OBJECTIVE\nMETHODS\nRESULTS\nCONCLUSIONS\nSIGNIFICANCE\nDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time... Show moreOBJECTIVE\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. Show less
Predictive maintenance (PdM) is a maintenance policy that uses the past, current, and prognosticated health condition of an asset to predict when timely maintenance should occur. PdM overcomes... Show morePredictive maintenance (PdM) is a maintenance policy that uses the past, current, and prognosticated health condition of an asset to predict when timely maintenance should occur. PdM overcomes challenges of more conservative policies, such as corrective or scheduled maintenance. The remaining useful life (RUL) is a critical notion in PdM that determines the time remaining until a system is no longer useful and requires maintenance. Among the approaches employed to estimate the RUL, data-driven PdM methods have shown to be a good candidate due to their (mostly) domain-agnostic nature and broad applicability mos on the asset’s generated data. Nevertheless, there are various challenges to consider in data-driven PdM, such as algorithm selection, hyperparameter optimization, and uncertainty of the RUL estimation. This thesis proposes solutions and frameworks for these challenges using simulated datasets. We furthermore dive into scheduling optimization which is the next step in PdM and point towards the importance of understanding the data generating process in PdM using real-world data. Finally, we show how a method originally developed for PdM in the automotive industry can lend itself to the medical domain, exhibiting the significance of knowledge-transfer and the versatility of data-driven methods. Show less
Tannemaat, M.R.; Kefalas, M.; Geraedts, V.J.; Remijn-Nelissen, L.; Verschuuren, A.J.M.; Koch, M.; ... ; Bäck, T.H.W. 2022
ObjectiveDistinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm.MethodsEMGs of... Show moreObjectiveDistinguishing 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. Show less
Kefalas, M.; Stein, B. van; Baratchi, M.; Apostolidis, A.; Bäck, T.H.W. 2022
Background Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine... Show moreBackground Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha-synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline.Objective To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS.Methods Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Disorder Society criteria classified patients as cognitively stable or deteriorated at 1-year follow-up. A total of 16,674 EEG-features were extracted per patient; a Boruta algorithm selected EEG-features to reflect representative neurophysiological signatures for each class. A random forest classifier with 10-fold cross-validation with Bayesian optimization provided class-differentiation.Results Tweny-five patients were classified as cognitively stable and 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predictive value of 91.4% (95% CI 82.9, 95.9) and negative predictive value of 85.0% (95% CI 81.9, 91.4). Predicted probabilities between classes were highly differential (hazard ratio 11.14 [95% CI 7.25, 17.12]); the risk of cognitive decline in patients with high probabilities of being prognosticated as cognitively stable (>0.5) was very limited.Conclusions Preoperative EEGs can predict cognitive deterioration after STN DBS with high accuracy. Cortical neurophysiological alterations may indicate future cognitive decline and can be used as biomarkers during the DBS screening. (c) 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society Show less
Kefalas, M.; Koch, M.; Geraedts, V.J.; Wang, H.; Tannemaat, M.; Bäck, T.H.W. 2020
Needle electromyography (EMG) is a common technique used in clinical neurophysiology to record the electrical activity of muscles at different levels of activation. It can be used to diagnose... Show moreNeedle electromyography (EMG) is a common technique used in clinical neurophysiology to record the electrical activity of muscles at different levels of activation. It can be used to diagnose various neurological/muscular disorders, as the EMG signals of patients with both nerve diseases (neuropathies) andmuscle diseases (myopathies) differ from the signal in healthy controls. A major drawback of this examination is that it relies on visual inspection and as such, it is highly subjective and prone to errors. Based on EMG time series of 65 individuals (40 with ALS/IBM and 25 healthy), we aim to develop an automated machine-learning pipeline for the classification of EMG recordings of muscles in either disease or healthy (muscle-level). The automated pipeline consists of feature extraction, feature selection, modelling algorithm, and optimization, in which the most significant features are automatically selected from the feature space and the hyperparameters of the model are optimized by a Bayesian technique as part of the automatedapproach. Aside from the muscle-level approach, we also explore a patient-level approach, which uses the output of the muscle-level automated pipeline in a post-processing manner to classify patients in being either disease or healthy, based on their muscle recordings. The resulting two approaches yield an AUC scoreof 81.7% (muscle-level) and 81.5% (patient-level), indicating that such approaches can assist clinicians in diagnosing if a patient has a neuropathy/myopathy or is healthy. Show less
Nguyen, D.V.; Kefalas, M.; Yang, K.; Apostolidis, A.; Olhofer, M.; Limmer, S.; Bäck, T.H.W. 2019
Prognostics and Health Management (PHM) attracts increasing interest of many researchers due to its potentially important applications in diverse disciplines and industries. In general, PHM systems... Show morePrognostics and Health Management (PHM) attracts increasing interest of many researchers due to its potentially important applications in diverse disciplines and industries. In general, PHM systems use real-time and historical state information of subsystems and components of the operating systems to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. Every year, a substantial number of papers in this area including theory and practical applications, appear in academic journals, conference proceedings and technical reports. This paper aims to summarize and review researches, developments and recent contributions in PHM for automotive- and aerospace industries. It can also be considered as the starting point for researchers and practitioners in general to assist them through PHM implementation and help them to accomplish their work more easily. Show less
