Human Lung Non-Invasive Anomaly Detection through UWB Microwave Imaging and Diagnosis of COVID-19: A Possible Application

This study shows a prototype for detecting lung effects using microwave imaging. Continuous monitoring of pulmonary fluid levels is one of the most successful approaches for detecting fluid in the lung;early Chest X-rays, computational tomography (CT)-scans, and magnetic resonance imaging (MRI) are the most commonly used instruments for fluid detection. Nonetheless, they lack sensitivity to ionizing radiation and are inaccessible to the general public. This research focuses on the development of a low-cost, portable, and noninvasive device for detecting Covid-19 or lung damage. The simulation of the system involved the antenna design, a 3D model of the human lung, the building of a COMSOL model, and image processing to estimate the lung damage percentage. The simulation consisted of three components. The primary element requires mode switching for four array antennas (transmit and receive). In the paper, microwave tomography was used. Using microwave near-field imaging, the second component of the simulation analyses the lung's bioheat and electromagnetic waves as well as examines the image creation under various conditions;many electromagnetic factors seen at the receiving device are investigated. The final phase of the simulation shows the affected area of the lung phantom and the extent of the damage. © 2023 IEEE.

Continuous Monitoring for Diagnosis: A Wearable for Covid-19 Detection in Symptomatic Patients

We develop an approach for systematically designing continuous monitoring solutions for early symptom diagnosis. Effective early diagnosis requires collecting and correlating symptoms derived from a number of vitals. For designing a continuous monitoring solution, it is crucial to determine the vitals to be monitored for targeted detection, the errors that can be tolerated, various parameters that need to be tuned, etc. Furthermore, this determination must be made before the design of the monitoring solution itself. Our approach shows how to use a variety of machine learning techniques to systematically derive, tune, and optimize the vitals to be monitored before accessing the continuous monitoring data. We show the effectiveness of our approach in the design of a wearable for early detection of COVID-19 infections in symptomatic patients. © 2023 IEEE.

Biosignal Databases for Training of Artificial Intelligent Systems

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will have mild to moderate respiratory diseases, however, the elderly population is the most vulnerable, becoming seriously ill, requiring continuous medical follow-up. In this sense, technologies were developed that allow continuous and individual monitoring of patients, in a home environment, namely through wearable devices, thus avoiding continuous hospitalization. Thus, these devices allow great improvements in data analysis methods since they can continuously acquire the physiological signals of an individual and process them in real-Time through artificial intelligence (AI) methods. However, training of AI methods is not straightforward, requiring a large amount of data. In this study, we review the most common biosignal databases available in the literature. A total of thirteen databases were selected. Most of the databases (9 databases) were related to ECG signal, as well as 4 databases containing signals from SPO2, Heart Rate, Blood Pressure, etc. Characteristics were described, namely: The population of the databases, data resolution, sampling rates, sample time, number of signal samples, annotated classes, data acquisition conditions, among other aspects. Overall, this study summarizes and described the public biosignals databases available in the literature, which may be important in the implementation of intelligent classification methods. © 2022 ACM.

Calculation of the Public Health Index in the Regions of the Russian Federation

Introduction: The COVID-19 pandemic has demonstrated the need to improve methods of public health assessment and approaches to the development of a system for its monitoring in the Russian Federation. Public health represents a sociomedical resource of the society, deterioration of which has a negative effect on the potential of the society to resist emerging threats. Within a series of previous studies, the authors have developed a methodological approach to calculating the public health index, the monitoring of which will facilitate managerial decisions aimed at strengthening of the potential of public health. Objective: To test a methodological approach to calculating the public health index in the regions of the Russian Federation. Materials and methods: To estimate the public health index, we applied an original methodology specially developed with account for strategic goals outlined by the Russian President and provisions of the WHO Handbook for calculation and use of the Urban Health Index. It includes correlation assessment and standardization of parameters. The components of the public health index were selected in view of the requirements established by the presidential decree on preserving the population of the country, developing the human potential, and strengthening national defense capabilities. Results: We calculated Russian regional values of the public health index for the year 2019. The year selection was determined by the absence of significant biological challenges, currently posed by the COVID-19 pandemic, and the aftermath of the pension reform. The estimated mean of the public health index in the Russian Federation in 2019 was 0.238, with extremes established in the Yamalo-Nenets Autonomous Okrug (0.458) and the Kurgan Region (0.036). Conclusions: Public health monitoring involves tracking of achieved values of the public health index and its individual constituents as they allow judgment on the potential of the society to counteract external threats. Further research should be aimed at analyzing changes in the public health index in the regions of Russia during and after large-scale biological and social challenges. It seems expedient to consider the issue of creating a national information portal devoted to public health problems in the country. © 2022, Federal Center for Hygiene and Epidemiology. All rights reserved.

