aim of this study was to investigate the construct validity of the DASH questionnaire and to create a revised DASH questionnaire, the Manchester-modified or M2 DASH, with fewer questions that is more specific to the upper limb. Patients were asked to fill in the DASH questionnaire in a fracture clinic after ethical approval. This included 79 patients with upper limb injuries, 61 patients with lower limb injuries, and 52 control subjects. The mean DASH scores for the three groups varied significantly, and the lower limb group had a mean score of 16. The M2 DASH questionnaire was developed using questions more specific to the upper limb and included questions 1-4, 6, 13-17, 21-23, and 26-30 from the original questionnaire. The mean M2 DASH score for the lower limb group was 9 and, unlike the original DASH score, was not statistically different from the control group. The M2 DASH scores were then calculated for the upper limb group and a correlation study showed highly significant correlation between the original DASH scores and the M(2) DASH scores. Our study shows that the original DASH questionnaire is not specific for the upper limb. The M2 DASH questionnaire has the advantage of being more specific for the upper limb than the DASH questionnaire, and it correlates well with the original DASH questionnaire when looking at isolated upper limb injuries.
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Project description: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.
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