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In this paper, the brain reactions to explosion sound are investigated. Electroencephalogram (EEG) signals of 17 people were recorded. Subjects were selected from three groups: staff who did not face explosion before, blasting employees of surface, and underground mining workers. Routine EEG signals, also called explosion sounds, were recorded. Explosion sound was broadcasted without any previous alarm. Then it was repeated with their pre-awareness. Gradient and time duration of Delta band of EEG signals were extracted as features. Results showed that for blasting employees, especially underground ones, an increase of mean amplitude of delta band power of EEG signals of motor, speech, auditory and visual sensations were occurred, while in the case of staff it was decreased. This shows consciousness arising of blasting employees with hearing explosion sound. The reaction of somatosensory sense was dropped for all three groups. In general, reaction time for blasting employees has been longer than staff.

The neurocognitive sequelae of a sport-related concussion and its management are poorly defined. Detecting deficits are vital in making a decision about the treatment plan as it can persist one year or more following a brain injury. The reliability of traditional cognitive assessment tools is debatable, and thus attention has turned to assessments based on electroencephalogram (EEG) to evaluate subtle post-concussive alterations. In this study, we calculated neurocognitive deficits combining EEG analysis with three standard post-concussive assessment tools. Data were collected for all testing modalities from 21 adolescent athletes (seven concussive and fourteen healthy) in three different trials. For EEG assessment, along with linear frequency-based features, we introduced a set of time-frequency (Hjorth Parameters) and nonlinear features (approximate entropy and Hurst exponent) for the first time to explore post-concussive deficits. Besides traditional frequency-band analysis, we also presented a new individual frequency-based approach for EEG assessment. While EEG analysis exhibited significant discrepancies between the groups, none of the cognitive assessment resulted in significant deficits. Therefore, the evidence from the study highlights that our proposed EEG analysis and markers are more efficient at deciphering post-concussion residual neurocognitive deficits and thus has a potential clinical utility of proper concussion assessment and management.

Cardiovascular and postural control systems have been studied independently despite the increasing evidence showing the importance of cardiopostural interaction in blood pressure regulation. In this study, we aimed to assess the role of the cardiopostural interaction in relation to cardiac baroreflex in blood pressure regulation under orthostatic stress before and after mild exercise. Physiological variables representing cardiovascular control (heart rate and systolic blood pressure), lower limb muscle activation (electromyography), and postural sway (center of pressure derived from force and moment data during sway) were measured from 17 healthy participants (25 ± 2 yr, 9 men and 8 women) during a sit-to-stand test before and after submaximal exercise. The cardiopostural control (characterized by baroreflex-mediated muscle-pump effect in response to blood pressure changes, i.e., muscle-pump baroreflex) was assessed using wavelet transform coherence and causality analyses in relation to the baroreflex control of heart rate. Significant cardiopostural blood pressure control was evident counting for almost half of the interaction time with blood pressure changes that observed in the cardiac baroreflex (36.6-72.5% preexercise and 34.7-53.9% postexercise). Thus, cardiopostural input to blood pressure regulation should be considered when investigating orthostatic intolerance. A reduction of both cardiac and muscle-pump baroreflexes in blood pressure regulation was observed postexercise and was likely due to the absence of excessive venous pooling and a less stressed system after mild exercise. With further studies using more effective protocols evoking venous pooling and muscle-pump activity, the cardiopostural interaction could improve our understanding of the autonomic control system and ultimately lead to a more accurate diagnosis of cardiopostural dysfunctions. NEW & NOTEWORTHY We examined the interaction between cardiovascular and postural control systems during standing before and after mild exercise. Significant cardiopostural input to blood pressure regulation was shown, suggesting the importance of cardiopostural integration when investigating orthostatic hypotension. In addition, we observed a reduction of baroreflex-mediated blood pressure regulation after exercise., (Copyright © 2017 the American Physiological Society.)