Continuous monitoring is superior to manual measurements in detecting vital sign deviations in patients with COVID-19.

BACKGROUND: Patients admitted to the emergency care setting with COVID-19-infection can suffer from sudden clinical deterioration, but the extent of deviating vital signs in this group is still unclear. Wireless technology monitors patient vital signs continuously and might detect deviations earlier than intermittent measurements. The aim of this study was to determine frequency and duration of vital sign deviations using continuous monitoring compared to manual measurements. A secondary analysis was to compare deviations in patients admitted to ICU or having fatal outcome vs. those that were not. METHODS: Two wireless sensors continuously monitored (CM) respiratory rate (RR), heart rate (HR), and peripheral arterial oxygen saturation (SpO2 ). Frequency and duration of vital sign deviations were compared with point measurements performed by clinical staff according to regional guidelines, the National Early Warning Score (NEWS). RESULTS: SpO2 < 92% for more than 60 min was detected in 92% of the patients with CM vs. 40% with NEWS (p < .00001). RR > 24 breaths per minute for more than 5 min were detected in 70% with CM vs. 33% using NEWS (p = .0001). HR ≥ 111 for more than 60 min was seen in 51% with CM and 22% with NEWS (p = .0002). Patients admitted to ICU or having fatal outcome had longer durations of RR > 24 brpm (p = .01), RR > 21 brpm (p = .01), SpO2 < 80% (p = .01), and SpO2 < 85% (p = .02) compared to patients that were not. CONCLUSION: Episodes of desaturation and tachypnea in hospitalized patients with COVID-19 infection are common and often not detected by routine measurements.

Biosensors for healthcare: current and future perspectives.

Biosensors are utilized in several different fields, including medicine, food, and the environment; in this review, we examine recent developments in biosensors for healthcare. These involve three distinct types of biosensor: biosensors for in vitro diagnosis with blood, saliva, or urine samples; continuous monitoring biosensors (CMBs); and wearable biosensors. Biosensors for in vitro diagnosis have seen a significant expansion recently, with newly reported clustered regularly interspaced short palindromic repeats (CRISPR)/Cas methodologies and improvements to many established integrated biosensor devices, including lateral flow assays (LFAs) and microfluidic/electrochemical paper-based analytical devices (µPADs/ePADs). We conclude with a discussion of two novel groups of biosensors that have drawn great attention recently, continuous monitoring and wearable biosensors, as well as with perspectives on the commercialization and future of biosensors.

Portable HEPA filtration successfully augments natural-ventilation-mediated airborne particle clearance in a legacy design hospital ward.

As the severe acute respiratory syndrome coronavirus-2 pandemic has proceeded, ventilation has been recognized increasingly as an important tool in infection control. Many hospitals in Ireland and the UK do not have mechanical ventilation and depend on natural ventilation. The effectiveness of natural ventilation varies with atmospheric conditions and building design. In a challenge test of a legacy design ward, this study showed that portable air filtration significantly increased the clearance of pollutant aerosols of respirable size compared with natural ventilation, and reduced spatial variation in particle persistence. A combination of natural ventilation and portable air filtration is significantly more effective for particle clearance than either intervention alone.

Detecting Patient Deterioration Early Using Continuous Heart rate and Respiratory rate Measurements in Hospitalized COVID-19 Patients.

