Introduction to this Special Issue: Intelligent Data Analysis on Electromyography and Electroneurography

Computer-aided electromyography (EMG) and elec- troneurography (ENG) have become indispensable tools in the daily activities of neurophysiology laboratories in facilitating quantitative analysis and decision making in clinical neurophysiology, rehabilitation, sports medicine, and studies of human physiology. These tools form the basis of a new era in the practice of neurophysiology facilitating the: (i) Standardization . Diagnoses obtained with similar criteria in different laboratories can be veri- fied. (ii) Sensitivity . Neurophysiological findings in a particular subject under investigation may be compared with a database of normal values to determine whether abnormality exists or not. (iii) Specificity . Findings may be compared with databases derived from patients with known diseases, to evaluate whether they fit a specific diagnosis. (iv) Equivalence . Results from serial examin- ations on the same patient may be compared to decide whether there is evidence of disease progression or of response to treatment. Also, findings obtained from dif- ferent quantitative methods may be contrasted to deter- mine which are most sensitive and specific. Different methodologies have been developed in com- puter-aided EMG and ENG analysis ranging from simple quantitative measures of the recorded potentials, to more complex knowledge-based and neural network systems that enable the automated assessment of neuromuscular disorders. However, the need still exists for the further advancement and standardization of these method- ologies, especially nowadays with the emerging health telematics technologies which will enable their wider application in the neurophysiological laboratory. The main objective of this Special Issue of Medical Engin- eering & Physics is to provide a snapshot of current activities and methodologies in intelligent data analysis in peripheral neurophysiology. A total of 12 papers are published in this Special Issue under the following topics: Motor Unit Action Potential (MUAP) Analysis, Surface EMG (SEMG) Analysis, Electroneurography, and Decision Systems. In this intro- duction, the papers are briefly introduced, following a brief review of the major achievements in quantitative electromyography and electroneuropathy

Imaging biomarkers in the idiopathic inflammatory myopathies

Idiopathic inflammatory myopathies (IIMs) are a group of acquired muscle diseases with muscle inflammation, weakness, and other extra-muscular manifestations. IIMs can significantly impact the quality of life, and management of IIMs often requires a multi-disciplinary approach. Imaging biomarkers have become an integral part of the management of IIMs. Magnetic resonance imaging (MRI), muscle ultrasound, electrical impedance myography (EIM), and positron emission tomography (PET) are the most widely used imaging technologies in IIMs. They can help make the diagnosis and assess the burden of muscle damage and treatment response. MRI is the most widely used imaging biomarker of IIMs and can assess a large volume of muscle tissue but is limited by availability and cost. Muscle ultrasound and EIM are easy to administer and can even be performed in the clinical setting, but they need further validation. These technologies may complement muscle strength testing and laboratory studies and provide an objective assessment of muscle health in IIMs. Furthermore, this is a rapidly progressing field, and new advances are going to equip care providers with a better objective assessment of IIMS and eventually improve patient management. This review discusses the current state and future direction of imaging biomarkers in IIMs

Probabilistic Characterization of Neuromuscular Disease: Effects of Class Structure and Aggregation Methods

Neuromuscular disorders change the underlying structure and function of motor units within a muscle, and are detected using needle electromyography. Currently, inferences about the presence or absence of disease are made subjectively and are largely impression-based. Quantitative electromyography (QEMG) attempts to improve upon the status quo by providing greater levels of precision, objectivity and reproducibility through numeric analysis, however, their results must be transparently presented and explained to be clinically viable. The probabilistic muscle characterization (PMC) model is ideally suited for a clinical decision support system (CDSS) and has many analogues to the subjective analysis currently used. To improve disease characterization performance globally, a hierarchical classification strategy is developed that accounts for the wide range of MUP feature values present at different levels of involvement (LOI) of a disorder. To improve utility, methods for detecting LOI are considered that balance the accuracy in reporting LOI with its clinical utility. Finally, several aggregation methods that represent commonly used human decision-making strategies are considered and evaluated for their suitability in a CDSS. Four aggregation measures (Average, Bayes, Adjusted Bayes, and WMLO) are evaluated, that offer a compromise between two common decision making paradigms: conservativeness (average) and extremeness (Bayes). Standard classification methods have high specificity at a cost of poor sensitivity at low levels of disease involvement, but tend to improve with disease progression. The hierarchical model is able to provide a better balance between low-LOI sensitivity and specificity by providing the classifier with more concise definitions of abnormality due to LOI. Furthermore, a method for detecting two discrete levels of disease involvement (low and high) is accomplished with reasonable accuracy. The average aggregation method offers a conservative decision that is preferred when the quality of the evidence is poor or not known, while the more extreme aggregators such as Bayes rule perform optimally when the evidence is accurate, but underperform otherwise due to outlier values that are incorrect. The methods developed offer several improvements to PMC, by providing a better balance between sensitivity and specificity, through the definition of a clinically useful and accurate measure of LOI, and by understanding conditions for which each of the aggregation measures is better suited. These developments will enhance the quality of decision support offered by QEMG techniques, thus improving the diagnosis, treatment and management of neuromuscular disorders

