Predicting viral respiratory tract infections using wearable biosensor monitoring during 3-minute constant rate stair stepping tests

Heart rate variability (HRV) is a physiological marker of the cardiac autonomic modulation and related emotional regulation. Electroencephalography (EEG) is reflective of brain cortical activities and related psychopathology. The HRV and EEG have been employed in machine learning- and deep learning-based algorithms either alone or with other wearable device-based features to classify patients with psychiatric disorder (PT) and healthy controls (HC). Little study examined the utility of wearable device-based physiological markers to discern PT with various psychiatric diagnosis versus HC. This study examined the HRV and prefrontal EEG features most frequently selected in the support vector machine (SVM) having the highest classification accuracy of PT versus HC, contributing to the individual-level initial screening of PT and minimized duration of untreated psychiatric illness. A simultaneous acquisition of 5 minute-length PPG (measured on right ear lobe) and resting-state EEG (with eye-closed; using two left/right forehead-located electrodes) of 182 participants [87 PT (including major depressive disorder (70.1%) and panic disorder (12.6%)) and 95 HC] were performed. The PPG-based HRV features were quantified for both time- and frequency-domains. The time-varying EEG signals were converted into frequency-domain signals of the power spectral density. In the feature selection of the Gaussian radial basis function kernel-based support vector machine (SVM) models, estimators were comprised of top N (1£N£22) highest scored HRV/EEG features based on the one-way ANOVA F-value. Classification performance of SVM model (PT vs. HC) having N estimators was assessed using the Leave-one-out cross-validation (LOOCV; N = 182), to confirm those showing the highest balanced accuracy and area under the receiver operating characteristic curve (AUROC) as final classification model. The final SVM model having 13 estimators showed balanced accuracy of 0.76 and AUROC of 0.78. Power spectral density of HRV in the high frequency, very low frequency, low frequency (LF) bands, and total power, a product of the mean of the 5-minute standard deviation of all NN intervals (SDNN) and normalized LF power of HRV, power spectral density of frontal EEG in the high alpha and alpha peak frequency comprised the top 13-scored classification features in > 90% of the LOOCV. This study showed a possible synergic effect of combining the HRV and prefrontal EEG features in machine learning-based mental health screening. Future studies to predict the treatment response and to propose the preferred treatment regimen based on the baseline physiological markers are required. N/A.

In the intensive care unit (ICU), managing traumatic brain injury (TBI) patients presents significant challenges due to the dynamic interaction between physiological and clinical markers. This study aims to uncover these subtle interconnections and identify the key ICU markers for the timely care of TBI patients using advanced machine-learning techniques. We combined correlation-based network analysis and graph neural network (GNN) techniques to explore relationships among electrocardiography (ECG) features, vital signs, pathology test results, Glasgow Coma Scale (GCS) scores, and demographics from 29 TBI patients admitted to the Gold Coast University Hospital (GCUH). Our findings highlighted that the final GCS index strongly correlated with arterial and diastolic blood pressure variations, patient demographics such as gender and age, and certain heart rate variability (HRV) features. Variability in diastolic blood pressure, GCS, and pNN50 (an HRV measure) demonstrated strong associations with several other physiological and clinical markers during the first 12 hours post-ICU admission. HRV features and variability in physiological signals during the first 12 hours in the ICU are important factors in assessing the severity of TBI patients.

BackgroundThis paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress.MethodsECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman’s rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features.ResultsSix out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier.ConclusionThis study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation.