BACKGROUND: Presenting symptoms of COVID-19 patients are unusual compared with many other illnesses. Blood pressure, heart rate, and respiratory rate may stay within acceptable ranges as the disease progresses. Consequently, intermittent monitoring does not detect deterioration as it is happening. We investigated whether continuously monitoring heart rate and respiratory rate enables earlier detection of deterioration compared with intermittent monitoring, or introduces any risks. METHODS: When available, patients admitted to a COVID-19 ward received a wireless wearable sensor which continuously measured heart rate and respiratory rate. Two intensive care unit (ICU) physicians independently assessed sensor data, indicating when an intervention might be necessary (alarms). A third ICU physician independently extracted clinical events from the electronic medical record (EMR events). The primary outcome was the number of true alarms. Secondary outcomes included the time difference between true alarms and EMR events, interrater agreement for the alarms, and severity of EMR events that were not detected. RESULTS: In clinical practice, 48 (EMR) events occurred. None of the 4 ICU admissions were detected with the sensor. Of the 62 sensor events, 13 were true alarms (also EMR events). Of these, two were related to rapid response team calls. The true alarms were detected 39 min (SD = 113) before EMR events, on average. Interrater agreement was 10%. Severity of the 38 non-detected events was similar to the severity of 10 detected events. CONCLUSION: Continuously monitoring heart rate and respiratory rate does not reliably detect deterioration in COVID-19 patients when assessed by ICU physicians.

The Past, Present and Future of the Healthcare Delivery System through Digitalization

The prevalence of chronic diseases and the recent global spread of deadly communicable diseases such as COVID-19 has resulted in changing global health needs which require new and adaptable approaches towards delivering healthcare. Healthcare digitization has aided in dealing with old and new healthcare issues and there is still enormous untapped power. Enough power to transform healthcare delivery systems when safe and accurate aggregation of individual health data is achieved. We explore a typical patient's healthcare pathway for two major chronic conditions, namely cardiovascular and mental diseases. The aim is to reveal healthcare delivery approach changes as used in the past, present, with a look to the future to manage these diseases. Further, we provide a holistic overview of the technologies behind the digital healthcare transformation. The study also offers a roadmap which depicts the evolution in the healthcare delivery system enabled by these technological health advancements and concludes with a critical evaluation of such systems. IEEE

Association of Continuously Measured Vital Signs With Respiratory Insufficiency in Hospitalized COVID-19 Patients: Retrospective Cohort Study.

BACKGROUND: Continuous monitoring of vital signs has the potential to assist in the recognition of deterioration of patients admitted to the general ward. However, methods to efficiently process and use continuously measured vital sign data remain unclear. OBJECTIVE: The aim of this study was to explore methods to summarize continuously measured vital sign data and evaluate their association with respiratory insufficiency in COVID-19 patients at the general ward. METHODS: In this retrospective cohort study, we included patients admitted to a designated COVID-19 cohort ward equipped with continuous vital sign monitoring. We collected continuously measured data of respiratory rate, heart rate, and oxygen saturation. For each patient, 7 metrics to summarize vital sign data were calculated: mean, slope, variance, occurrence of a threshold breach, number of episodes, total duration, and area above/under a threshold. These summary measures were calculated over timeframes of either 4 or 8 hours, with a pause between the last data point and the endpoint (the "lead") of 4, 2, 1, or 0 hours, and with 3 predefined thresholds per vital sign. The association between each of the summary measures and the occurrence of respiratory insufficiency was calculated using logistic regression analysis. RESULTS: Of the 429 patients that were monitored, 334 were included for analysis. Of these, 66 (19.8%) patients developed respiratory insufficiency. Summarized continuously measured vital sign data in timeframes close to the endpoint showed stronger associations than data measured further in the past (ie, lead 0 vs 1, 2, or 4 hours), and summarized estimates over 4 hours of data had stronger associations than estimates taken over 8 hours of data. The mean was consistently strongly associated with respiratory insufficiency for the three vital signs: in a 4-hour timeframe without a lead, the standardized odds ratio for heart rate, respiratory rate, and oxygen saturation was 2.59 (99% CI 1.74-4.04), 5.05 (99% CI 2.87-10.03), and 3.16 (99% CI 1.78-6.26), respectively. The strength of associations of summary measures varied per vital sign, timeframe, and lead. CONCLUSIONS: The mean of a vital sign showed a relatively strong association with respiratory insufficiency for the majority of vital signs and timeframes. The type of vital sign, length of the timeframe, and length of the lead influenced the strength of associations. Highly associated summary measures and their combinations could be used in a clinical prediction score or algorithm for an automatic alarm system.

