Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals.

The continuous monitoring of respiratory rate (RR) and oxygen saturation (SpO2) is crucial for patients with cardiac, pulmonary, and surgical conditions. RR and SpO2 are used to assess the effectiveness of lung medications and ventilator support. In recent studies, the use of a photoplethysmogram (PPG) has been recommended for evaluating RR and SpO2. This research presents a novel method of estimating RR and SpO2 using machine learning models that incorporate PPG signal features. A number of established methods are used to extract meaningful features from PPG. A feature selection approach was used to reduce the computational complexity and the possibility of overfitting. There were 19 models trained for both RR and SpO2 separately, from which the most appropriate regression model was selected. The Gaussian process regression model outperformed all the other models for both RR and SpO2 estimation. The mean absolute error (MAE) for RR was 0.89, while the root-mean-squared error (RMSE) was 1.41. For SpO2, the model had an RMSE of 0.98 and an MAE of 0.57. The proposed system is a state-of-the-art approach for estimating RR and SpO2 reliably from PPG. If RR and SpO2 can be consistently and effectively derived from the PPG signal, patients can monitor their RR and SpO2 at a cheaper cost and with less hassle.

COVID-19 Detection Using Photoplethysmography and Neural Networks.

The early identification of microvascular changes in patients with Coronavirus Disease 2019 (COVID-19) may offer an important clinical opportunity. This study aimed to define a method, based on deep learning approaches, for the identification of COVID-19 patients from the analysis of the raw PPG signal, acquired with a pulse oximeter. To develop the method, we acquired the PPG signal of 93 COVID-19 patients and 90 healthy control subjects using a finger pulse oximeter. To select the good quality portions of the signal, we developed a template-matching method that excludes samples corrupted by noise or motion artefacts. These samples were subsequently used to develop a custom convolutional neural network model. The model accepts PPG signal segments as input and performs a binary classification between COVID-19 and control samples. The proposed model showed good performance in identifying COVID-19 patients, achieving 83.86% accuracy and 84.30% sensitivity (hold-out validation) on test data. The obtained results indicate that photoplethysmography may be a useful tool for microcirculation assessment and early recognition of SARS-CoV-2-induced microvascular changes. In addition, such a noninvasive and low-cost method is well suited for the development of a user-friendly system, potentially applicable even in resource-limited healthcare settings.

Automatic Health Monitoring In Health Estate

This paper proposes an automatic system to monitor the health status of the individuals in an estate such as their blood pressure values, their blood glucose value, their blood oxygen value, their heart rate and their respiratory rate. In particular, the system consists of an intelligent watch, a mobile application, a central server and a medical platform. The intelligent watch acquires five photoplethysmograms (PPGs) via different photo sensors with different wavelengths and transmits the PPGs to the mobile via a bluetooth transmitter. The mobile application allows the inputs of the reference values of these health indices of the individuals and displays the estimated values. Also, it sends the PPGs and these reference values to the central server. The central server estimates the health indices. The medical platform consists of a team of medical officers. They monitor the health indices of the individuals and provide the medical advices. This system can detect the occurrence of the sudden decay of the health status of the individuals. Hence, it can reduce the death rate due to the spread of the new diseases such as the COVID19. © 2022 IEEE.

COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology.

At present, COVID-19 is spreading widely around the world. It causes many health problems, namely, respiratory failure and acute respiratory distress syndrome. Wearable devices have gained popularity by allowing remote COVID-19 detection, contact tracing, and monitoring. In this study, the correlation of photoplethysmogram (PPG) morphology between patients with COVID-19 infection and healthy subjects was investigated. Then, machine learning was used to classify the extracted features between 43 cases and 43 control subjects. The PPG data were collected from 86 subjects based on inclusion and exclusion criteria. The systolic-onset amplitude was 3.72% higher for the case group. However, the time interval of systolic-systolic was 7.69% shorter in the case than in control subjects. In addition, 12 out of 20 features exhibited a significant difference. The top three features included dicrotic-systolic time interval, onset-dicrotic amplitude, and systolic-onset time interval. Nine features extracted by heatmap based on the correlation matrix were fed to discriminant analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The ANN showed the best performance with 95.45% accuracy, 100% sensitivity, and 90.91% specificity by using six input features. In this study, a COVID-19 prediction model was developed using multiple PPG features extracted using a low-cost pulse oximeter.

Development of Real-Time Methods for Calculating Cardiointervalograms for Estimating the State of the Cardiovascular System Using a Single Photoplethysmogram Signal

We have developed four real-time methods for calculating a cardiointervalogram from a photoplethysmogram signal to calculate the total percentage of phase synchronization for estimating the state of the cardiovascular system. The methods were compared with the classical method of calculating a cardiointervalogram from an electrocardiogram signal using signals recorded from healthy volunteers and patients with COVID-19. © 2022 IEEE.

Dynamics of connections between autonomic regulatory circuits of the cardiovascular system in patients with Covid-19

The work aims to study the dynamics of connections between the circuits of the autonomic regulation of blood circulation in patients with Covid-19. The tools for the study are methods of nonlinear analysis. The work shows dynamics of the degree of phase synchronization between 0.1-Hz components of the RR-interval signals and the photoplethysmogram, and no visible change in the direction of connections. © 2022 IEEE.

Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal.

Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients.

The Cuffless Blood Pressure Measurement with Ballistocardiogram and Photoplethysmogram

In order to decrease the infection of COVID-19 virus, people must frequency wash the hands or decrease the times of touching the public things. The blood pressure (BP) and weight are the important parameters under the healthy management. The goal of this study is to measure BP with a weight scale. The Ballistocardiogram and photoplethysmogram (PPG) were measured to extract the pulse transit time (PTT) that was used to estimate BP. The results show that the performances of proposed method were close to the reference method, using the electrocardiogram and PPG. Thus, the proposed method can decrease the infected risk when measuring BP with the cuff. © 2022 IEEE.

