Signal-dependent wavelets for electromyogram classification

In the study, an efficient method to perform supervised classification of surface electromyogram (EMG) signals is proposed. The method is based on the choice of a relevant representation space and its optimisation with respect to a training set. As EMG signals are the summation of compact-support waveforms (the motor unit action potentials), a natural tool for their representation is the discrete dyadic wavelet transform. The feature space was thus built from the marginals of a discrete wavelet decomposition. The mother wavelet was designed to minimise the probability of classification error estimated on the learning set (supervised classification). As a representative example, the method was applied to simulate surface EMG signals generated by motor units with different degrees of short-term synchronisation. The proposed approach was able to distinguish surface EMG signals with degrees of synchronisation that differed by 10%, with a misclassification rate of 8%. The performance of a spectral-based classification (error rate approximately 33%) and of the classification with Daubechies wavelet (21%) was significantly poorer than with the proposed wavelet optimisation. The method can be used for a number of different application fields of surface EMG classification, as the feature space is adapted to the characteristics of the signal that discriminate between classes. An erratum to this article is available at .     

Wigner-Ville analysis and classification of electrocardiograms during thrombolysis

Non-stationary analysis of electrocardiograms (ECGs) using Wigner-Ville distribution is presented. Analysis was performed on subjects with acute myocardial infarction who had undergone thrombolysis, in Holter recordings of lead V1. The distinction between successfully and non-successfully thrombolysed patients was evaluated, based on time-frequency features of the Wigner-Ville transformed ECGs at the sixth hour after lysis. Characteristic parameters were extracted from time-frequency areas, and linear discriminant analysis was performed on these parameters, leading to a prediction index to distinguish the two classes. Thirteen features were found statistically significant by t-test and were used for the classification with linear modelling. Out of these features, four corresponded to frequencies lower than 25 Hz and higher than 50 Hz for, roughly, the QRS complex, five features corresponded to all the frequency bands of, roughly, the ST area, and the last four features corresponded to the T-wave. The feature-vector used in linear modelling was iteratively generated, and the iterative prediction found all 18 features significant. The iterative method resulted in better classification than that of the standard statistical procedure (3.8% error against 18.1% with the classic method). The evolution of the prediction index with time for the first 12 h was different for the successfully and non-successfully thrombolysed groups. Specifically, in the successful thrombolysis group, oscillations and variation with time were more obvious, indicating a possible difference in the dynamics of the cardiac system.     

Selecting relevant electrode positions for classification tasks based on the electro-encephalogram

The aim is to describe a general approach to determining important electrode positions when measured electro-encephalogram signals are used for classification. The approach is exemplified in the frame of the brain-computer interface, which crucially depends on the classification of different brain states. To classify two brain states, e.g. planning of movement of right and left index fingers, three different approaches are compared: classification using a physiologically motivated set of four electrodes, a set determined by principal component analysis and electrodes determined by spatial pattern analysis. Spatial pattern analysis enhances the classification rate significantly from 61.3±1.8% (with four electrodes) to 71.8±1.4%, whereas the classification rate using principal component analysis is significantly lower (65.2±1.4%). Most of the 61 electrodes used have no influence on the classification rate, so that, in future experiments, the setup can be simplified drastically to six to eight electrodes without loss of information.     

CD8+T-bet+ cells as a predominant biomarker for inclusion body myositis

Background

Myositis is a heterogeneous group of muscular auto-immune diseases with clinical and pathological criteria that allow the classification of patients into different sub-groups. Inclusion body myositis is the most frequent myositis above fifty years of age. Diagnosing inclusion body myositis requires expertise and is challenging. Little is known concerning the pathogenic mechanisms of this disease in which conventional suppressive-immune therapies are inefficacious.

股骨上端形态曲线的测量、参数化与统计分析

通过对84根完好的中国人成人股骨标本进行正位和侧位两个方向的X线摄影，得到股骨正、侧两方位的X光片。对X光片上股骨上端髓腔内侧形态进行描绘，将描绘好的图像输入计算机，由计算机进行图像预处理后提取其曲线形态数据，并将形态数据参数化，从而得到可比较的、能准确表现股骨形态的量化数据，为股骨形态的分类分析和系列型人工髋关节的参数设计打下基础。     

Presenting characteristics,comorbidities, and outcomes of patients coinfected with COVID-19 and Mycoplasma pneumoniae in the USA

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is spreading at a rapid pace, and the World Health Organization declared it as pandemic on 11 March 2020. Mycoplasma pneumoniae is an "atypical" bacterial pathogen commonly known to cause respiratory illness in humans. The coinfection from SARS-CoV-2 and mycoplasma pneumonia is rarely reported in the literature to the best of our knowledge. We present a study in which 6 of 350 patients confirmed with COVID-19 were also diagnosed with M. pneumoniae infection. In this study, we described the clinical characteristics of patients with coinfection. Common symptoms at the onset of illness included fever (six [100%] patients); five (83.3%) patients had a cough, shortness of breath, and fatigue. The other symptoms were myalgia (66.6%), gastrointestinal symptoms (33.3%-50%), and altered mental status (16.7%). The laboratory parameters include lymphopenia, elevated erythrocyte sedimentation rate, C-reactive protein, lactate dehydrogenase, interleukin-6, serum ferritin, and D-dimer in all six (100%) patients. The chest X-ray at presentation showed bilateral infiltrates in all the patients (100%). We also described electrocardiogram findings, complications, and treatment during hospitalization in detail. One patient died during the hospital course.     

Automatic analysis of immunocytochemically stained tissue samples

An automatic colour image segmentation and cell counting software system has been developed for immunocytochemical analysis of stained tissue samples. The system was designed to count the total number of positive and negative cells in tissue samples treated with cytokine DNA probes from pigs naturally parasitised with Taenia solium metacestodes, using in situ hybridisation. A reaction index was calculated as the ratio of the number of cells with a positive reaction to the total number of cells (positives plus negatives) for each of five different probes. The objectives of automatic counting were to improve the reproducibility of the analysis and reduce the processing time of large image batches. A fast KNN classifier was used for colour segmentation. Watershed segmentation combined with edge detection was used to isolate individual cells that were then automatically labelled, using the results of the corresponding colour segmented image. Validation was performed on 122 non-training digital images with a total of 1069 positive cells and 1459 negative cells, with the following results: a mean true positive rate of 90.2% for positive cells and a mean true positive rate of 85.4% for negative cells. The corresponding mean false positive rates were 9.6% and 6.6%. The mean reaction index error of the automatic analysis was 5.35%. The processing of each digital image took 10 s on a Pentium IV PC.     

Mediastinal lymphatic efferents from the diaphragm

The prognosis of non small cell lung cancer (NSCLC) invading the diaphragm is poor, probably due to the richness of the lymphatic drainage of the diaphragm. The aim of this study was to determine mediastinal lymphatic efferents from the diaphragm. The diaphragms of 20 adult cadavers (77-104 years) were injected with a dye (modified Gerota's medium) to permit the lymph vessels to be catheterised and then dissected. Each stage of the dissection was described and photographed: 23 injections on the right and 25 on the left. Diaphragmatic lymph vessels passed to one of three lymph centres: posterior (paraaortic nodes, n = 16), anterior (juxtasternal nodes, n = 16) and mediastinal (visceral nodes, n = 16). From these lymph centres arose ascending lymph pathways: posteriorly to the thoracic duct (8/16), anteriorly along the internal thoracic vessels (10/16) and in the mediastinum to the peritracheobronchial nodes (6/10). Lymphatics from the diaphragm are abundant and drain towards mediastinal node lymph centres connecting to the blood stream via the thoracic duct. These lymph pathways are common with those of the pulmonary segments. Poor prognosis of NSCLC invading the diaphragm may be explained by the common lymphatic drainage of both the lung and diaphragm.