A NOVEL SECURITY FRAMEWORK FOR PREVENTING ATTACKS IN SENTIMENT ANALYSIS

Nowadays, Twitter data-based sentiment analysis is the mainly common topic in Natural Language Processing (NLP). Nevertheless, security attacks on Twitter data are increased day by day because hackers and the attacks will reduce the performance of sentiment analysis. Many kinds of research are developed to overcome this problem, but there are no accurate results found. So this current research proposed a novel Ant Lion honeypot with Regression (ALHR) for detecting the attacks and continuous monitoring of data. Moreover, the fitness function of the introduced replica is used for preventing attacks and continuous monitoring. Also, this model utilizes Twitter-based data about the corona disease 2019 (COVID-19) for detecting attacks and enhances the classification of sentiments using continuous monitoring. For verifying the effectiveness of ALHR technique, launch attacks in classification layer. The developed technique is executed in Python, and the achieved performance metrics are compared with another existing replica regarding the accuracy, recall, precision, F-measure, and error rate. Finally, the ALHR technique enhances the sentiment analysis and provides continuous monitoring. © 2022, Engg Journals Publications. All rights reserved.

Detecting COVID-19 from respiratory sound recordings with transformers

Auscultation is an established technique in clinical assessment of symptoms for respiratory disorders. Auscultation is safe and inexpensive, but requires expertise to diagnose a disease using a stethoscope during hospital or office visits. However, some clinical scenarios require continuous monitoring and automated analysis of respiratory sounds to pre-screen and monitor diseases, such as the rapidly spreading COVID-19. Recent studies suggest that audio recordings of bodily sounds captured by mobile devices might carry features helpful to distinguish patients with COVID-19 from healthy controls. Here, we propose a novel deep learning technique to automatically detect COVID-19 patients based on brief audio recordings of their cough and breathing sounds. The proposed technique first extracts spectrogram features of respiratory recordings, and then classifies disease state via a hierarchical vision transformer architecture. Demonstrations are provided on a crowdsourced database of respiratory sounds from COVID-19 patients and healthy controls. The proposed transformer model is compared against alternative methods based on state-of-the-art convolutional and transformer architectures, as well as traditional machine-learning classifiers. Our results indicate that the proposed model achieves on par or superior performance to competing methods. In particular, the proposed technique can distinguish COVID-19 patients from healthy subjects with over 94% AUC. © 2022 SPIE.

IoT Enabled Therapeutical Assistance Unit for COPD Patient Drug Delivery and Management

Chronic Obstructive Pulmonary Disease (COPD) results in progressive airflow limitation caused by an inflammatory reaction in the lungs due to the inhalation of noxious particles of gas, and this is the third dominant cause of death globally. Proper management and care can reduce risk factors and complications to improve the quality of life. In the covid situation due to the sudden increase in workload for doctors and Nurses, the regular COPD patients of the hospital were to face problems in proper drug administration and monitoring during infusion and nebulization and, also proper continuous monitoring of some critical parameters like SPO2, ECG, etc. This proper monitoring and controlling of critical cases by the physician at a distinct place away from the patient by smartphone is very much essential. To fulfill these requirements the proposed system involves the integration of medical devices that supports two different drug delivery system with an oxygen facility and continuous monitoring of vital parameters. This is real-time implantation using Atmega328 IC and IOT to overcome the technical shortcoming and to enhance the safety of COPD patients. In one section IR sensor with IR LED and the photodiode is used for monitoring drop rate count and volume infused during infusion. In the other section, the Arduino Nano microcontroller directs the MOSFET to control the speed and time during Nebulization. Here, the On and Off of the system can be done both manually and digitally. Blynk application is used to read and visualize sensor data and to control the hardware remotely. This system saves resources and time simultaneously and aids to improves the patient's quality of life. © 2022 IEEE.

Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras.

