Carpal Tunnel Syndrome Automated Diagnosis: A Motor vs. Sensory Nerve Conduction-Based Approach

• Published in: Bioengineering (Basel) Vol. 11; no. 2; p. 175
• Main Authors: Bakalis, Dimitrios, Kontogiannis, Prokopis, Ntais, Evangelos, Simos, Yannis V., Tsamis, Konstantinos I., Manis, George
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2024
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Artificial neural networks; Automation; Care and treatment; Carpal tunnel syndrome; Classification; Classifiers; Datasets; Decomposition; deep learning; Diagnosis; Fourier transforms; Learning algorithms; Machine learning; Median (statistics); Median nerve; Medical imaging; Methods; multilevel wavelet decomposition; Nerve conduction; nerve conduction studies; Neural networks; Neuropathy; Neurophysiology; Sensory neurons; short-time Fourier transform; Signal processing; Spectrograms; Statistics; Time-frequency analysis; Ultrasonic imaging; Wavelet transforms; Greece; deep learning; spectrogram; carpal tunnel syndrome; multilevel wavelet decomposition; machine learning; short-time Fourier transform; nerve conduction studies

The objective of this study was to evaluate the effectiveness of machine learning classification techniques applied to nerve conduction studies (NCS) of motor and sensory signals for the automatic diagnosis of carpal tunnel syndrome (CTS). Two methodologies were tested. In the first methodology, mot...