Goal: the objective of this study was to develop a method to identify respiratory phases (i.e., inhale or exhale) of seismocardiogram (SCG) cycles. An SCG signal is obtained by placing an accelerometer on the sternum to capture cardiac vibrations., Methods: SCGs from 19 healthy subjects were collected, preprocessed, segmented, and labeled. To extract the most important features, each SCG cycle was divided to equal-sized bins in time and frequency domains, and the average value of each bin was defined as a feature. Support vector machines was employed for feature selection and identification. The features were selected based on the total accuracy. The identification was performed in two scenarios: leave-one-subject-out (LOSO), and subject-specific (SS)., Results: time-domain features resulted in better performance. The time-domain features that had higher accuracies included the characteristic points correlated with aortic-valve opening, aortic-valve closure, and the length of cardiac cycle. The average total identification accuracies were 88.1% and 95.4% for LOSO and SS scenarios, respectively., Conclusion: the proposed method was an efficient, reliable, and accurate approach to identify the respiratory phases of SCG cycles., Significance: The results obtained from this study can be employed to enhance the extraction of clinically valuable information such as systolic time intervals.

Melanoma is the most serious type of skin cancer and causes more deaths than other forms of skin cancer. It is a tiny small malignant mole that is usually black or brown but also appears in other color patterns. Early detection of melanoma is key as this is the time period when it is most likely to be cured. Due to the advancement of smartphone technology, automatic and efficient detection of melanoma mole using a smartphone is an active area of research. In this study, we developed an automatic melanoma diagnosis system using images captured from the digital camera. Our work differs from other studies in the area of segmentation of melanoma region and consideration of non-linear features for classification of malignant and benign melanoma. In this paper, a combination of Otsu and k-means clustering segmentation methods are applied to automatically segment and extract the borders of affected region with satisfactory accuracy. Also, we explored and extracted different non-linear features along with color and texture features existed in literature from the lesion mole. The effectiveness of these features was predicted with a machine learning model consisting of five different classifiers. Our model predicted the diagnosis of mole with an accuracy of 89.7%, i.e., around 10% more than reported results by others (to the best of our knowledge) with the same database.

Gait speed measurement is vital for diagnosis of motor disorder and monitoring the progress of patient rehabilitation. This study presents an algorithm for moderate distance gait speed measurement from data acquired with inertial motion sensors comprised of a tri-axial accelerometer and a tri-axial gyroscope. Gait speed was measured in four different speed levels set by a treadmill: 0.5, 1, 2, and 3 miles/hour. The calculated speed was tuned by implementing Kalman Filter. The performance of the proposed algorithm was evaluated by calculating the mean square error between estimated speed and the actual treadmill speed. The preliminary results obtained from various treadmill speeds suggest that proposed algorithm estimated speed in a reasonable accuracy. The average error rate was 0.23 m/h which is nearly similar to other studies in this area. Algorithm performance evaluation for various speeds implied that the best performance was exhibited when the speed was set at 1 mile/hour. Moreover, the use of Kalman Filter helped to fine-tune the estimated speed by removing uncertainty and eventually provided a better approximation of the speed measured from the inertial measurement unit.

This paper presents experimental results for measuring respiratory rate using non-contact and low power system for health-monitoring applications. The system is based on a Doppler effect. A mechanical setup with controllable movement frequency and displacement was built to mimic the human chest movements while breathing. A Doppler radar system was used to measure the frequency of the proposed system. Three different antennas were used to study the effect of antenna radiation pattern, gain, and cross-polarization on the accuracy of the measurements. An error analysis was conducted for different frequencies and displacements. Results demonstrated that the antenna of a moderate directivity and gain values, and of the least cross-polarization components has higher accuracy compared to other proposed antennas. It can be concluded that with a good selection of antenna it is possible to measure respiratory rate with a small error using the proposed radar system.