Heart rate (HR) and respiratory rate (fR) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique for continuously monitoring HR and fR via a multi-ROI approach based on the spectral analysis of RGB video frames recorded with a mobile device (i.e., a smartphone's camera). The respiratory signal was estimated by the motion of the chest, whereas the cardiac signal was retrieved from the pulsatile activity at the level of right and left cheeks and forehead. Videos were recorded from 18 healthy volunteers in four sessions with different user-camera distances (i.e., 0.5 m and 1.0 m) and illumination conditions (i.e., natural and artificial light). For HR estimation, three approaches were investigated based on single or multi-ROI approaches. A commercially available multiparametric device was used to record reference respiratory signals and electrocardiogram (ECG). The results demonstrated that the multi-ROI approach outperforms the single-ROI approach providing temporal trends of both the vital parameters comparable to those provided by the reference, with a mean absolute error (MAE) consistently below 1 breaths·min-1 for fR in all the scenarios, and a MAE between 0.7 bpm and 6 bpm for HR estimation, whose values increase at higher distances.

IoT Based Patient Safety Monitoring System in Hospitals

The exponential surge in India's coronavirus infections over the past months has swamped the health care system, which limited the supply of medical oxygen cylinders. Dozens of hospitals in several Indian cities and towns have run short of oxygen cylinders and also lack continuous monitoring of patients due to labor shortage and patient admitted exponentially. This leads to the lack of attention to patients who advanced to critical complications. To overcome this, it is proposed to automatically measure the pulse rate, the oxygen level in the cylinder, and glucose level by weight with the help of a microcontroller and load cell. The real-time data send to hospital management to change or resupply. It will lead to the continuous monitoring of the patient and reduce the risk. The proposed method will ensure patient safety and also has alert the doctors if any unforeseen problems or accident occurs. © 2022 IEEE.

Non-invasive Machine Learning approach for classifying Blood Pressure using PPG Signals in COVID situation

Blood pressure is one of the possible factors that cause cardiovascular diseases. It is one of the useful parameters for early detection, using which we can diagnose and treat cardiac diseases. Continuous monitoring of blood pressure can help us to maintain good health and to have a longer life span. At present, BP estimation is principally based on cuff-based techniques[1] which can cause inconvenience or discomfort to patients. ECG is one of the cuff-based methods to estimate or classify Blood Pressure. Nowadays, Studies are taking place on non-invasive and cuff-less-based methods and one of them is PPG signals (photoplethysmography). PPG is a non-invasive optical method for estimating the blood volume changes per pulse[21]. We can also say that the PPG signal indicates the mechanical activity of the heart[8]. In this paper, we proposed a non-invasive method using a whole-based approach that uses raw values from PPG signals to classify blood pressure. Using Machine learning algorithms to classify blood pressure is a feasible way for the analysis and predicting the results. In this paper, we applied various machine learning models(Random forest, Gradient boost, and XGBoost). In order to avoid overfitting, we used Repeated-stratified k-fold cross-validation and obtained enough accuracy in classifying the BP. when compared to the parameter-based method, our method(whole based method) is independent of the PPG waveform of a signal. © 2021 IEEE.

CheckING: An IoT Device for COVID-19 Monitoring and Prevention

The COVID-19 pandemic has spread rapidly around the world forcing people to isolate at home and collapsing hospitals causing millions of deaths. The continuous and efficient monitoring of those who showed symptoms jointly with the analysis of the environment conditions to avoid the spread of the virus gave rise to the development of different technological alternatives. In the present work, a comprehensive device with multi-parameter sensing has been designed, emphasizing the integration of physiological and environmental parameters with remote monitoring, of the interest in the current pandemic context. © 2021 IEEE.

The Use of Wearable Pulse Oximeters in the Prompt Detection of Hypoxemia and During Movement: Diagnostic Accuracy Study.

