The laryngeal video stroboscope is an important instrument to test glottal diseases and read vocal fold images and voice quality for physician clinical diagnosis. This study is aimed to develop a medical system with functionality of automatic intelligent recognition of dynamic images. The static images of glottis opening to the largest extent and closing to the smallest extent were screened automatically using color space transformation and image preprocessing. The glottal area was also quantized. As the tongue base movements affected the position of laryngoscope and saliva would result in unclear images, this study used the gray scale adaptive entropy value to set the threshold in order to establish an elimination system. The proposed system can improve the effect of automatically captured images of glottis and achieve an accuracy rate of 96%. In addition, the glottal area and area segmentation threshold were calculated effectively. The glottis area segmentation was corrected, and the glottal area waveform pattern was drawn automatically to assist in vocal fold diagnosis. When developing the intelligent recognition system for vocal fold disorders, this study analyzed the characteristic values of four vocal fold patterns, namely, normal vocal fold, vocal fold paralysis, vocal fold polyp, and vocal fold cyst. It also used the support vector machine classifier to identify vocal fold disorders and achieved an identification accuracy rate of 98.75%. The results can serve as a very valuable reference for diagnosis.
Eyelid movement patterns are a key factor in the detection of fatigue, and in this study, electroencephalography (EEG) was used to record the brainwave patterns associated with eyelid movement in subjects during various stages of fatigue. The three movements involved were no eyelid movement, closing the eye, and opening the eye. The collected signals were processed using the wavelet transform (WT) to break down the EEG signal and obtain the main features. The support vector machine (SVM) and back propagation neural network (BPNN) were used to determine eyelid movement conditions.
Cycling is a very popular activity worldwide and cyclists often listen to music while riding. This usually involves the use of a portable music player or a cellphone, which may present safety problems. Since these devices may easily distract the attention of the cyclist. This is a matter that has not received much attention but has great relevance to traffic safety. In this study, the attention level of cyclists was measured and recorded as electroencephalograms (EEGs), and discrete wavelet transforms (DWTs) based on Daubechies wavelets were used to extract the EEG features. Six different cycling activity patterns were investigated and eigenfunctions were used to classify the attention level. After feature extraction by the DWTs, support vector machines (SVMs) and general regression neural networks (GRNNs) were employed to recognize different states of mind associated with specific cycling activities. In Case I, the recognition rates of the SVM and GRNN were used to determine the state of mind associated with two different cycling activities, riding in a straight line and riding around obstacles. In Case II, rider vigilance was investigated using the SVM and GRNN for eight different cycling scenarios. The experimental results validated the proposed method and showed the brainwave patterns to be clearly associated with different cycling activities. The experimental results showed that looking at a cellphone screen or engaging in a call caused riders to miss very obvious peripheral stimuli and the use of a cellphone by a cyclist is a clear danger to the rider and traffic safety.
The extremely busy and stressful nature of life in modern society places a heavy toll on both the physical and mental health of many individuals. This has led to an increase in the numbers of patients with physical and mental illnesses. Of the many types of psychiatric illness, we focus on the study of schizophrenia. Many previous studies have shown that the emotional responses of schizophrenia patients are different from those of normal patients. In this study, we used different visual stimuli to induce emotions on subjects, and electroencephalography (EEG) signals were simultaneously collected for analysis. The collected EEG signals were subjected to Daubechies wavelet transformation, and the extracted features were input to a support vector machine (SVM) for analysis and identification.
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