Cuffless and Touchless Measurement of Blood Pressure from Ballistocardiogram Based on a Body Weight Scale.

Currently, in terms of reducing the infection risk of the COVID-19 virus spreading all over the world, the development of touchless blood pressure (BP) measurement has potential benefits. The pulse transit time (PTT) has a high relation with BP, which can be measured by electrocardiogram (ECG) and photoplethysmogram (PPG). The ballistocardiogram (BCG) reflects the mechanical vibration (or displacement) caused by the heart contraction/relaxation (or heart beating), which can be measured from multiple degrees of the body. The goal of this study is to develop a cuffless and touchless BP-measurement method based on a commercial weight scale combined with a PPG sensor when measuring body weight. The proposed method was that the PTTBCG-PPGT was extracted from the BCG signal measured by a weight scale, and the PPG signal was measured from the PPG probe placed at the toe. Four PTT models were used to estimate BP. The reference method was the PTTECG-PPGF extracted from the ECG signal and PPG signal measured from the PPG probe placed at the finger. The standard BP was measured by an electronic blood pressure monitor. Twenty subjects were recruited in this study. By the proposed method, the root-mean-square error (ERMS) of estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 6.7 ± 1.60 mmHg and 4.8 ± 1.47 mmHg, respectively. The correlation coefficients, r2, of the proposed model for the SBP and DBP are 0.606 ± 0.142 and 0.284 ± 0.166, respectively. The results show that the proposed method can serve for cuffless and touchless BP measurement.

Analysis of the coupling of autonomic regulatory loops of blood circulation in patients with Covid and cardiovascular pathologies

The work aims to study the features of autonomic control of the cardiovascular system in two groups of patients with Covid-19: with and without arterial hypertension. A total of 15 pairs of 20-minute electrocardiogram and photoplethysmogram signals were registrated in each group. We used the methods of spectral analysis, as well as the previously proposed method for assessing the phase synchronization of 0.1-Hz rhythms of signals of autonomic control of heart rate and blood pressure. The data of patients with chronic arterial hypertension showed a lower level of synchronization than patients without it. This is probably due to the peculiarities of autonomic control of the cardiovascular system in patients with chronic arterial hypertension. © 2022 SPIE.

SpO2 Measurement: Non-Idealities and Ways to Improve Estimation Accuracy in Wearable Pulse Oximeters

The blood oxygen saturation level (SpO2) has become one of the vital body parameters for the early detection, monitoring, and tracking of the symptoms of coronavirus diseases 2019 (COVID-19) and is clinically accepted for patient care and diagnostics. Pulse oximetry provides non-invasive SpO2 monitoring at home and ICUs without the need of a physician/doctor. However, the accuracy of SpO2 estimation in wearable pulse oximeters remains a challenge due to various non-idealities. We propose a method to improve the estimation accuracy by denoising the red and IR signals, detecting the signal quality, and providing feedback to hardware to adjust the signal chain parameters like LED current or transimpedance amplifier gain and enhance the signal quality. SpO2 is calculated using the red and infrared photoplethysmogram (PPG) signals acquired from the wrist using Texas Instruments AFE4950EVM. We introduce the green PPG signal as a reference to obtain the window size of the moving average filter for baseline wander removal and as a timing reference for peak and valley detection in the red and infrared PPG signals. We propose the improved peak and valley detection algorithm based on the incremental merge segmentation algorithm. Kurtosis, entropy, and Signal-to-noise ratio (SNR) are used as signal quality parameters, and SNR is further related to the variance in the SpO2 measurement. A closed-loop implementation is performed to enhance signal quality based on the signal quality parameters of the recorded PPG signals. The proposed algorithm aims to estimate SpO2 with a variance of 1% for the pulse oximetry devices. IEEE

Analysis of coupling between autonomic control loops of blood circulation in patients with Covid-19

This work aims to analyze the coupling between autonomic control loops of blood circulation in patients with Covid-19. In this work, we assessed the degree of coupling between the mechanisms of autonomic control using noninvasive signals of the cardiovascular system - RR-intervals signals and photoplethysmogram signals. Statistical evaluation of the study results using the methods of phase synchronization analysis did not reveal significant differences between the sample of patients with Covid-19 and healthy subjects of the corresponding age group. © 2021 IEEE

Respiratory Rate Estimations using Three Respiratory-Induced Variations on Photoplethysmogram

Other than SpO2 and heart rate (HR), respiratory rate (RR) has become one of the baseline clinical characteristics as risk factors for Covid-19 mortality. Commonly measured using capnography and impedance pneumography (IP) in hospitals, respiratory rate is possibly incorporated in one portable device of pulse oximeter for home monitoring together with SpO2 and HR. Photoplethysmogram (PPG) signals, recorded by pulse oximeters, are modulated by respiration due to physiological mechanisms. There are three respiratory-induced variations on PPG signals: baseline wander (BW), amplitude modulation (AM), and frequency modulation (FM). From these three modulating signals, we estimated an RR value by various data fusions. In addition to the artifact handling mechanism, we also presented the effects of signal lengths on the performance of RR estimation methods in terms of error metrics and their ability to detect sudden changes in RR values. © 2021 IEEE.

Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum.

In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO2) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO2, we attempted to demonstrate accurate SpO2 estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO2 from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO2 was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPGgreen-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO2. The accurate SpO2 estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications.