Aggregate features in multisample classification problems

A clinical decision support system for diagnosis and severity quantification of lumbosacral radiculopathy using intramuscular electromyography signals

Interpreting intramuscular electromyography (iEMG) signals for diagnosing and quantifying the severity of lumbosacral radiculopathy is challenging due to the subjective evaluation of signals. To address this limitation, a clinical decision support system (CDSS) was developed for the diagnosis and quantification of the severity of lumbosacral radiculopathy based on intramuscular electromyography (iEMG) signals. The CDSS uses the EMG interference pattern method (QEMG IP) to directly extract features from the iEMG signal and provide a quantitative expression of injury severity for each muscle and overall radiculopathy severity. From 126 time and frequency domain features, a set of five features, including the crest factor, mean absolute value, peak frequency, zero crossing count, and intensity, were selected. These features were derived from raw iEMG signals, empirical mode decomposition, and discrete wavelet transform, and the wrapper method was utilized to determine the most significant features. The CDSS was trained and tested on a dataset of 75 patients, achieving an accuracy of 93.3%, sensitivity of 93.3%, and specificity of 96.6%. The system shows promise in assisting physicians in diagnosing lumbosacral radiculopathy with high accuracy and consistency using iEMG data. The CDSS's objective and standardized diagnostic process, along with its potential to reduce the time and effort required by physicians to interpret EMG signals, makes it a potentially valuable tool for clinicians in the diagnosis and management of lumbosacral radiculopathy. Future work should focus on validating the system's performance in diverse clinical settings and patient populations.