In this study, we investigated the effect of principal component analysis (PCA) in congestive heart failure (CHF) diagnosis using various machine learning algorithms from 5-min HRV data. The extracted 59 heart rate variability (HRV) features consist of statistical time-domain measures, frequency-domain measures (power spectral density estimations from Fourier transform and Lomb-Scargle methods), time-frequency HRV measures (Wavelet transform), and nonlinear HRV measures (Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy). All these HRV features are the classifiers' inputs. We repeated the study ten times using the first one to the first 10 principal components from PCA instead of all HRV features. Nine different classifiers, namely logistic regression, Naive Bayes, k-nearest neighbors, decision tree, AdaBoost, support vector machines, stochastic gradient descent, random forest, and artificial neuronal network (multilayer perceptron) are examined. The proposed study results in the 100% accuracy, 100% specificity, and 100% sensitivity after utilizing PCA (with the first eight principal components) using the Random Forest classifier where the maximum classifier performances are the 86% accuracy, 79% specificity, and 86% sensitivity before PCA. In conclusion, PCA is beneficial in the diagnosis of patients with CHF. In addition, we experienced the online Python-based visual machine learning tool, Orange, which can implement well-known machine learning algorithms.

Development of machine learning prediction models for systemic inflammatory response following controlled exposure to a live attenuated influenza vaccine in healthy adults using multimodal wearable biosensors in Canada: a single-centre, prospective controlled trial.

In the present study, we studied the impact of cigarette smoking on Heart Rate Variability(HRV). HRV is a non-invasive screening technique that measures the variation in consecutive heartbeats. HRV features were derived from the Electrocardiogram(ECG), and it is an indicator of the autonomous nervous system(ANS) status. The ECG was recorded before and 5 minutes after smoking, and HRV features were extracted. The healthy subjects HRV data was considered as a baseline for the study. Both real and synthetic HRV data were used to perform this study. The synthetic HRV data was generated using Synthetic Minority Oversampling Technique(SMOTE) Machine Learning(ML) technique. The data of healthy and smokers was analyzed using both statistical test and machine learning algorithms. Both approaches suggest an ANS imbalance in smokers that ultimately shows the modulation in HRV. The modulation in HRV indicates the high chances of cardiovascular risk in smokers.

Combining early post-resuscitation EEG and HRV features improves the prognostic performance in cardiac arrest model of rats

Non-invasive early diabetes prediction has been gaining much premarkable over the last decade. Heart rate variability (HRV) is the only non-invasive technique that can predict the future occurrence of the disease. Early prediction of diabetes can help doctors start an early intervention. To this end, the authors have developed a computational machine learning model to predict type 2 diabetes mellitus (T2DM) risk using heart rate variability features and have evaluated its robustness against the HRV of 50 patients data. The electrocardiogram (ECG) signal of the control population (n=40) and T2DM population (n=120) have been recorded in the supine position for 5 minutes, and HRV signals have been obtained. The time domain, frequency domain, and non-linear features have been extracted from the HRV signal. A decision support system has been developed based on a machine learning algorithm. Finally, the decision support system has been validated using the HRV features of 50 patients (Control n=10 and T2DM n=40). HRV features are selected for the prediction of T2DM. The decision support system has been designed using three machine learning models: Gradient boosting decision tree (GBDT), Extreme Gradient boosting (XGBoost), Categorical boosting (CatBoost), and their performance have been evaluated based on the Accuracy (ACC), Sensitivity (SEN), Specificity (SPC), Positive predicted value (PPV), Negative predicted value (NPV), False-positive rate (FPR), False-negative rate (FNR), F1 score, and Area under the receiver operating characteristic curve (AUC) metrics. The CatBoost model offers the best performance outcomes, and its results have been validated on 50 patients. Thus the CatBoost model can be use as a decision support system in hospitals to predict the risk of T2DM.

The WE SENSE study protocol: A controlled, longitudinal clinical trial on the use of wearable sensors for early detection and tracking of viral respiratory tract infections

Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability (HRV) levels compared to healthy controls. This research aimed to create and evaluate a machine learning (ML) model enabling discrimination between cancer patients and healthy controls based on 5-min-ECG recordings. We selected 12 HRV features based on previous research and compared the results between cancer patients and healthy individuals using Wilcoxon sum-rank test. Recursive Feature Elimination (RFE) identified the top five features, averaged over 5 min and employed them as input to three different ML. Next, we created an ensemble model based on a stacking method that aggregated the predictions from all three base classifiers. All HRV features were significantly different between the two groups. SDNN, RMSSD, pNN50%, HRV triangular index, and SD1 were selected by RFE and used as an input to three different ML. All three base-classifiers performed above chance level, RF being the most efficient with a testing accuracy of 83%. The ensemble model showed a classification accuracy of 86% and an AUC of 0.95. The results obtained by ML algorithms suggest HRV parameters could be a reliable input for differentiating between cancer patients and healthy controls. Results should be interpreted in light of some limitations that call for replication studies with larger sample sizes.

