Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach

ObjectiveDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.MethodsEMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM). A machine learning pipeline was applied based on 5-second EMG fragments of each muscle. Diagnostic yield expressed as area under the curve (AUC) of a receiver-operator characteristics curve, accuracy, sensitivity, and specificity were determined per muscle (muscle-level) and per patient (patient-level).ResultsDiagnostic yield of the classification ALS vs. HC was: AUC 0.834 ± 0.014 at muscle-level and 0.856 ± 0.009 at patient-level. For the classification HC vs. IBM, AUC was 0.744 ± 0.043 at muscle-level and 0.735 ± 0.029 at patient-level. For the classification ALS vs. IBM, AUC was 0.569 ± 0.024 at muscle-level and 0.689 ± 0.035 at patient-level.ConclusionsAn automated time series classification algorithm can distinguish EMGs from healthy individuals from those of patients with ALS with a high diagnostic yield. Using longer EMG fragments with different levels of muscle activation may improve performance.Neurological Motor Disorder

Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach

OBJECTIVE\nMETHODS\nRESULTS\nCONCLUSIONS\nSIGNIFICANCE\nDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.\nEMGs of healthy controls (HC, n = 25), patients with amyotrophic lateral sclerosis (ALS, n = 20) and inclusion body myositis (IBM, n = 20), were retrospectively selected based on longitudinal clinical follow-up data (ALS and HC) or muscle biopsy (IBM). A machine learning pipeline was applied based on 5-second EMG fragments of each muscle. Diagnostic yield expressed as area under the curve (AUC) of a receiver-operator characteristics curve, accuracy, sensitivity, and specificity were determined per muscle (muscle-level) and per patient (patient-level).\nDiagnostic yield of the classification ALS vs. HC was: AUC 0.834 ± 0.014 at muscle-level and 0.856 ± 0.009 at patient-level. For the classification HC vs. IBM, AUC was 0.744 ± 0.043 at muscle-level and 0.735 ± 0.029 at patient-level. For the classification ALS vs. IBM, AUC was 0.569 ± 0.024 at muscle-level and 0.689 ± 0.035 at patient-level.\nAn automated time series classification algorithm can distinguish EMGs from healthy individuals from those of patients with ALS with a high diagnostic yield. Using longer EMG fragments with different levels of muscle activation may improve performance.\nIn the future, machine learning algorithms may help improve the diagnostic accuracy of EMG examinations.Algorithms and the Foundations of Software technolog

Age-related changes in global motion coherence: conflicting haemodynamic and perceptual responses

Our aim was to use both behavioural and neuroimaging data to identify indicators of perceptual decline in motion processing. We employed a global motion coherence task and functional Near Infrared Spectroscopy (fNIRS). Healthy adults (n = 72, 18-85) were recruited into the following groups: young (n = 28, mean age = 28), middle-aged (n = 22, mean age = 50), and older adults (n = 23, mean age = 70). Participants were assessed on their motion coherence thresholds at 3 different speeds using a psychophysical design. As expected, we report age group differences in motion processing as demonstrated by higher motion coherence thresholds in older adults. Crucially, we add correlational data showing that global motion perception declines linearly as a function of age. The associated fNIRS recordings provide a clear physiological correlate of global motion perception. The crux of this study lies in the robust linear correlation between age and haemodynamic response for both measures of oxygenation. We hypothesise that there is an increase in neural recruitment, necessitating an increase in metabolic need and blood flow, which presents as a higher oxygenated haemoglobin response. We report age-related changes in motion perception with poorer behavioural performance (high motion coherence thresholds) associated with an increased haemodynamic response

Structural and functional evolution of the P2Y12-like receptor group

Metabotropic pyrimidine and purine nucleotide receptors (P2Y receptors) belong to the superfamily of G protein-coupled receptors (GPCR). They are distinguishable from adenosine receptors (P1) as they bind adenine and/or uracil nucleotide triphosphates or diphosphates depending on the subtype. Over the past decade, P2Y receptors have been cloned from a variety of tissues and species, and as many as eight functional subtypes have been characterized. Most recently, several members of the P2Y12-like receptor group, which includes the clopidogrel-sensitive ADP receptor P2Y12, have been deorphanized. The P2Y12-like receptor group comprises several structurally related GPCR which, however, display heterogeneous agonist specificity including nucleotides, their derivatives, and lipids. Besides the established function of P2Y12 in platelet activation, expression in macrophages, neuronal and glial cells as well as recent results from functional studies implicate that several members of this group may have specific functions in neurotransmission, inflammation, chemotaxis, and response to tissue injury. This review focuses specifically on the structure-function relation and shortly summarizes some aspects of the physiological relevance of P2Y12-like receptor members

The effectiveness and side effects of pyridostigmine in the treatment of myasthenia gravis: a cross-sectional study

Pyridostigmine is the most commonly used drug in the symptomatic treatment of myasthenia gravis (MG); however, research into its effectiveness and side effects is scarce. The aim of this study was to assess the effectiveness, prevalence of side effects and net benefit of pyridostigmine. All MG patients participating in the Dutch-Belgian myasthenia patient registry were included. A dynamic online questionnaire was developed to assess the effectiveness, side effects and net benefit of pyridostigmine. Out of 642 invited patients, 410 patients (64%) fully completed the questionnaire; 61% reported that they currently used pyridostigmine, 36% had discontinued pyridostigmine and 2% reported to never have used pyridostigmine. Patients reported a median effectiveness of 60, IQR 28-78 and net benefit of 65, IQR 45-84. Of all patients currently using pyridostigmine, 91% reported side effects (vs. 55% in the control group). Most frequently reported side effects were flatulence, urinary urgency, muscle cramps, blurred vision and hyperhidrosis. In the group of patients who discontinued pyridostigmine, side effects were the reason for discontinuation in 26%. Diarrhea, abdominal cramps and muscle twitching were the most frequently cited reasons to discontinue pyridostigmine. These results can be used to guide shared decision making prior to starting symptomatic treatment for MG.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )Neurological Motor Disorder

Emergent technologies in mixed and multimethod research: incorporating mobile technologies

This chapter presents an overview of the developments and potentials of mobile technologies and their major impact on society and the daily activities of individuals. The increase of sensors embedded in everyday objects enable these objects to sense the environment and communicate. This creates new possibilities to gather and process large amounts of data. We show how these opportunities can trigger a paradigm shift in the social sciences. Social scientists no longer collect data but use data that is available and collected for other reasons. These data will vary in validity and quality. It can come from sensors in personal mobile devices, smart environments, or social infrastructures. This asks a strong interpretive approach from multimethod- and mixed methods researches to harness these data