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Difference between revisions of "Artificial Neural Networks: Automatic Neuromuscular Diagnostic"
Artificial Neural Networks: Automatic Neuromuscular Diagnostic (view source)
Revision as of 17:29, 19 October 2024
, 29 days agono edit summary
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{{ArtBy|||autore= Neri Accornero|autore2=Antonietta Romaniello|autore3=Giancarlo Filligoi|autore4=Edvina Galiè|autore5=Bruno Gregori}} | {{ArtBy|||autore= Neri Accornero|autore2=Antonietta Romaniello|autore3=Giancarlo Filligoi|autore4=Edvina Galiè|autore5=Bruno Gregori}} | ||
<br /> | <br />'''Abstract:''' This study presents a preliminary investigation into the use of artificial neural networks (ANNs) for the automatic diagnosis of neuromuscular disorders by evaluating both electrical and acoustic surface signals of muscles. Surface electromyography (EMG) and acousto-myography (AMG) offer non-invasive, cost-effective methods for diagnosing muscular pathologies, potentially even in subclinical stages. These methods also show promise in sports medicine and motorized prosthesis control, as well as neuromotor rehabilitation. The complementary nature of EMG and AMG provides valuable insights into muscle force and fatigue during contraction, with AMG reflecting muscle vibrations at lower frequencies than EMG signals. | ||
This study presents a preliminary investigation into the use of artificial neural networks (ANNs) for the automatic diagnosis of neuromuscular disorders by evaluating both electrical and acoustic surface signals of muscles. Surface electromyography (EMG) and acousto-myography (AMG) offer non-invasive, cost-effective methods for diagnosing muscular pathologies, potentially even in subclinical stages. These methods also show promise in sports medicine and motorized prosthesis control, as well as neuromotor rehabilitation. The complementary nature of EMG and AMG provides valuable insights into muscle force and fatigue during contraction, with AMG reflecting muscle vibrations at lower frequencies than EMG signals. | |||
The methodology involved the development of a composite electrode capable of recording both electrical and acoustic signals simultaneously, allowing for enhanced signal capture and noise reduction. A neural network with a three-layer forward architecture was trained on a dataset of 152 recordings from both healthy and pathological subjects, achieving a low error rate of 0.03. Subsequent tests on 30 randomly selected cases showed accurate classification of neuromuscular conditions. | The methodology involved the development of a composite electrode capable of recording both electrical and acoustic signals simultaneously, allowing for enhanced signal capture and noise reduction. A neural network with a three-layer forward architecture was trained on a dataset of 152 recordings from both healthy and pathological subjects, achieving a low error rate of 0.03. Subsequent tests on 30 randomly selected cases showed accurate classification of neuromuscular conditions. | ||