Automated Machine Learning for the Classification of Normal and Abnormal Electromyography Data

Kefalas, Marios; Koch, Milan; Geraedts, Victor J.; Wang, Hao; Tannemaat, Martijn R.; Bäck, Thomas

doi:10.1109/bigdata50022.2020.9377780

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…Various features have been utilized as input for different ML algorithms, resulting in variable performance outcomes, 42,43 as can be seen in Table S1. 34,35,39,[42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59] The classification performance varied when distinguishing between normal and myopathic, normal and neuropathic, or myopathic and neuropathic conditions. For instance, TD and FD techniques with K-nearest neighbors as a classifier, can achieve 100% accuracy in a small dataset differentiating ALS from normal, but only 66% accuracy in differentiating myopathy from normal.…”

Section: Emg Signal Classificationmentioning

confidence: 99%

“…For instance, TD and FD techniques with K-nearest neighbors as a classifier, can achieve 100% accuracy in a small dataset differentiating ALS from normal, but only 66% accuracy in differentiating myopathy from normal. 44 A myriad of ML, [45][46][47][48][49] and DL 50,51,[57][58][59] techniques have also been used with variable performance accuracy.…”

Section: Emg Signal Classificationmentioning

confidence: 99%

“…Various features have been utilized as input for different ML algorithms, resulting in variable performance outcomes, 42,43 as can be seen in Table S1 34,35,39,42–59 . The classification performance varied when distinguishing between normal and myopathic, normal and neuropathic, or myopathic and neuropathic conditions.…”

Section: Ai In Edx Medicinementioning

confidence: 99%

See 2 more Smart Citations

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions

Taha,

Morren

2023

Muscle and Nerve

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The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.

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Section: Emg Signal Classificationmentioning

confidence: 99%

Section: Emg Signal Classificationmentioning

confidence: 99%

See 1 more Smart Citation

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions

Taha,

Morren

2023

Muscle and Nerve

View full text Add to dashboard Cite

show abstract

“…There are six machine learning classifiers, i.e., support vector machine (SVM), random forest (RF) decision tree (DT), logistic regression (LR), naive bayes (NB) and ridge classifier (RC) are comparison and different hyperparameters methods tuning are employed Elgeldawi et al [18]. Development the automatic machine learning to classification EMG signal that extracted with needle electrode based on hyper-parameters optimization for healthy and disease muscles Kefalas et al [19]. The classification of EMG pick up from forearm using MYO armband and the training dataset's 5-fold cross-validation methods are used to choose the hyperparamet ers for classifiers [20].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of machine learning algorithms on myoelectric signal from intact and transradial amputated limbs

Kadhim

Raheema

Hussein

2023

IJ-AI

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Control strategies of smart hand prosthesis-based myoelectric signals in recent years don't provide the patients with the sensation of biological control of prostheses hand fingers. Therefore, in current work hyperparameters optimization in machine learning algorithm and hand gesture recognition techniques were applied to the myoelectric signal-based on residual muscles contraction of the amputees corresponding to intact forearm limb movement to improve their biological control. In this paper, myoelectric signals are extracted using the MYO armband to recognize ten gestures from ten volunteers (healthy and transradial amputation) on the forearm, thereafter the noise of myoelectric signals using a notch filter (NF) is removed. The proposed classification system involved two machine learning algorithms: (1) the decision tree (DT), tri-layered neural network (TLNN), k-nearest-neighbor (KNN), support vector machine (SVM) and ensemble boosted tree (EBT) classifiers. (2) the optimized machine learning classifiers, i.e., OKNN, OSVM, OEBT with optical diffraction tomography (ODT) and ommatidia detecting algorithm (ODA). The experimental results of classifiers comparison pointed out an algorithm that outperformed with high accuracy is OEBT closely followed by OKNN achieves an accuracy of 97.8% and 97.1% for intact forearm limb, while for transradial amputation with an accuracy of 91.9% and 91.4%, respectively.

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

Tannemaat¹,

Kefalas²,

Geraedts³

et al. 2023

Clinical Neurophysiology

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Automated Machine Learning for the Classification of Normal and Abnormal Electromyography Data

Cited by 4 publications

References 24 publications

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions

Comparative analysis of machine learning algorithms on myoelectric signal from intact and transradial amputated limbs

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

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