Comparison of Autonomic Control of Blood Pressure During Standing and Artificial Gravity Induced via Short-Arm Human Centrifuge

Autonomic control of blood pressure is essential toward maintenance of cerebral perfusion during standing, failure of which could lead to fainting. Long-term exposure to microgravity deteriorates autonomic control of blood pressure. Consequently, astronauts experience orthostatic intolerance on their return to gravitational environment. Ground-based studies suggest sporadic training in artificial hypergravity can mitigate spaceflight deconditioning. In this regard, short-arm human centrifuge (SAHC), capable of creating artificial hypergravity of different g-loads, provides an auspicious training tool. Here, we compare autonomic control of blood pressure during centrifugation creating 1-g and 2-g at feet with standing in natural gravity. Continuous blood pressure was acquired simultaneously from 13 healthy participants during supine baseline, standing, supine recovery, centrifugation of 1-g, and 2-g, from which heart rate (RR) and systolic blood pressure (SBP) were derived. The autonomic blood pressure regulation was assessed via spectral analysis of RR and SBP, spontaneous baroreflex sensitivity, and non-linear heart rate and blood pressure causality (RR↔SBP). While majority of these blood pressure regulatory indices were significantly different (p < 0.05) during standing and 2-g centrifugation compared to baseline, no change (p > 0.05) was observed in the same indices during 2-g centrifugation compared to standing. The findings of the study highlight the capability of artificial gravity (2-g at feet) created via SAHC toward evoking blood pressure regulatory controls analogous to standing, therefore, a potential utility toward mitigating deleterious effects of microgravity on cardiovascular performance and minimizing post-flight orthostatic intolerance in astronauts

Application of Recurrent Neural Network for the Prediction of Target Non-Apneic Arousal Regions in Physiological Signals

This work presents a new method for detection of target non-apneic arousals by applying a recurrent neural network architecture on the various specified polysomnographic (PSG) signals. The proposed two stage architecture uses sequences of instantaneous frequencies and spectral entropies of the chosen PSG signals as feature vectors. At the first stage, these feature vectors are used to train several long-short term memory (LSTM) models. The LSTM networks can learn long-term relationships between time steps of time-frequency based sequences obtained out of physiological signals. As a second stage, some quadratic discriminant (QD) layers are modelled and appended to the trained LSTMs in groups. Subsequently, the outputs of all the QD layers are averaged for making final prediction. The models are trained using features obtained from one minute windows of the signals. However, the decision making on test signals involves inputs of one minute windows with half minute overlapping. When evaluated with 2018 PhysioNet/CinC Challenge dataset, the experimental outcomes demonstrate overall AUROC and AUPRC scores of 0.85±0.10 and 0.50±0.15 respectively for the training data. The generated test results indicate the AUROC and AUPRC scores of 0.624 and 0.10 respectively on a random subset of the test data

Development of a Novel Contactless Mechanocardiograph Device

A novel method of detecting mechanical movement of the heart, Mechanocardiography (MCG), with no connection to the subject's body is presented. This measurement is based on radar technology and it has been proven through this research work that the acquired signal is highly correlated to the phonocardiograph signal and acceleration-based ballistocardiograph signal (BCG) recorded directly from the sternum. The heart rate and respiration rate have been extracted from the acquired signal as two possible physiological monitoring applications of the radar-based MCG device

Identifying Opportunities for Augmented Cognition During Live Flight Scenario: An Analysis of Pilot Mental Workload Using EEG

Augmented cognition is a form of human-systems interaction in which physiological sensing of a user’s cognitive state is used to precisely invoke system automations when needed. The present study monitored the in-flight physiological state of the pilot to determine the optimal combination of EEG indices to predict variations in workload, or opportunities for augmented cognition.The [sic] participants were 10 collegiate aviation students with FAA commercial pilot certificates and current medical certificates. Each participant performed a uniform flight scenario that included procedures that varied in workload demands. All maneuvers were performed while simultaneously acquiring EEG data in flight. The EEG data were divided into periods of high and low workload. Power spectral density values were computed and subjected to several machine learning methods to distinguish high and low workload periods. The results indicate excellent classification accuracy for distinguishing low and high workload. The present results further demonstrate the potential of augmented cognition

Identifying Patients With Coronary Artery Disease Using Rest and Exercise Seismocardiography

