ABSTRACT
Meditation is commonly perceived as an alternative medicine management tool for psychological diseases such as depression and anxiety disorders. To our knowledge, there is no published study providing an index for estimating meditation's depth from biological signals. Estimating the depth of meditation can be useful in controlling its different levels, and it can be used as a biofeedback technique to help a person achieve the desired state of meditation. In this study, an index for meditation depth is offered using the features of electroencephalogram and heart rate signals. For this purpose, EEG signals in Fz, Cz, and Pz channels, and the heart rate time series of 25 healthy women were collected both before and during the meditations. The algorithm is suggested based on the rational alpha power of EEG signals and the time domain feature of the heart rate to estimate the depth of meditation. The analysis of biological signals using this method suggests that 22 of the 25 participants have experienced the deepest meditation state. Interestingly, 2 of the beginners as well as one expert-mediators could not reach the deepest state by following up the master mediator. These results were in line with the evaluation of the questionnaire. Conclusions: The suggested algorithm has some practical characteristics including: the option of being calibrated for each subject; not requiring high-volume calculations; and it does not take much time
ABSTRACT
The purpose of this study is to quantify the voice disorders in children with cochlear implantation and hearing aids. Until now, quantifying voice disorders has been done subjectively by speech experts and it is for the first time that the preset study tends to run an objective experiment using signal processing features. 4 levels were considered to be qualify speech. Linear and nonlinear features were extracted from 5 Farsi words: "mashin', "mar', "moosh', "gav" and "mowz" uttered by 30 subjects and then put into hidden Markov classifiers. Classifier outputs then were fused together to have better accuracy. The main hypothesis of the study is to answer this question: Can we separate children into 4 levels based on their voice features? Voice features including "fundamental frequency, first formant, second formant, third formant, first to second formant ratio, third to second formant ratio, Rational Intensity, nasality, approximate entropy and fractal dimension were extracted from speech segments and then are were given to artificial decision making system [classifiers]. The results show that classifiers can separate 4 levels of voice disorders with the accuracy of 93.1%. Among the introduced features, first to second formant ratio and third to second formant ratio can be used directly to track voice recovery after using cochlear implantation or hearing aid. The output of this research study can act as a speaker independent system to help speech specialists with evaluating voice disorder recovery in children who fall in the same range of age