Gangseong Lee

Respiratory rate (RR) monitoring is vital for people with respiratory conditions, especially the elderly. However, traditional respiration monitors only provide single-point estimates without confidence intervals. Therefore, the statistical variability of the estimate cannot be distinguished from the inherent variability caused by physiological processes. In this study, we present a novel method using exact Gaussian process regression (EGPR)-based multiple mix feature extraction and smart fusion using photoplethysmography (PPG) and electrocardiogram (ECG) signals to improve the reliability of RR and confidence intervals (CIs) estimation. To estimate CIs for accurate RR, we obtain the input data dimensions using power spectral features. After that, the muti-phase feature extraction model is used to compensate for insufficient input data. Finally, we combine the four feature sets and selected features with high weights using adaptive neighbor component analysis. The proposed EGPR methodology is currently the only method that can provide highly stable strains and CIs. Therefore, the proposed model is a robust algorithm for rapidly estimating respiration rates in hospitals and medical centers, featuring significant estimation performance and low estimation mean absolute error (MAE), standard deviation, and CIs. The proposed EGPR-based multiple mix feature (MF) 0.98 breath per minute (bpm) based on the PPG (PMF-EGPR) model shows the lowest MAE compared with the other methods. Moreover, the proposed PMF-EGPR model represents 4.85 CI (bpm) for RR estimation.

The COVID-19 outbreak is a disastrous event that has elevated many psychological problems such as lack of employment and depression given abrupt social changes. Simultaneously, psychologists and social scientists have drawn considerable attention towards understanding how people express their sentiments and emotions during the pandemic. With the rise in COVID-19 cases with strict lockdowns, people expressed their opinions publicly on social networking platforms. This provides a deeper knowledge of human psychology at the time of disastrous events. By applying user-produced content on social networking platforms such as Twitter, the sentiments and views of people are analyzed to assist in introducing awareness campaigns and health intervention policies. The modern evolution of artificial intelligence (AI) and natural language processing (NLP) mechanisms has revealed remarkable performance in sentimental analysis (SA). This study develops a new Marine Predator Optimization with Natural Language Processing for Twitter Sentiment Analysis (MPONLP-TSA) for the COVID-19 Pandemic. The presented MPONLP-TSA model is focused on the recognition of sentiments that exist in the Twitter data during the COVID-19 pandemic. The presented MPONLP-TSA technique undergoes data preprocessing to convert the data into a useful format. Furthermore, the BERT model is used to derive word vectors. To detect and classify sentiments, a bidirectional recurrent neural network (BiRNN) model is utilized. Finally, the MPO algorithm is exploited for optimal hyperparameter tuning process, and it assists in enhancing the overall classification performance. The experimental validation of the MPONLP-TSA approach can be tested by utilizing the COVID-19 tweets dataset from the Kaggle repository. A wide comparable study reported a better outcome of the MPONLP-TSA method over current approaches.

Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodology that combines the Gaussian process with hybrid optimal feature decision (HOFD) in cuffless blood pressure estimation. First, we can choose one of the feature selection methods: robust neighbor component analysis (RNCA), minimum redundancy, maximum relevance (MRMR), and F-test, based on the proposed hybrid optimal feature decision. After that, a filter-based RNCA algorithm uses the training dataset to obtain weighted functions by minimizing the loss function. Next, we combine the Gaussian process (GP) algorithm as the evaluation criteria, which is used to determine the best feature subset. Hence, combining GP with HOFD leads to an effective feature selection process. The proposed combining Gaussian process with the RNCA algorithm shows that the root mean square errors (RMSEs) for the SBP (10.75 mmHg) and DBP (8.02 mmHg) are lower than those of the conventional algorithms. The experimental results represent that the proposed algorithm is very effective.

Cuffless blood pressure (BP) monitoring is crucial for patients with cardiovascular disease and hypertension. However, conventional BP monitors provide only single-point estimates without confidence intervals. Therefore, the statistical variability in the estimates is indistinguishable from the intrinsic variability caused by physiological processes. This study introduced a novel method for improving the reliability of BP and confidence intervals (CIs) estimations using a hybrid feature selection and decision method based on a Gaussian process. F-test and robust neighbor component analysis were applied as feature selection methods for obtaining a set of highly weighted features to estimate accurate BP and CIs. Akaike’s information criterion algorithm was used to select the best feature subset. The performance of the proposed algorithm was confirmed through experiments. Comparisons with conventional algorithms indicated that the proposed algorithm provided the most accurate BP and CIs estimates. To the best of the authors’ knowledge, the proposed method is currently the only one that provides highly reliable BP and CIs estimates. Therefore, the proposed algorithm may be robust for concurrently estimating BP and CIs.