Sport related mild traumatic brain injury (mTBI), generally known as a concussion, is a worldwide critical public health concern nowadays. Despite growing concern emphasized by scientific research and recent media presentation regarding mTBI and its effect in athletics life, the management, and prevention of mTBI are still not properly done. The evaluation mainly hampered due to the lack of proper knowledge, subjective nature of assessment tools including the fact that the brain functional deficits after mTBI can be mild or hidden. As a result, development of an effective tool for proper management of these mild incidents is a subject of active research. In this paper, to examine the neural substrates following mTBI, an analysis based on electroencephalogram (EEG) from twenty control and twenty concussed athletes is presented. Preliminary results suggest that the concussed athletes have a significant increase in delta, theta and alpha power but a decrease in beta power. We also calculated the power for individual frequencies from 1 Hz to 40 Hz in order to find out the specific frequencies with the highest deficits. The significant deficiencies were found at 1-2 Hz of delta band, 6-7 Hz of theta band, 8-10 Hz of the alpha band, and 16-18 Hz and 24-29 Hz of the beta band. Though there was no significant difference as observed in gamma band, we found the deficit was significant at 34-36 Hz range within the gamma band. The observed deficits at various frequencies demonstrate that even if there is no significant difference in the traditional frequency bands, there may be hidden deficits at some specific frequencies within a frequency band. These preliminary results suggest that the EEG analysis at each unity frequency may be more promising means of identifying the neuronal damage than the traditional frequency band based analysis. Eventually, the proposed analysis can provide an improved approximation to monitor the pathophysiological recovery after a concussion.

Parkinson's disease (PD) patients regularly exhibit abnormal gait patterns. Automated differentiation of abnormal gait from normal gait can serve as a potential tool for early diagnosis as well as monitoring the effect of PD treatment. The aim of current study is to differentiate PD patients from healthy controls, on the basis of features derived from plantar vertical ground reaction force (VGRF) data during walking at normal pace. The current work presents a comprehensive study highlighting the efficacy of different machine learning classifiers towards devising an accurate prediction system. Selection of meaningful feature based on sequential forward feature selection, the swing time, stride time variability, and center of pressure features facilitated successful classification of control and PD gaits. Support Vector Machine (SVM), K-nearest neighbor (KNN), random forest, and decision trees classifiers were used to build the prediction model. We found that SVM with cubic kernel outperformed other classifiers with an accuracy of 93.6%, the sensitivity of 93.1%, and specificity of 94.1%. In comparison to other studies, utilizing same dataset, our designed prediction system improved the classification performance by approximately 10%. The results of the current study underscore the ability of the VGRF data obtained non-invasively from wearable devices, in combination with a SVM classifier trained on meticulously selected features, as a tool for diagnosis of PD and monitoring effectiveness of therapy post pathology.

The causal interaction between cardio-postural-musculoskeletal systems is critical in maintaining postural stability under orthostatic challenge. The absence or reduction of such interactions could lead to fainting and falls often experienced by elderly individuals. The causal relationship between systolic blood pressure (SBP), calf electromyography (EMG), and resultant center of pressure (COPr) can quantify the behavior of cardio-postural control loop. Convergent cross mapping (CCM) is a non-linear approach to establish causality, thus, expected to decipher nonlinear causal cardio-postural-musculoskeletal interactions. Data were acquired simultaneously from young participants (25 ± 2 years, n = 18) during a 10-minute sit-to-stand test. In the young population, skeletal muscle pump was found to drive blood pressure control (EMG → SBP) as well as control the postural sway (EMG → COPr) through the significantly higher causal drive in the direction towards SBP and COPr. Furthermore, the effect of aging on muscle pump activation associated with blood pressure regulation was explored. Simultaneous EMG and SBP were acquired from elderly group (69 ± 4 years, n = 14). A significant (p = 0.002) decline in EMG → SBP causality was observed in the elderly group, compared to the young group. The results highlight the potential of causality to detect alteration in blood pressure regulation with age, thus, a potential clinical utility towards detection of fall proneness.

