Multiple diagnosis based on photoplethysmography: hematocrit, SpO 2 , pulse, and respiration

Yoon, Gilwon; Lee, Jong Youn; Jeon, Kug Jin; Park, Kun-Kook; Yeo, Hyung S.; Hwang, Hyun Tae; Kim, Hong Sig; Hwang, In-Duk

doi:10.1117/12.482947

Cited by 14 publications

(9 citation statements)

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“…The pulse oximeter is the most commonly used mobile monitoring device for measuring patient oxygen saturation levels and heart rate (HR) [ 1 , 2 ]. Its popularity is due to its advantages as a non-invasive, inexpensive, and convenient screening tool that is remarkably easy to use and comfortable for patients.…”

Section: Introductionmentioning

confidence: 99%

Optimal Signal Quality Index for Photoplethysmogram Signals

2016

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A photoplethysmogram (PPG) is a noninvasive circulatory signal related to the pulsatile volume of blood in tissue and is typically collected by pulse oximeters. PPG signals collected via mobile devices are prone to artifacts that negatively impact measurement accuracy, which can lead to a significant number of misleading diagnoses. Given the rapidly increased use of mobile devices to collect PPG signals, developing an optimal signal quality index (SQI) is essential to classify the signal quality from these devices. Eight SQIs were developed and tested based on: perfusion, kurtosis, skewness, relative power, non-stationarity, zero crossing, entropy, and the matching of systolic wave detectors. Two independent annotators annotated all PPG data (106 recordings, 60 s each) and a third expert conducted the adjudication of differences. The independent annotators labeled each PPG signal with one of the following labels: excellent, acceptable or unfit for diagnosis. All indices were compared using Mahalanobis distance, linear discriminant analysis, quadratic discriminant analysis, and support vector machine with leave-one-out cross-validation. The skewness index outperformed the other seven indices in differentiating between excellent PPG and acceptable, acceptable combined with unfit, and unfit recordings, with overall F1 scores of 86.0%, 87.2%, and 79.1%, respectively.

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Section: Introductionmentioning

confidence: 99%

Optimal Signal Quality Index for Photoplethysmogram Signals

2016

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“…The IoT node is responsible to reads the output of the sensor, using an external 8-bit ADC at a 50 Hz sampling rate. By applying the signal processing algorithms on the PPG signal, the heart rate, blood oxygen saturation (SpO2), and respiratory rhythm can be extracted [39]. For measuring the SpO2 from the signal, first, an average of five subsequent samples of the signals (A1 and A2) and offsets (D1 and D2) for red and infrared waveforms (indexed as 1 and 2, respectively) are measured.…”

Section: Data Acquisitionmentioning

confidence: 99%

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

et al. 2020

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In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this paper, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the coronavirus exposure risk.

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“…According to the Beer-Lambert law, blood volume changes represent optical path length (Dd) to generate AC as heartbeats [22]. It has traditionally been used in the healthcare domain to measure blood oxygen saturation [23] and heart rate [24] with a pulse oximeter. However, PPG signals can be analysed to benefit many areas, like the automotive field, to get information about driver behaviour [25], monitoring blood circulatory condition, breathing, and subjective analysis [26].…”

Section: Background and Related Workmentioning

confidence: 99%

A Benchmark Study of Machine Learning for Analysis of Signal Feature Extraction Techniques for Blood Pressure Estimation Using Photoplethysmography (PPG)

Maqsood¹,

Springer

et al. 2021

IEEE Access

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Cardiovascular related diseases are the most significant health concern around the globe. The most crucial health indicator is blood pressure because it gives essential information about the health of a patient's heart. Cardiovascular diseases can be detected early and prevented if blood pressure is monitored continuously and regularly. Blood pressure cuffs, which are widely used to control blood flow in the arm or wrist when measuring blood pressure, are not practical for continuous blood pressure measurement. However, biosignals can be used for blood pressure estimation; but it is still critical and challenging. In this paper, we conducted a comprehensive analysis of feature extraction techniques for blood pressure estimation by using PPG signals. The feature extraction techniques were further divided into three subgroups to analyse the significance of each group. Group A includes timebased features; group B presents statistical feature extraction, and group C presents frequency domain-based features. The analysis employed several machine learning algorithms and compared their performance from many perspectives for the first time, to the best of our knowledge. The experimental results from two publicly available datasets demonstrated that the set of features belonging to group A were more reliable than other techniques for blood pressure estimation. We found that deep learning models achieved better performance than all traditional machine learning methods. We also found that the GRU model and Bi-LSTM achieved the best performance for time-domain features for blood pressure estimation. We believe the findings of this benchmark study will help researchers choose the most appropriate method for feature extraction and machine learning algorithms.

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Multiple diagnosis based on photoplethysmography: hematocrit, SpO 2 , pulse, and respiration

Cited by 14 publications

References 0 publications

Optimal Signal Quality Index for Photoplethysmogram Signals

Optimal Signal Quality Index for Photoplethysmogram Signals

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

A Benchmark Study of Machine Learning for Analysis of Signal Feature Extraction Techniques for Blood Pressure Estimation Using Photoplethysmography (PPG)

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