Accurately estimating respiratory rate (RR) has become essential for patients and the elderly. Hence, we propose a novel method that uses exact Gaussian process regression (EGPR)-assisted hybrid feature extraction and feature fusion based on photoplethysmography and electrocardiogram signals to improve the reliability of accurate RR and uncertainty estimations. First, we obtain the power spectral features and use the multi-phase feature model to compensate for insufficient input data. Then, we combine four different feature sets and choose features with high weights using a robust neighbor component analysis. The proposed EGPR algorithm provides a confidence interval representing the uncertainty. Therefore, the proposed EGPR algorithm, including hybrid feature extraction and weighted feature fusion, is an excellent model with improved reliability for accurate RR estimation. Furthermore, the proposed EGPR methodology is likely the only one currently available that provides highly stable variation and confidence intervals. The proposed EGPR-MF, 0.993 breath per minute (bpm), and EGPR-feature fusion, 1.064 (bpm), show the lowest mean absolute error compared to the other models.

Various machine learning models have been used in the biomedical engineering field, but only a small number of studies have been conducted on respiratory rate estimation. Unlike ensemble models using simple averages of basic learners such as bagging, random forest, and boosting, the gradient boosting algorithm is based on effective iteration strategies. This gradient boosting algorithm is just beginning to be used for respiratory rate estimation. Based on this, we propose a novel methodology combining an autocorrelation function-based power spectral feature extraction process with the gradient boosting algorithm to estimate respiratory rate since we acquire the respiration frequency using the autocorrelation function-based power spectral feature extraction that finds the time domain’s periodicity. The proposed methodology solves overfitting for the training datasets because we obtain the data dimension by applying autocorrelation function-based power spectral feature extraction and then split the long-resampled wave signal to increase the number of input data samples. The proposed model provides accurate respiratory rate estimates and offers a solution for reliably managing the estimation uncertainty. In addition, the proposed method presents a more precise estimate than conventional respiratory rate measurement techniques.

The monitors of oscillometry blood pressure measurements are generally utilized to measure blood pressure for many subjects at hospitals, homes, and office, and they are actively studied. These monitors usually provide a single blood pressure point, and they are not able to indicate the confidence interval of the measured quantity. In this paper, we propose a new technique using a recursive ensemble based on a support vector machine to estimate a confidence interval for oscillometry blood pressure measurements. The recursive ensemble is based on a support vector machine that is used to effectively estimate blood pressure and then measure the confidence interval for the systolic blood pressure and diastolic blood pressure. The recursive ensemble methodology provides a lower standard deviation of error, mean error, and mean absolute error for the blood pressure as compared to those of the conventional techniques.

Automated oscillometric blood pressure monitors are commonly used to measure blood pressure for many patients at home, office, and medical centers, and they have been actively studied recently. These devices usually provide a single blood pressure point and they are not able to indicate the uncertainty of the measured quantity. We propose a new technique using an ensemble-based recursive methodology to measure uncertainty for oscillometric blood pressure measurements. There are three stages we consider: the first stage is pre-learning to initialize good parameters using the bagging technique. In the second stage, we fine-tune the parameters using the ensemble-based recursive methodology that is used to accurately estimate blood pressure and then measure the uncertainty for the systolic blood pressure and diastolic blood pressure in the third stage.

Oscillometric blood pressure (BP) monitors currently estimate a single point but do not identify variations in response to physiological characteristics. In this paper, to analyze BP’s normality based on oscillometric measurements, we use statistical approaches including kurtosis, skewness, Kolmogorov-Smirnov, and correlation tests. Then, to mitigate uncertainties, we use a deep learning method to determine the confidence limits (CLs) of BP measurements based on their normality. The proposed deep learning regression model decreases the standard deviation of error (SDE) of the mean error and the mean absolute error and reduces the uncertainties of the CLs and SDEs of the proposed technique. We validate the normality of the distribution of the BP estimation which fits the standard normal distribution very well. We use a rank test in the deep learning technique to demonstrate the independence of the artificial systolic BP and diastolic BP estimations. We perform statistical tests to verify the normality of the BP measurements for individual subjects. The proposed methodology provides accurate BP estimations and reduces the uncertainties associated with the CLs and SDEs using the deep learning algorithm.