Chronic skin diseases like eczema may lead to severe health and financial consequences for patients if not detected and controlled early. Early measurement of disease severity, combined with a recommendation for skin protection and use of appropriate medication can prevent the disease from worsening. Current diagnosis can be costly and time-consuming. In this paper, an automatic eczema detection and severity measurement model are presented using modern image processing and computer algorithm. The system can successfully detect regions of eczema and classify the identified region as mild or severe based on image color and texture feature. Then the model automatically measures skin parameters used in the most common assessment tool called "Eczema Area and Severity Index (EASI)," by computing eczema affected area score, eczema intensity score, and body region score of eczema allowing both patients and physicians to accurately assess the affected skin.

Assessment, treatment, and management of sport-related concussions are a widely recognized public health issue. Although several neuropsychological and motor assessment tools have been developed and implemented for sports teams at various levels and ages, the sensitivity of these tests has yet to be validated with more objective measures to make return-to-play (RTP) decisions more confidently. The present study sought to analyze the residual effect of concussions on a sample of adolescent athletes who sustained one or more previous concussions compared to those who had no concussion history. For this purpose, a wide variety of assessment tools containing both neurocognitive and electroencephalogram (EEG) elements were used. All clinical testing and EEG were repeated at 8 months, 10 months, and 12 months post-injury for both healthy and concussed athletes. The concussed athletes performed poorer than healthy athletes on processing speed and impulse control subtest of neurocognitive test on month 8, but no alterations were marked in terms of visual and postural stability. EEG analysis revealed significant differences in brain activities of concussed athletes through all three intervals. These long-term neurocognitive and EEG deficits found from this ongoing sport-related concussion study suggest that the post-concussion physiological deficits may last longer than the observed clinical recovery.

This paper presents a new approach to identify the respiratory phases of heart cycles from acceleration signals (i.e., seismocardiogram) recorded from the sternum, in back to front direction. The acceleration signals were recorded simultaneously with a single lead electrocardiogram (ECG), and the respiratory signal (using a chest band strain gauge) from 20 healthy subjects. Two accelerometer-derived respiration (ADR) signals were computed by computing the lower and upper envelope of the accelerometer signal. In the proposed methodology, for each subject a metric so-called, the piecewise total harmonic distortion (THD) was used to identify which one of lower and upper envelopes is the best ADR for detecting respiratory phases. The accuracy of piecewise THD in the selection of the correct envelope of SCG signal as an estimation of ADR is 84.6%. Consequently, respiratory phases of heart cycles were identified using the estimated ADR signals. Results confirm that the proposed envelope detection based ADR technique can detect respiratory phases of heartbeats with the accuracy of above 75%. In other words, using aforementioned methods, THD thresholding and piecewise THD, the capability of ADR signal to detect respiratory phases is increased approximately 14% compared to the lower envelope of the accelerometer (ADRLower) and 4% compared to the upper envelope of accelerometer signal (ADRUpper).

Knowledge of a cause-and-effect relationship between different physiological systems is helpful in predicting their performance under perturbations, such as orthostatic challenge. The causal coupling between representative signals of the cardiovascular and postural systems under orthostatic challenge remains unknown. Understanding the causal relationship between these two systems is critical, as their interplay is vital to maintain stable upright posture of the human body during quiet standing. In this research, convergent cross mapping (CCM) method was applied to study the causal relationship between the cardiovascular and postural systems previously shown to have coherent activity during quiet standing. Causality was studied between Systolic blood pressure (SBP)-EMG (calf muscles), EMG-COPr (resultant center of pressure), and COPr-SBP signal pairs. These signals were simultaneously recorded in a 5-minute sit-to-stand test from five young healthy participants. Strength of causality was obtained between the signal pairs in a 30-second time segments. The results from this study indicate that there exists a bidirectional causal relationship between the cardio-postural signal pairs, indicating a system level interaction to counter perturbation due to orthostatic challenge. Skeletal muscle pump was found to be driving control of SBP and COPr as the value of EMG→SBP (0.54±0.09) and EMG→COPr (0.52±0.07) were higher than the reverse causality of SBP→EMG (0.19±0.16) and COPr→EMG (0.29±0.16).