With its rising prevalence and serious complications, type 2 diabetes mellitus (T2DM) is a major worldwide health burden that calls for early detection using non-invasive screening techniques. Existing screening techniques, including OGTT, HbA1c, and fasting plasma glucose, have drawbacks in terms of accessibility, expense, and invasiveness. Recent developments in heart rate variability (HRV) analysis and machine learning (ML) offer a possible non-invasive substitute for diabetes screening. Previous research on HRV-based ML models in the classification of diabetes has issues with generalizability. The objective of this study is to develop and validate ML models using HRV features: time-domain, frequency-domain, and nonlinear HRV, to improve the prediction of T2DM. The study also evaluates the developed ML model's effectiveness against existing ML models. A retrospective dataset comprising 519 individuals (261 T2DM patients and 258 non-diabetic controls) was collected from the Autonomic Function Testing (AFT) laboratory repositories. To ensure comparability of age, gender, height, and weight among groups, post-hoc matching was used. HRV features were extracted from five-minute ECG recordings using the PowerLab data acquisition system and LabChart HRV module (ADInstruments, Sydney, Australia), following the European Society of Cardiology Task Force guidelines. An 80:20 train-test split was used to train and assess MLmodels, such as Logistic Regression, K-Nearest Neighbors (KNNs), Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost, and AdaBoost. Accuracy, precision, recall, F1-score, area under the curve (AUC) for the receiver operating characteristic (ROC), sensitivity, and specificity were among the performance indicators. GridSearchCV was used for hyperparameter adjustment to maximize model performance. The baseline characteristics of the non-diabetic and T2DM groups were similar (p>0.05). HRV analysis showed substantial decreases in the diabetic group's time-domain (SDNN - SDof Normal-to-Normal Intervals/RMSSD - RMS of Successive Differences), frequency-domain (Low/HighFrequency - LF/HF), and nonlinear (SD2 - SD of Poincaré Plot/CVRR - Coefficient of Variation of R-R Intervals) parameters (p<0.001). With a 91.2% accuracy rate and an AUC of 0.91, CatBoost outperformed other ML models in terms of prediction. LightGBM and Random Forest, which demonstrated high sensitivity and specificity, trailed closely behind. KNN achieved the highest accuracy (98.2%) and AUC (0.99), followed by Random Forest (96.4%) and CatBoost (94.5%), while hyperparameter modification further enhanced performance. CatBoost demonstrated the highest predictive performance, with an accuracy of 91.2% and an AUC of 0.91. According to correlation analysis, the most important HRV characteristics for diabetes prediction were SD2, SDRR (SD of R-R Intervals), and CVRR. This study validates the utility of HRV-based ML models for non-invasive T2DM prediction, with ensemble models like CatBoost and LightGBM demonstrating superior performance when compared to the results of prior ML models. The optimized ML model, integrated with wearable medical technology for real-time monitoring, offers a scalable, affordable, and non-invasive alternative for diabetes screening. To improve generalizability and clinical use, future studies should investigate wearable-based HRV monitoring, multimodal AI models, and longitudinal validation.

