OBJECTIVE: The main objective of this paper is to develop a robust algorithm to extract respiration rate using the contactless displacement sensor.
METHODS: In this study, chest movements were used as an indicative of inspiration and expiration to measure respiratory rate using the contactless displacement sensor. The contactless optical signals were recorded from 32 healthy subjects in four different controlled breathing conditions: rest, coughing, talking and hand movement to obtain the motion artifacts that the patients may have in the emergency department. The Empirical mode decomposition (EMD) algorithm was used to derive continuous RR signal from the contactless optical signal.
RESULTS: The analysis showed that there is a good correlation (0.9702) with RMSE of 0.33 breaths per minutes between the contact respiration rate and contactless respiration rate using empirical mode decomposition method.
CONCLUSION: It can be concluded that the empirical mode decomposition method can extract the respiration rate of the contactless optical signal from chest movement.
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.
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 we benefit from fractal analysis to study the effect of complexity of path of movement on the complexity of human brain reaction.
METHODS: For this purpose we calculate the fractal dimension of the electroencephalography (EEG) signal when subjects walk on different paths with different fractal dimensions (complexity).
RESULTS: The results of the analysis show that the complexity of brain activity increases with the increment of complexity of path of movement.
CONCLUSION: The method of analysis employed in this research can also be employed to analyse the reaction of the human heart and respiration when subjects move on paths with different complexities.
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 paper presents a machine learning-based approach for the automatic classification of regular and irregular capnogram segments.
METHODS: Herein, we proposed four time- and two frequency-domain features experimented with the support vector machine classifier through ten-fold cross-validation. MATLAB simulation was conducted on 100 regular and 100 irregular 15 s capnogram segments. Analysis of variance was performed to investigate the significance of the proposed features. Pearson's correlation was utilized to select the relatively most substantial ones, namely variance and the area under normalized magnitude spectrum. Classification performance, using these features, was evaluated against two feature sets in which either time- or frequency-domain features only were employed.
RESULTS: Results showed a classification accuracy of 86.5%, which outperformed the other cases by an average of 5.5%. The achieved specificity, sensitivity, and precision were 84%, 89% and 86.51%, respectively. The average execution time for feature extraction and classification per segment is only 36 ms.
CONCLUSION: The proposed approach can be integrated with capnography devices for real-time capnogram-based respiratory assessment. However, further research is recommended to enhance the classification performance.
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: 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.
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.
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: In this research, electroencephalography (EEG) as the feature of brain activity and voice signals were simultaneously analyzed.
METHOD: For this purpose, we changed the activity of the human brain by applying different odours and simultaneously recorded their voices and EEG signals while they read a text. For the analysis, we used the fractal theory that deals with the complexity of objects. The fractal dimension of EEG signal versus voice signal in different levels of brain activity were computed and analyzed.
RESULTS: The results indicate that the activity of human voice is related to brain activity, where the variations of the complexity of EEG signal are linked to the variations of the complexity of voice signal. In addition, the EEG and voice signal complexities are related to the molecular complexity of applied odours.
CONCLUSION: The employed method of analysis in this research can be widely applied to other physiological signals in order to relate the activities of different organs of human such as the heart to the activity of his brain.