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

Tannemaat, Martijn R.; Kefalas, Marios; Geraedts, Victor J.; Remijn-Nelissen, L.; Verschuuren, A.J.M.; Koch, Milan; Kononova, Anna V.; Wang, H.; Bäck, Thomas

doi:10.1016/j.clinph.2022.11.019

Cited by 13 publications

(6 citation statements)

References 23 publications

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“…The use of machine learning algorithms is a promising topic of study in bioengineering and Brain Machine Interfaces (BMI) fields. Recent work has explored the use of machine learning for EEG and EMG signal processing and classification [19] , [20] , [21] . Machine learning algorithms are increasingly finding application in transhumeral and transradial prostheses.…”

Section: Hardware In Contextmentioning

confidence: 99%

A low-cost robotic hand prosthesis with apparent haptic sense controlled by electroencephalographic signals

Cutipa-Puma,

Coaguila-Quispe,

Yanyachi

2023

HardwareX

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Section: Hardware In Contextmentioning

confidence: 99%

A low-cost robotic hand prosthesis with apparent haptic sense controlled by electroencephalographic signals

Cutipa-Puma,

Coaguila-Quispe,

Yanyachi

2023

HardwareX

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“…When exploring DL structures, it was found that different configurations, such as the number of neurons and "hidden" layers, had variable impacts on performance, often deviating from expected outcomes. 34 In a recent study, 63 In one study, 64 traditional ML algorithms using methods such as "gradient boosting", were employed to classify six different spontaneous activities, including complex repetitive discharges, myotonic discharges, positive sharp waves and/or fibrillation potentials, fasciculation potentials, endplate potentials, and noise artifacts. The model achieved an overall accuracy ranging from 89% to 90%.…”

Section: Emg Signal Classificationmentioning

confidence: 99%

“…In a recent study, 63 a traditional ML approach utilizing a random forest classifier (RFC) was employed to classify EMG signals into three categories: normal, neuropathic, and myopathic. The study involved collecting EMG signals during voluntary muscle contraction from 25 healthy controls, 20 ALS patients, and 20 inclusion body myositis (IBM) patients.…”

Section: Ai In Edx Medicinementioning

confidence: 99%

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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“…Based on a thorough literature study regarding the use of e.g. electromyography (EMG) for appropriate movement pattern assessment [94][95][96][97] and also based on a very wide expertise in spine disease diagnostics and treatment -we assumed that in most cases subjects with back pain were especially challenging to distinguish from the healthy reference group. 23 , where the authors showed that in some cases it is hard to distinguish healthy patterns from those affected with a disease.…”

Section: Study Limitationsmentioning

confidence: 99%

Initial study on an expert system for spine diseases screening using inertial measurement unit

Pelc,

Vilimkova Kahankova,

Blaszczyszyn

et al. 2023

Sci Rep

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In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection.

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

Cited by 13 publications

References 23 publications

A low-cost robotic hand prosthesis with apparent haptic sense controlled by electroencephalographic signals

A low-cost robotic hand prosthesis with apparent haptic sense controlled by electroencephalographic signals

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

Initial study on an expert system for spine diseases screening using inertial measurement unit

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