BACKGROUND: Commercially available wearable (ambulatory) pulse oximeters have been recommended as a method for managing patients at risk of physiological deterioration, such as active patients with COVID-19 disease receiving care in hospital isolation rooms; however, their reliability in usual hospital settings is not known. OBJECTIVE: We report the performance of wearable pulse oximeters in a simulated clinical setting when challenged by motion and low levels of arterial blood oxygen saturation (SaO2). METHODS: The performance of 1 wrist-worn (Wavelet) and 3 finger-worn (CheckMe O2+, AP-20, and WristOx2 3150) wearable, wireless transmission-mode pulse oximeters was evaluated. For this, 7 motion tasks were performed: at rest, sit-to-stand, tapping, rubbing, drinking, turning pages, and using a tablet. Hypoxia exposure followed, in which inspired gases were adjusted to achieve decreasing SaO2 levels at 100%, 95%, 90%, 87%, 85%, 83%, and 80%. Peripheral oxygen saturation (SpO2) estimates were compared with simultaneous SaO2 samples to calculate the root-mean-square error (RMSE). The area under the receiver operating characteristic curve was used to analyze the detection of hypoxemia (ie, SaO2<90%). RESULTS: SpO2 estimates matching 215 SaO2 samples in both study phases, from 33 participants, were analyzed. Tapping, rubbing, turning pages, and using a tablet degraded SpO2 estimation (RMSE>4% for at least 1 device). All finger-worn pulse oximeters detected hypoxemia, with an overall sensitivity of ≥0.87 and specificity of ≥0.80, comparable to that of the Philips MX450 pulse oximeter. CONCLUSIONS: The SpO2 accuracy of wearable finger-worn pulse oximeters was within that required by the International Organization for Standardization guidelines. Performance was degraded by motion, but all pulse oximeters could detect hypoxemia. Our findings support the use of wearable, wireless transmission-mode pulse oximeters to detect the onset of clinical deterioration in hospital settings. TRIAL REGISTRATION: ISRCTN Registry 61535692; http://www.isrctn.com/ISRCTN61535692. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2019-034404.

Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study.

BACKGROUND: Continuous telemonitoring of vital signs in a clinical or home setting may lead to improved knowledge of patients' baseline vital signs and earlier detection of patient deterioration, and it may also facilitate the migration of care toward home. Little is known about the performance of available wearable sensors, especially during daily life activities, although accurate technology is critical for clinical decision-making. OBJECTIVE: The aim of this study is to assess the data availability, accuracy, and concurrent validity of vital sign data measured with wearable sensors in volunteers during various daily life activities in a simulated free-living environment. METHODS: Volunteers were equipped with 4 wearable sensors (Everion placed on the left and right arms, VitalPatch, and Fitbit Charge 3) and 2 reference devices (Oxycon Mobile and iButton) to obtain continuous measurements of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and temperature. Participants performed standardized activities, including resting, walking, metronome breathing, chores, stationary cycling, and recovery afterward. Data availability was measured as the percentage of missing data. Accuracy was evaluated by the median absolute percentage error (MAPE) and concurrent validity using the Bland-Altman plot with mean difference and 95% limits of agreement (LoA). RESULTS: A total of 20 volunteers (median age 64 years, range 20-74 years) were included. Data availability was high for all vital signs measured by VitalPatch and for HR and temperature measured by Everion. Data availability for HR was the lowest for Fitbit (4807/13,680, 35.14% missing data points). For SpO2 measured by Everion, median percentages of missing data of up to 100% were noted. The overall accuracy of HR was high for all wearable sensors, except during walking. For RR, an overall MAPE of 8.6% was noted for VitalPatch and that of 18.9% for Everion, with a higher MAPE noted during physical activity (up to 27.1%) for both sensors. The accuracy of temperature was high for VitalPatch (MAPE up to 1.7%), and it decreased for Everion (MAPE from 6.3% to 9%). Bland-Altman analyses showed small mean differences of VitalPatch for HR (0.1 beats/min [bpm]), RR (-0.1 breaths/min), and temperature (0.5 °C). Everion and Fitbit underestimated HR up to 5.3 (LoA of -39.0 to 28.3) bpm and 11.4 (LoA of -53.8 to 30.9) bpm, respectively. Everion had a small mean difference with large LoA (-10.8 to 10.4 breaths/min) for RR, underestimated SpO2 (>1%), and overestimated temperature up to 2.9 °C. CONCLUSIONS: Data availability, accuracy, and concurrent validity of the studied wearable sensors varied and differed according to activity. In this study, the accuracy of all sensors decreased with physical activity. Of the tested sensors, VitalPatch was found to be the most accurate and valid for vital signs monitoring.

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.