To evaluate the value of clinical application of examination of fibrillation potential amplitude, 110 patients, 97 males and 13 females, were examined and only the maximum fibrillation potential amplitudes were recorded in 420 muscles. The results showed that there was no significant difference between sexes, ages and sides. However, significant difference was evident between the groups of different frequency (1+ to 4+). The fibrillation potential amplitude was maximum at 3 to 4 months after denervation and still remained at relatively high level for years in certain patients. No significant difference was showed between the time groups in incomplete nerve injuries. Surgery did not affect the course of fibrillation potential amplitude change. It was suggested that the muscle cells sustained their property for years after denervation in some patients, thus it might explain that satisfactory result could be obtained from operative repair in some late cases. The changes of fibrillation potential amplitude might indicate that the changes from muscle denervation was still reversible and might be more accurate than traditional method of examination.
Emotion classification and recognition is a crucial area in emotional computing. Physiological signals, such as electroencephalogram (EEG), provide an accurate reflection of emotions and are difficult to disguise. However, emotion recognition still faces challenges in single-modal signal feature extraction and multi-modal signal integration. This study collected EEG, electromyogram (EMG), and electrodermal activity (EDA) signals from participants under three emotional states: happiness, sadness, and fear. A feature-weighted fusion method was applied for integrating the signals, and both support vector machine (SVM) and extreme learning machine (ELM) were used for classification. The results showed that the classification accuracy was highest when the fusion weights were set to EEG 0.7, EMG 0.15, and EDA 0.15, achieving accuracy rates of 80.19% and 82.48% for SVM and ELM, respectively. These rates represented an improvement of 5.81% and 2.95% compared to using EEG alone. This study offers methodological support for emotion classification and recognition using multi-modal physiological signals.