Information-based decoding of the coupling among human brain activity and movement paths

An automated cervical cancer diagnosis using genetic algorithm and CANFIS approaches

BACKGROUND

Cervical malignancy is considered among the most perilous cancers affecting women in numerous East African and South Asian nations, both in terms of its prevalence and fatality rates.

OBJECTIVE

This research aims to propose an efficient automated system for the segmentation of cancerous regions in cervical images.

METHODS

The proposed techniques encompass preprocessing, feature extraction with an optimized feature set, classification, and segmentation. The original cervical image undergoes smoothing using the Gaussian Filter technique, followed by the extraction of Local Binary Pattern (LBP) and Grey Level Co-occurrence Matrix (GLCM) features from the enhanced cervical images. LBP features capture pixel relationships within a mask window, while GLCM features quantify energy metrics across all pixels in the images. These features serve to distinguish normal cervical images from abnormal ones. The extracted features are optimized using Genetic Algorithm (GA) as an optimization method, and the optimized sets of features are classified using the Co-Active Adaptive Neuro-Fuzzy Inference System (CANFIS) classification method. Subsequently, a morphological segmentation technique is employed to categorize irregular cervical images, identifying and segmenting malignant regions within them.

RESULTS

The proposed approach achieved a sensitivity of 99.09%, specificity of 99.39%, and accuracy of 99.36%.

CONCLUSION

The proposed approach demonstrated superior performance compared to state-of-the-art techniques, and the results have been validated by expert radiologists.

Decoding the correlation between heart activation and walking path by information-based analysis

BACKGROND

One of the important areas of heart research is to analyze heart rate variability during (HRV) walking.

OBJECTIVE

In this research, we investigated the correction between heart activation and the variations of walking paths.

METHOD

We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation.

RESULTS

According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series.

CONCLUSION

The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.

Decoding of the coupling between the brain and facial muscle reactions in auditory stimulation

BACKGROUND

Analysis of the reactions of different organs to external stimuli is an important area of research in physiological science.

OBJECTIVE

In this paper, we investigated the correlation between the brain and facial muscle activities by information-based analysis of electroencephalogram (EEG) signals and electromyogram (EMG) signals using Shannon entropy.

METHOD

The EEG and EMG signals of thirteen subjects were recorded during rest and auditory stimulations using relaxing, pop, and rock music. Accordingly, we calculated the Shannon entropy of these signals.

RESULTS

The results showed that rock music has a greater effect on the information of EEG and EMG signals than pop music, which itself has a greater effect than relaxing music. Furthermore, a strong correlation (r= 0.9980) was found between the variations of the information of EEG and EMG signals.

CONCLUSION

The activities of the facial muscle and brain are correlated in different conditions. This technique can be utilized to investigate the correlation between the activities of different organs versus brain activity in different situations.

Decoding the coupling between the brain and skin reactions in auditory stimulation by information-based analysis of EEG and GSR signals

BACKGROUND

The analysis of brain activity in different conditions is an important research area in neuroscience.

OBJECTIVE

This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals.

METHODS

We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents.

RESULTS

The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions.

CONCLUSION

This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.