Artificial intelligence-based classification of motor unit action potentials in real-world needle EMG recordings

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

(2022) M.R. Tannemaat et al.   CLINICAL NEUROPHYSIOLOGY

Feature extraction from multifractal spectrum of electromyograms for diagnosis of neuromuscular disorders

(2020) Soumya Chatterjee et al.   IET Science Measurement & Technology

Deep learning for waveform identification of resting needle electromyography signals

(2019) Hiroyuki Nodera et al.   CLINICAL NEUROPHYSIOLOGY

Standards of instrumentation of EMG

(2019) Hatice Tankisi et al.   CLINICAL NEUROPHYSIOLOGY

Deep neural network assisted diagnosis of time-frequency transformed electromyograms

(2018) A. Bakiya et al.   MULTIMEDIA TOOLS AND APPLICATIONS

Measuring inter-rater reliability for nominal data – which coefficients and confidence intervals are appropriate?

(2016) Antonia Zapf et al.   BMC Medical Research Methodology

Analysis of ALS and normal EMG signals based on empirical mode decomposition

(2016) Vipin K. Mishra et al.   IET Science Measurement & Technology

STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration

(2016) Jérémie F Cohen et al.   BMJ Open

Classification of EMG signals using combined features and soft computing techniques

(2012) Abdulhamit Subasi   APPLIED SOFT COMPUTING

Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition

(2011) Md. Sahidullah et al.   SPEECH COMMUNICATION