A two-phase iterative machine learning method in identifying mechanical biomarkers of peripheral neuropathy

Wei, Yuan; Gu, Feng; Zhang, Wei

doi:10.1016/j.eswa.2020.114333

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…ML approach can help distinguish healthy individuals and patients with amyotrophic lateral sclerosis [83]. The ML can help in the identification of critical biomarkers that differentiates EMG patterns between patients with certain disease and the healthy individuals [84]. It also helps in the identification of a biomarker that causes pain [85].…”

Section: Biomedical Researchmentioning

confidence: 99%

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

Ojha

2023

Extsv. Rev.

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Electromyography (EMG) is about studying electrical signals from muscles and can provide a wealth of information on the function, contraction, and activity of your muscles. In the field of EMG pattern recognition, these signals are used to identify and categorize patterns linked to muscle activity. Various machine learning (ML) methods are used for this purpose. Successful detection of these patterns depends on using effective signal-processing techniques. It is crucial to reduce noise in EMG for accurate and meaningful information about muscle activity, improving signal quality for precise assessments. ML tools such as SVMs, neural networks, KNNs, and decision trees play a crucial role in sorting out complex EMG signals for different pattern recognition tasks. Clustering algorithms also help analyze and interpret muscle activity. EMG and ML find diverse uses in rehabilitation, prosthetics, and human-computer interfaces, though real-time applications come with challenges. They bring significant changes to prosthetic control, human-computer interfaces, and rehabilitation, playing a vital role in pattern recognition. They make prosthetic control more intuitive by understanding user intent from muscle signals, enhance human-computer interaction with responsive interfaces, and support personalized rehabilitation for those with motor impairments. The combination of EMG and ML opens doors for further research into understanding muscle behavior, improving feature extraction, and advancing classification algorithms.

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Section: Biomedical Researchmentioning

confidence: 99%

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

Ojha

2023

Extsv. Rev.

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“…12 The treatment option is mostly determined according to this grading result. 13 The treatment options for CTS are, in order, from mild to severe; medical treatment, rest splints and surgical treatments. If severe CTS is left untreated, it can cause irreversible damage of median nerve, causing atrophy and weakness in the hand muscles.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Based Rating of Carpal Tunnel Syndrome Efficacy in Clinical Diagnosis

SARICA DAROL,

ECE,

UZUN

et al. 2023

Acta Med Nicomedia

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Objective: The most common entrapment neuropathy seen by the clinician is Carpal tunnel syndrome (CTS). CTS is graded as mild, moderate, and severe according to the results obtained on electroneuromyography (ENMG) by clinicians. We aimed to show the effectiveness of the use of artificial intelligence in clinical diagnosis in the grading of CTS. Methods: In our study, the data of 315 people with a pre-diagnosis of CTS were used and classified into four classes based on AI as CTS grade. Machine Learning (ML) algorithms Ensemble, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (Tree) algorithms were used in classification processes. 10% Hold-out validation was used and the learning rate was determined as 0.1. As a result of the classification, accuracy, precision, sensitivity, specificity, and F1-score performance values were obtained. Results: SVM made the best estimation and KNN made the worst estimation in the 0 class. The best estimate in class 1 belongs to the Support Vector Machine. Ensemble and Tree made the best guesses in the 2nd and 3rd grades. In our study, the best algorithm with an overall success rate is SVM with 93.55%. Conclusions: The results showed that ML algorithm models consistently provided better predictive results and would assist physicians in determining the medical treatment modality of CTS. Artificial intelligence (AI) techniques are reliable methods that assist clinicians to deliver quality healthcare.

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Biomarker discovery from high-throughput data by connected network-constrained support vector machine

Li¹,

Liu²

2023

Expert Systems with Applications

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A two-phase iterative machine learning method in identifying mechanical biomarkers of peripheral neuropathy

Cited by 4 publications

References 41 publications

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

Artificial Intelligence Based Rating of Carpal Tunnel Syndrome Efficacy in Clinical Diagnosis

Biomarker discovery from high-throughput data by connected network-constrained support vector machine

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