Classification of EMG Signals for Assessment of Neuromuscular Disorders

Goen, Anjana

doi:10.12720/ijeee.2.3.242-248

Cited by 25 publications

(9 citation statements)

References 17 publications

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“…Table 3 shows the patients' electromyographic findings. In nearly all the patients their LSN showed up as a neuropathic EMG recording.Only in patient 1 did the recording meet the criteria for myopathy [26]: her low EMG amplitude wasregarded as indicating muscle weakness, and under endoscopic examination the patient showed features of glottal insufficiency. In all otherpatients, recordings showed neuropathic features.…”

Section: Resultsmentioning

confidence: 99%

Laryngeal sensory neuropathy caused by COVID-19: findings using laryngeal electromyography

Krasnodębska

Szkiełkowska

Miaśkiewicz

2023

Eur Arch Otorhinolaryngol

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Purpose Laryngeal sensory neuropathy (LSN) is caused by a disorder of the superior laryngeal nerve or the recurrent laryngeal nerve. A diagnosis of LSN should include laryngeal electromyography (LEMG) and laryngovideostroboscopy (LVS). The aim of this study was to characterize the physical and subjective symptoms of neuropathy in patients diagnosed with LSN following COVID-19. Material and methods Since the beginning of the COVID-19 pandemic, 6 patients who had recovered from the disease presented to us with LSN symptoms. All patients underwent laryngological and phoniatric examination, objective and subjective voice assessment, and LEMG. Results The most common LSN symptom reported by patients was periodic hoarseness of varying severity. Other common symptoms were the sensation of a foreign body in the throat and voice fatigue. Endoscopy often showed functional abnormalities. The LSN patients could be characterized by LEMG recordings, and all showed abnormal activity of the cricothyroid (CT) muscle. The degree of EMG changes in the CT correlated moderately with the severity of dysphonia. Conclusions Sensory neuropathy of the larynx may be a long-lasting complication of SARS-COV-2 infection. The severity of EMG neuropathic changes in the CT muscle broadly corresponds to the severity of dysphonia.

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Section: Resultsmentioning

confidence: 99%

Laryngeal sensory neuropathy caused by COVID-19: findings using laryngeal electromyography

Krasnodębska

Szkiełkowska

Miaśkiewicz

2023

Eur Arch Otorhinolaryngol

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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.…”

Section: Emg Signal Classificationmentioning

confidence: 99%

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine:Currentstate 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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“…(2) Not many systematic approaches have been dedicated to address the binary (neuropathy/myopathy) classification problem with iEMG signals. (3) Unlike traditional methods, 14,15,16 our approach makes use of a single feature and is thus suitable for real-time implementation due to the speed-up and simplicity. (4) We exploited a comprehensive set of diverse classification models that are integrated via the BMMV algorithm to reach a decision superior to EL methods.…”

Section: Introductionmentioning

confidence: 99%

IntramuscularEMGclassifier for detecting myopathy and neuropathy

Jose

George

Samuel

et al. 2022

Int J Imaging Syst Tech

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This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one‐dimensional (1‐D) signals. In this approach, the 1‐D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer‐Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated 10‐fold cross‐validation, CSLBP‐Combined‐Classifiers‐BMMV (CSLBP‐CC‐BMMV) achieved an average classification accuracy of 92.80%, 94.25%, and 93.71% for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP‐CC‐BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.

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Classification of EMG Signals for Assessment of Neuromuscular Disorders

Cited by 25 publications

References 17 publications

Laryngeal sensory neuropathy caused by COVID-19: findings using laryngeal electromyography

Laryngeal sensory neuropathy caused by COVID-19: findings using laryngeal electromyography

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

IntramuscularEMGclassifier for detecting myopathy and neuropathy

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