Coronary artery disease (CAD) is the most common cause of death globally. Patients with suspected CAD are usually assessed by exercise electrocardiography (ECG). Subsequent tests, such as coronary angiography and coronary computed tomography angiography (CCTA) are performed to localize the stenosis and to estimate the degree of blockage. The present study describes a non-invasive methodology to identify patients with CAD based on the analysis of both rest and exercise seismocardiography (SCG). SCG is a non-invasive technology for capturing the acceleration of the chest induced by myocardial motion and vibrations. SCG signals were recorded from 185 individuals at rest and immediately after exercise. Two models were developed using the characterization of the rest and exercise SCG signals to identify individuals with CAD. The models were validated against related results from angiography. For the rest model, accuracy was 74%, and sensitivity and specificity were estimated as 75 and 72%, respectively. For the exercise model accuracy, sensitivity, and specificity were 81, 82, and 84%, respectively. The rest and exercise models presented a bootstrap-corrected area under the curve of 0.77 and 0.91, respectively. The discrimination slope was estimated 0.32 for rest model and 0.47 for the exercise model. The difference between the discrimination slopes of these two models was 0.15 (95% CI: 0.10 to 0.23, p \u3c 0.0001). Both rest and exercise models are able to detect CAD with comparable accuracy, sensitivity, and specificity. Performance of SCG is better compared to stress-ECG and it is identical to stress-echocardiography and CCTA. SCG examination is fast, inexpensive, and may even be carried out by laypersons

Fetal ECG Extraction from Maternal ECG using Attention-based CycleGAN

Non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal. to map MECG to FECG efficiently. The high correlation between maternal and fetal ECG parts decreases the performance of convolution layers. Therefore, the masking region of interest using the attention mechanism is performed for improving signal generators' precision. The sine activation function is also used since it could retain more details when converting two signal domains. Three available datasets from the Physionet, including A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, are used to evaluate the performance. The proposed method could map abdominal MECG to scalp FECG with an average 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on A&D FECG dataset. Moreover, it achieved 99.7 % F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on, A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. These results are comparable to the state-of-the-art; thus, the proposed algorithm has the potential of being used for high-performance signal-to-signal conversion

Non-linear Heart Rate and Blood Pressure Interaction in Response to Lower-Body Negative Pressure

Early detection of hemorrhage remains an open problem. In this regard, blood pressure has been an ineffective measure of blood loss due to numerous compensatory mechanisms sustaining arterial blood pressure homeostasis. Here, we investigate the feasibility of causality detection in the heart rate and blood pressure interaction, a closed-loop control system, for early detection of hemorrhage. The hemorrhage was simulated via graded lower-body negative pressure (LBNP) from 0 to -40 mmHg. The research hypothesis was that a significant elevation of causal control in the direction of blood pressure to heart rate (i.e., baroreflex response) is an early indicator of central hypovolemia. Five minutes of continuous blood pressure and electrocardiogram (ECG) signals were acquired simultaneously from young, healthy participants (27 ± 1 years, N = 27) during each LBNP stage, from which heart rate (represented by RR interval), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were derived. The heart rate and blood pressure causal interaction (RR SBP and RR MAP) was studied during the last 3 min of each LBNP stage. At supine rest, the non-baroreflex arm (RR SBP and RR MAP) showed a significantly (p \u3c 0.001) higher causal drive toward blood pressure regulation compared to the baroreflex arm (SBP RR and MAP RR). In response to moderate category hemorrhage (-30 mmHg LBNP), no change was observed in the traditional marker of blood loss i.e., pulse pressure (p = 0.10) along with the RR SBP (p = 0.76), RR MAP (p = 0.60), and SBP RR (p = 0.07) causality compared to the resting stage. Contrarily, a significant elevation in the MAP RR (p = 0.004) causality was observed. In accordance with our hypothesis, the outcomes of the research underscored the potential of compensatory baroreflex arm (MAP RR) of the heart rate and blood pressure interaction toward differentiating a simulated moderate category hemorrhage from the resting stage. Therefore, monitoring baroreflex causality can have a clinical utility in making triage decisions to impede hemorrhage progression

Determining the Respiratory State From a Seismocardiographic Signal - A Machine Learning Approach

Seismocardiography (SCG) is a non-invasive method for measurement of vibrations on the chest wall originating from the heart. Respiration changes the morphology of the SCG-signal and analyzing these changes could improve the diagnostic value of SCG. This study aimed to determine the nasal respiration signal amplitude at mitral closure (MC) and aortic opening (AO) using SCG features. The three proposed methods for this were multiple regression analysis (MRA), support vector regression (SVR), and a neural network (NN). SCG, Electrocardiography and nasal-catheter flow signals were acquired from 18 healthy subjects (age 29± 6). SCG-signal fiducial points were used as features and were found using an automatic algorithm followed by manual verification. Fiducial points amplitudes, timings between these and frequency components formed 12 features. All models were trained on 80% of the data, underwent 10-fold cross-validation and were tested on the remaining 20% of the data. Predictions on test data for MC and AO time points, the Pearson correlations coefficient, and sum of squared errors of prediction were: (rMC, rAO, SSEMC, SSEAO) for the following models: NN (0.908, 0.904, 11.71, 12.05), SVR (0.881, 0.833, 18.95, 19.76) and MRA (0.450, 0.437, 51.21, 51.48). These predictive models show a strong correlation between the SCG-signal and respiration

Vertical Ground Reaction Force Marker for Parkinson’s Disease

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