METHODS: In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes.
RESULTS: Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature.
CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.
OBJECTIVE: The chief aim of the study was to evaluate microbial retention on the salivary pellicle on treatment with oral rinses (CHX & EO)/PS (mimicking after meals use of mouth wash/PS).
METHODS: Noordini's Artifical Mouth model was used for developing the single species biofilm with early microbial colonizers of oral biofilm (A. viscosus, Strep. mitis and Strep. sanguinis respectively). The microbial retention on use of oral rinses comprising of CHX and EO as an active ingredients respectively was compared with Curcumin PS. For evaluating the microbial retention, the pellicle with microbial inoculation was developed on the glass beads in the mouth model. Subsequently the respective single specie biofilm was exposed to the mouth wash and PS after inoculation. It mimicked as use of mouth wash/PS after meals. The bacterial count in the dental biofilm was evaluated on serial dilution (CFU/ml). Sterile deionized water was used as a negative control. For qualitative analysis, Scanning electron microscope (SEM) was used to evaluate the microbial count.
RESULTS: From the data it was observed that for the treatment of single species experimental biofilm with commercially available mouth rinses (CHX & EO) and PS (curcumin), there was significant retention for S.mitis, S.sanguinis and A.viscosus. There was no significant difference observed between PS and CHX treated single species biofilm. Whereas a significant difference was observed between EO treated biofilms and CHX/PS treated biofilms (p⩽ 0.05).
CONCLUSION: It can be concluded from the results that curcumin PS and CHX should not be used after meals whereas EO containing mouth rinse can be used to maintain the oral mocroflora.
OBJECTIVE: This research aimed to verify the effects and progress of video-guided deep breathing (DB) integrated into CP through study on the changes of alpha waves and pain scale.
METHODS: Alpha waves were recorded using an electroencephalogram (EEG) and a visual analogue scale (VAS) to assess pain intensity before and after the intervention (6 weeks). Thirty CAP participants were recruited and randomly assigned to two groups: group A for video-guided DB integration into their CP and group B for CP. The effects of pre and post intervention were analyzed using a paired t-test with statistical significance set at p< 0.05.
RESULTS: Profound results from the research have shown that the participants who received both the DB+CP revealed a significant increase in alpha wave (p< 0.05) at occipital region.
CONCLUSION: The significant result reveals an increase in alpha waves in the occipital region after 6 weeks and indicates that the video-guided DB with a smartphone application is able to produce a change in CAP participants. This supports the DB integration to the CP for altering the pain perception.
OBJECTIVES: This study aims to develop a highly accurate, portable FMD and to demonstrate real-time monitoring of force applied by health professionals during JMT without altering its execution.
METHODS: The FMD was constructed using the FlexiForce sensor, potential divider, ATmega 328 microcontroller, custom-written software, and liquid crystal display. The calibration, accuracy, and cyclic repeatability of the FMD were tested from 0 to 90 N applied load with a gold standard universal testing machine. For practical demonstration, the FMD was tested for monitoring applied force by a physiotherapist while performing Maitland's grade I to IV over the 6th cervical vertebra among 30 healthy subjects.
RESULTS: The obtained Bland-Altman plot limits agreement for accuracy, and cyclic repeatability was -1.57 N to 1.22 N, and -1.26 N to 1.26 N, respectively with standard deviation and standard error of the mean values of 3.77% and 0.73% and 2.15% and 0.23%, respectively. The test-retest reliability of the FMD tested by the same researcher at an interval of one week showed an excellent intra-class correlation coefficient of r= 1.00. The obtained force readings for grade I to IV among 30 subjects ranged from 10.33 N to 45.24 N.
CONCLUSIONS: Appreciable performance of the developed FMD suggested that it may be useful to monitor force applied by clinicians during JMT among neck pain subjects and is a useful educational tool for academicians to teach mobilization skills.
OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view.
METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents.
RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.
OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain.
METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content).
RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals.
CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.
OBJECTIVE: In order to address this issue, we analyzed how leg muscle activity is related to the variations of the path of movement.
METHOD: Since the electromyography (EMG) signal is a feature of muscle activity and the movement path has complex structures, we used entropy analysis in order to link their structures. The Shannon entropy of EMG signal and walking path are computed to relate their information content.
RESULTS: Based on the obtained results, walking on a path with greater information content causes greater information content in the EMG signal which is supported by statistical analysis results. This allowed us to analyze the relation between muscle activity and walking path.
CONCLUSION: The method of analysis employed in this research can be applied to investigate the relation between brain or heart reactions and walking path.
OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis.
METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18-22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked.
RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566).
CONCLUSION: We conclude that heart and brain activities are related.
METHODS: The proposed device measures and displays the FHR on a screen liquid crystal display (LCD). The device consists of hardware that comprises condenser microphone sensor, signal conditioning, microcontroller and LCD, and software that involves the algorithm used for processing the conditioned fetal heart signal prior to FHR display. The device's performance is validated based on analysis of variance (ANOVA) test.
RESULTS: FHR data was recorded from 22 pregnant women during the 17th to 37th week of gestation using the developed device and two standard devices; AngelSounds and Electronic Stethoscope. The results show that F-value (1.5) is less than F𝑐𝑟𝑖𝑡, (3.1) and p-value (p> 0.05). Accordingly, there is no significant difference between the mean readings of the developed and existing devices. Hence, the developed device can be used for monitoring FHR in clinical and home environments.
OBJECTIVE: The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM).
METHODS: The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete's physical fitness status.
RESULTS: The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete's physical fitness and overall performance to a considerable extent.
CONCLUSION: The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions.
OBJECTIVE: The aim of this study was to compare the activity and relationship between surface EMG and static force from the BB muscle in terms of three sensor placement locations.
METHODS: Twenty-one right hand dominant male subjects (age 25.3 ± 1.2 years) participated in the study. Surface EMG signals were detected from the subject's right BB muscle. The muscle activation during force was determined as the root mean square (RMS) electromyographic signal normalized to the peak RMS EMG signal of isometric contraction for 10 s. The statistical analysis included linear regression to examine the relationship between EMG amplitude and force of contraction [40-100% of maximal voluntary contraction (MVC)], repeated measures ANOVA to assess differences among the sensor placement locations, and coefficient of variation (CoV) for muscle activity variation.
RESULTS: The results demonstrated that when the sensor was placed on the muscle belly, the linear slope coefficient was significantly greater for EMG versus force testing (r^{2} = 0.61, P > 0.05) than when placed on the lower part (r^{2}=0.31, P< 0.05) and upper part of the muscle belly (r^{2}=0.29, P > 0.05). In addition, the EMG signal activity on the muscle belly had less variability than the upper and lower parts (8.55% vs. 15.12% and 12.86%, respectively).
CONCLUSION: These findings indicate the importance of applying the surface EMG sensor at the appropriate locations that follow muscle fiber orientation of the BB muscle during static contraction. As a result, EMG signals of three different placements may help to understand the difference in the amplitude of the signals due to placement.