1. Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach.
- Author
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Tannemaat, M.R., Kefalas, M., Geraedts, V.J., Remijn-Nelissen, L., Verschuuren, A.J.M., Koch, M., Kononova, A.V., Wang, H., and Bäck, T.H.W.
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MACHINE learning , *INCLUSION body myositis , *AMYOTROPHIC lateral sclerosis , *CLASSIFICATION algorithms , *TIME series analysis - Abstract
• A machine learning algorithm can differentiate EMGs from healthy individuals from patients with ALS with a high diagnostic yield. • The automated approach aimed at limiting all arbitrary choices with regards to epoch selection and hyperparameter optimization. • This algorithm allows the identification of features used for classification, allowing interpretation of the model. Distinguishing normal, neuropathic and myopathic electromyography (EMG) traces can be challenging. We aimed to create an automated time series classification algorithm. EMGs 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). Diagnostic 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. An 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. In the future, machine learning algorithms may help improve the diagnostic accuracy of EMG examinations. [ABSTRACT FROM AUTHOR] more...
- Published
- 2023
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