Deep Learning Based Abnormal Gait Classification System Study with Heterogeneous Sensor Network

Gait is one of the important biological characteristics of the human body. Abnormal gait is mostly related to the lesion site and has been demonstrated to play a guiding role in clinical research such as medical diagnosis and disease prevention. In order to promote the research of automatic gait pattern recognition, this paper introduces the research status of abnormal gait recognition and systems analysis of the common gait recognition technologies. Based on this, two gait information extraction methods, sensor-based and vision-based, are studied, including wearable system design and deep neural network-based algorithm design. In the sensor-based study, we proposed a lower limb data acquisition system. The experiment was designed to collect acceleration signals and sEMG signals under normal and pathological gaits. Specifically, wearable hardware-based on MSP430 and upper computer software based on Labview is designed. The hardware system consists of EMG foot ring, high-precision IMU and pressure-sensitive intelligent insole. Data of 15 healthy persons and 15 hemiplegic patients during walking were collected. The classification of gait was carried out based on sEMG and the average accuracy rate can reach 92.8% for CNN. For IMU signals five kinds of abnormal gait are trained based on three models: BPNN, LSTM, and CNN. The experimental results show that the system combined with the neural network can classify different pathological gaits well, and the average accuracy rate of the six-classifications task can reach 93%. In vision-based research, by using human keypoint detection technology, we obtain the precise location of the key points through the fusion of thermal mapping and offset, thus extracts the space-time information of the key points. However, the results show that even the state-of-the-art is not good enough for replacing IMU in gait analysis and classification. The good news is the rhythm wave can be observed within 2 m, which proves that the temporal and spatial information of the key points extracted is highly correlated with the acceleration information collected by IMU, which paved the way for the visual-based abnormal gait classification algorithm.步态指人走路时表现出来的姿态，是人体重要生物特征之一。异常步态多与病变部位有关，作为反映人体健康状况和行为能力的重要特征，其被论证在医疗诊断、疾病预防等临床研究中具有指导作用。为了促进步态模式自动识别的研究，本文介绍了异常步态识别的研究现状，系统地分析了常见步态识别技术以及算法，以此为基础研究了基于传感器与基于视觉两种步态信息提取方法，内容包括可穿戴系统设计与基于深度神经网络的算法设计。 在基于传感器的研究中，本工作开发了下肢步态信息采集系统，并利用该信息采集系统设计实验，采集正常与不同病理步态下的加速度信号与肌电信号，搭建深度神经网络完成分类任务。具体的，在系统搭建部分设计了基于MSP430的可穿戴硬件设备以及基于Labview的上位机软件，该硬件系统由肌电脚环，高精度IMU以及压感智能鞋垫组成，该上位机软件接收、解包蓝牙数据并计算出步频步长等常用步态参数。 在基于运动信号与基于表面肌电的研究中，采集了15名健康人与15名偏瘫病人的步态数据，并针对表面肌电信号训练卷积神经网络进行帕金森步态的识别与分类，平均准确率可达92.8%。针对运动信号训练了反向传播神经网络，LSTM以及卷积神经网络三种模型进行五种异常步态的分类任务。实验结果表明，本工作中步态信息采集系统结合神经网络模型，可以很好地对不同病理步态进行分类，六分类平均正确率可达93%。 在基于视觉的研究中，本文利用人体关键点检测技术，首先检测出图片中的一个或多个人，接着对边界框做图像分割，接着采用全卷积resnet对每一个边界框中的人物的主要关节点做热力图并分析偏移量，最后通过热力图与偏移的融合得到关键点的精确定位。通过该算法提取了不同步态下姿态关键点时空信息，为基于视觉的步态分析系统提供了基础条件。但实验结果表明目前最高准确率的人体关键点检测算法不足以替代IMU实现步态分析与分类。但在2m之内可以观察到节律信息，证明了所提取的关键点时空信息与IMU采集的加速度信息呈现较高相关度，为基于视觉的异常步态分类算法铺平了道路

Advanced bioelectrical signal processing methods: Past, present and future approach - Part III: Other biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).Web of Science2118art. no. 606