Materials and Methods: It was a cross-sectional study involving a two-stage sampling to select the district and villages. A total of 325 participants were selected based on convenience sampling.
Results: Almost half of the participants rated their oral health as poor or average. The mean GOHAI score was 52.96 (±7.749), ranging from 29 to 60. The GOHAI score was statistically significantly lower for female gender (P = 0.025), lower education level (P = 0.001), and elderly (P = 0.001). The GSROH score was also statistically significant with GOHAI score (P = 0.001).
Conclusions: A limited number of studies were conducted in this area, particularly in the vulnerable population of OA. Our study found that half of the OA living in the fringe had a poor GOHAI score. It is, therefore, suggested that potential study and intervention programs concentrate on the low GOHAI score group; the male, lower educational context, and the elderly.
Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd-ball paradigm. The proposed system records EEG signals from 10 individuals at eight-channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross-correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross-correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures ("minimum," "mean," "maximum," and "standard deviation") were derived from the cross-correlated signals. Finally, the extracted feature sets were validated through online sequential-extreme learning machine algorithm.
Results and Conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 91.93%.