The most common ways of measuring Cognitive Load (CL) is using physiological sensor signals e.g., Electroencephalography (EEG), or Electrocardiogram (ECG). However, these signals are problematic in situations e.g., in dynamic moving environments where the user cannot relax with all the sensors attached to the body and it provides significant noises in the signals. This paper presents a case study using a contact-free approach for CL classification based on Heart Rate Variability (HRV) collected from ECG signal. Here, a contact-free approach i.e., a camera-based system is compared with a contact-based approach i.e., Shimmer GSR+ system in detecting CL. To classify CL, two different Machine Learning (ML) algorithms, mainly, Support Vector Machine (SVM) and k-Nearest-Neighbor (k-NN) have been applied. Based on the gathered Inter-Beat-Interval (IBI) values from both the systems, 13 different HRV features were extracted in a controlled study to determine three levels of CL i.e., S0: low CL, S1: normal CL and S2: high CL. To get the best classification accuracy with the ML algorithms, different optimizations such as kernel functions were chosen with different feature matrices both for binary and combined class classifications. According to the results, the highest average classification accuracy was achieved as 84% on the binary classification i.e. S0 vs S2 using k-NN. The highest F1 score was achieved 88% using SVM for the combined class considering S0 vs (S1 and S2) for contact-free approach i.e. the camera system. Thus, all the ML algorithms achieved a higher classification accuracy while considering the contact-free approach than contact-based approach.

Chronic stress has become an epidemic with negative health risks including cardiovascular disease, hypertension, and diabetes. Traditional methods of stress measurement and monitoring typically relies on self-reporting. However, wearable smart technologies offer a novel strategy to continuously and non-invasively collect objective health data in the real-world. A novel electrocardiogram (ECG) feature has recently been introduced to the Apple Watch device. Interestingly, ECG data can be used to derive Heart Rate Variability (HRV) features commonly used in the identification of stress, suggesting that the Apple Watch ECG app could potentially be utilized as a simple, cost-effective, and minimally invasive tool to monitor individual stress levels. Here we collected ECG data using the Apple Watch from 36 health participants during their daily routines. Heart rate variability (HRV) features from the ECG were extracted and analyzed against self-reported stress questionnaires based on the DASS-21 questionnaire and a single-item LIKERT-type scale. Repeated measures ANOVA tests did not find any statistical significance. Spearman correlation found very weak correlations (p < 0.05) between several HRV features and each questionnaire. The results indicate that the Apple Watch ECG cannot be used for quantifying stress with traditional statistical methods, although future directions of research (e.g., use of additional parameters and Machine Learning) could potentially improve stress quantification with the device.

Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined. Then, decision trees were used for driver fatigue detection using the HRV features of either all subjects or those of only males or females. Nineteen, eighteen, and thirteen HRV features were significantly different (Mann-Whitney U test, p < 0.01) between the two mental states for all subjects, males, and females, respectively. The fatigue detection models for all subjects, males, and females achieved classification accuracies of 86.3%, 94.8%, and 92.0%, respectively. In conclusion, sex differences in HRV features between drivers' mental states were found according to both the statistical analysis and classification results. By considering sex differences, precise HRV feature-based driver fatigue detection systems can be developed. Moreover, in contrast to conventional methods using HRV features from 5 min ECG signals, our method uses HRV features from 2 min ECG signals, thus enabling more rapid driver fatigue detection.

Machine learning (ML) integration in biomedical signal processing and medical diagnosis has the potential to revolutionize healthcare by improving diagnostic accuracy. This paper focuses on the applications of different ML algorithms for analyzing real-time physiological data collected from Photoplethysmography (PPG) sensors. Heart rate variability (HRV) analysis using electrocardiography (ECG) signals makes the process longer and bulky. Therefore, this paper demonstrates the real-time generation of HRV signals using a simple, low-cost, and non-invasive PPG sensor which is further processed using the Arduino ATMEGA328P microcontroller and then interfaced to a PC for display to investigate the usefulness of HRV feature analysis. HRV features have been computed using time domain analysis (TA), and frequency domain analysis (FA). At last, these TA and FA indices have been given to different ML models that could predict the gender, age group, and physiological conditions of a human being. Prediction of the physiological conditions using TA, FA, and ML models simultaneously makes the proposed approach more novel than the other existing methods. Comparative analysis of different ML approaches using ROC curves and confusion matrices has been shown to find the effectiveness and precision of different proposed models. It shows random forest ML approach has achieved 91% accuracy in identifying the physiological conditions. This simple yet accurate real-time PPG-based time-frequency ML system might be useful in medical assessment with faster response.