فصلنامه مهندسی پزشکی زیستی
سال چهاردهم شماره 2 (تابستان 1399)

An open research question is how the central nervous system (CNS) to find a solution for the problem of redundancy or degree of freedom in the human shoulder motion control. We used time-varying synergy theory in which assumed that the relative activation between muscles is time-varying, to investigate the combination of activation patterns of muscles in twelve 3-D hand-held exercises by Flexi-Bar using Anybody Technology software (A/S, Aalborg, Denmark). Using activation of 12 muscles and the moment across the joint as an input matrix for the optimization procedure to extract functional time-varying synergies, time delays and amplitude coefficients, the achieved 5 tonic and phasic synergies explained 79% of the data variation. Matching pursuit procedure and non-negative least square used to find timing shifts and amplitude coefficients respectively. Considering a new exercise out of the primary database, 60% of the activation patterns reconstructed using time-varying synergies. Although the extracted synergies seem to be directionally tuned, the results show that due to the same velocity in all exercises and also because the torque which that was applied due to the weight of the bar and arm on the joint is not significant, both timing shifts and separation phasic-tonic parts of the activation patterns provide no further explanation on CNS behavior and finding them causes unnecessary computational cost. Future study can focus on the comparison of synergies between two or more groups of exercises by the Flexi-Bar such as holding the bar vertically or horizontally with swinging it up and down or back and forth.

After Alzheimer, Parkinson's disease is known as the most common malignant disease of the nervous system. One of the common obstacles of this disease is the expansion of speech disorders. Since the speech production in humans is made by combination of vibration of the vocal cords (phonatory section) and then passage through the resonator in vocal tract (articulatory section), it is expected that both of these sections to be impaired. In this study, by using a noninvasive method, it is intended to diagnose Parkinson's disease from speech signal of each subject; for this purpose, using 3 sustain vowels in Persian language recorded from 48 people (27 people with Parkinson's disease and 21 healthy people), it has been evaluated to assess the extent of damage to both phonatory and articulatory sections. The phonatory model can include features such as jitter, shimmer, fundamental frequencies, opening and closing cycling time of the glottal pulses. On the other hand, for the articulatory section, features such as first, second, and third formmants, zero crossing rates, MFFCs, and LPC are investigated. In this study, 38 feature categories were extracted and four statistical parameters of mean, standard deviation, skewness and kurtosis were calculated. Genetic Algorithm was used to identify the optimum features. Then, using the SVM, KNN and the Decision Tree classifiers, the optimum extracted features are classified to determine whether a person is patient or healthy. Finally for the main aim of this study, the results of both phonatory and articulatory sections were compared and challenged. The results of this study showed that phonatory features with accuracy of 96.1±1.2% were more useful than articulatory section in diagnosing of Parkinson. Also it was proved that vowel /u/ has more significant role in the diagnosis of Parkinson's disease compared to other vowels by accuracy of 97.6%.

Consumption of alcohol contributes to disorders in brain. In this study, in order to detect the consumption of alcohol, electroencephalogram (EEG) signal of 20 participants (10 alcoholic and 10 control subjects) recorded by 64 channels was investigated. Frequency and non-frequency features of EEG signal including power spectrum of signal, permutation entropy, approximate entropy, Katz fractal dimension and Petrosion fractal dimension were extracted to analyses the EEG signal. Statistical analysis was used to investigate the significant differences between the alcohol and control groups. The Davis-Bouldin (DB) criterion was used to select the best channel distinguishing between the alcoholic and non-alcoholic EEG signal. Results showed that between frequency features, power of lower2 alpha frequency decreased in alcoholic individuals and regarding the DB criterion, the CP3 channel (DB=1.7638) showed the best discr imi na tion between the alcohol and control groups. Also, among the non-frequency features, the Katz fractal dimension increased in the control group and FP2 channel (DB = 0.862) had the best discrimination. Eventually, power of Lower2-alpha frequency band and Katz fractal dimension fed into the nearest neighbor classifier (KNN), 71% and 93% accuracy were achieved, respectively. According to the results, it can be concluded that the best feature and channel discriminating between alcohol and control groups is the Katz fractal dimension and FP2 channel.

Previous research has shown that eye movements in people with Attention Deficit Hyperactivity Disorder (ADHD) and healthy people were different, and it is possible that there is a difference between the two groups' EOG signals. Therefore, in the present study, the recorded EOG signals of 30 children with ADHD and 30 healthy children were examined during performing an attentional related task. For this purpose, the scaling exponents of the two groups' EOG signals were calculated and the differences between the two groups were examined using statistical tests. The EOG signals were then classified using a Growing Neural Gas network. The results show that the scaling exponents of the EOG signals in children with ADHD were significantly higher than that of healthy children (p < 0.001). This result shows that the decay slope of power spectrum in ADHD children is more as compared to healthy children. In addition, the EOG signals were classified into two groups with a detection accuracy of 72.22±2.8%. The results of this study could be used to design a course of treatment with EOG biofeedback to treat or reduce the symptoms of people with ADHD.