<b>Datos_individuos</b>
Creators: name: ortiz, Juan david
Publisher: name: figshare
Year: 2024
Subjects: classificationCode: 320202
subject: Clinical chemistry (incl. diagnostics)

description: <b>What is Cholesterol Concentration and Its Relationship with PPG Signals?</b>Cholesterol concentration refers to the total amount of cholesterol present in the blood, measured in milligrams per deciliter (mg/dL). While cholesterol is a vital fatty substance necessary for the body, elevated levels can increase the risk of cardiovascular diseases such as arteriosclerosis, heart attacks, and strokes. Monitoring cholesterol levels is crucial for the prevention and management of these conditions (Awasthi &amp; Dixit, 2021; Ma &amp; Shieh, 2006).On the other hand, photoplethysmography (PPG) signals are non-invasive measurements that use light to detect changes in blood volume within peripheral tissues, typically in fingers or ears. The PPG signal is directly related to cardiac activity and blood flow (Allen, 2007; Maeda et al., 2011).Recent studies have investigated the potential of PPG signals to estimate certain biometric parameters, including cholesterol concentration. Elevated cholesterol levels can affect the elasticity of blood vessels, suggesting that characteristics of PPG signals may provide indirect information about blood cholesterol levels (Maqsood et al., 2021; Chatterjee &amp; Roy, 2018).The dataset presented includes both PPG signal recordings from subjects and their cholesterol levels. By combining these two data sources, researchers can analyze correlations or specific patterns that may allow for estimating total cholesterol from PPG signals. This approach aims to advance the development of non-invasive methods for cholesterol detection and monitoring, potentially offering a simpler and more accessible alternative to traditional blood tests (Zhang et al., 2022).A total of 46 participants were recruited (14 men and 32 women), aged between 32 and 80 years, with a mean age of 54.5 years. Detailed information was collected for each individual, including key demographic and biometric data, as well as indicators related to their cardiovascular health. Collected parameters included weight, height, and cholesterol levels, along with information on whether participants were under medical treatment at the time of the study.PPG signals were obtained using standard measurement devices for this technique, although specific details are not provided in the current dataset. Cholesterol levels were measured using standard clinical methods. Additional details regarding the specific conditions under which the signals were collected, such as the time of day or whether subjects were at rest, have not been recorded.Data were organized in an Excel spreadsheet to facilitate analysis, structured as follows:RecordSexAgeWeightHeightMedicCholesterol1F45681.65Yes2102M56851.78No190This dataset, named <b>SUBJECT_DATA_21_06_2024_Sterol.xlsx</b>, can be found in the folder called <b>Data_individuals</b>, allowing for a comparative analysis between cholesterol levels and obtained PPG signals to validate tools for cardiovascular health diagnosis and monitoring.<b>Data Organization Method</b>In this work, 46 .MAT files were utilized, each named according to the format <b>subjectXX_PPG.mat</b>, along with an Excel file titled <b>SUBJECT_DATA_21_06_2024_Sterol.xlsx</b> mentioned earlier. This approach ensures that relevant characteristics of subjects, including age, gender, and cholesterol levels, are stored for each record. The MATLAB code used to organize these data can be found in the attached folder named <b>data</b>, specifically in the file named <b>data_juan.m</b>.To ensure that the organized data is easily accessible and downloadable, it has been saved in two formats: .MAT, compatible with MATLAB, and .CSV, usable in other programming languages like Python and R The code for the visualization in Python is located in a file with the same name in the folder already mentioned. The lines of code for saving the organized information are as follows:<pre>matlabCopiar código<pre>% Save the organized information<br>save('organized_data.mat', 'subject_data'); % Save as .MAT<br><br><b>% Save as CSV</b><br>data_table = struct2table(subject_data);<br>writetable(data_table, 'organized_data.csv'); % Save as CSV<br></pre></pre>The above code is included at the end of the script found in the <b>data_juan.m</b> file.<b>Observation</b>The .MAT files utilized contain three variables: <b>fs</b>, <b>t</b>, and <b>y</b>, which represent:<b>fs</b>: Generally represents the sampling frequency in Hz.<b>t</b>: Refers to the time vector, an array containing the times corresponding to each sample.<b>y</b>: Contains the data of interest (e.g., PPG signals).To visualize the data contained in the .MAT files, a document named <b>juanvisualiza.m</b> has been included, containing code designed to plot the graphs of the data. This script allows for visual exploration of the PPG signals from each subject, facilitating data analysis and identification of significant patterns. Utilizing this document is essential for complementing the interpretation of the organized data, providing a graphical representation that enhances the understanding of the results obtained in the study.<b>Scientific Validation</b><b>Pearson Correlation Coefficient</b>: This method is useful for measuring the linear correlation between PPG signals and cholesterol levels. For instance, previous studies have found that the Pearson correlation coefficient can be used to compare intervals between biometric signals, such as ECG and PPG, showing high correlation (r &gt; 0.9).<b>Linear Regression Analysis</b>: Linear regression can be used to model the relationship between a dependent variable (in this case, cholesterol level) and one or more independent variables (the characteristics of the PPG signals). A simple linear regression model can test whether changes in PPG signals (e.g., amplitude or time variability) predict changes in cholesterol levels (Maeda et al., 2011).The equation of a linear regression model would be:Y=β0+β1X+ϵ Where:Y is the cholesterol level.X represents a characteristic of the PPG signal.β0 is the intercept (constant).β1​ is the regression coefficient indicating the effect of X on Y.ϵ is the error term.<b>Advanced Models: Neural Networks and SVM</b>: More advanced models such as artificial neural networks (ANN) and support vector machines (SVM) can be used to predict cholesterol from multiple characteristics extracted from PPG signals.<b>Artificial Neural Networks (ANN)</b>: ANNs are useful when there is a large dataset with many input variables. They can capture complex relationships between PPG signals and cholesterol through interconnected layers of neurons, adjusting weights and biases to optimize predictive performance.<b>Basic Structure</b>:Input layer: features of the PPG signal.Hidden layers: where nonlinear interactions are processed.Output layer: prediction of cholesterol.<b>Support Vector Machines (SVM)</b>: SVMs are classification models that can be applied to find an optimal relationship between features extracted from PPG signals and different cholesterol levels. They aim to find a hyperplane that separates the data into different cholesterol classes (normal, high).Both approaches are widely used in biomedical research due to their ability to handle large datasets and identify complex patterns, such as those that could be obtained from non-invasive physiological signals like PPG.<b>Benchmarking</b><b>Validation and Extension of Health Biomarker Estimation Models through PPG Signals: A Comparative Approach with Blood Pressure Studies</b>: A relevant study conducted by Omer et al. (2024) utilized PPG signals and regression models to estimate blood pressure through a novel transfer learning-based approach. Similar to our analysis, features derived from the PPG signal, such as peak timings, were used to predict a cardiovascular variable. Our dataset can validate and extend these approaches by utilizing cholesterol levels instead of blood pressure. This provides a robust foundation for adapting existing methods to other health biomarkers, such as cholesterol, through non-invasive monitoring techniques.<b>Clinical Context Details</b>The use of PPG signals to estimate cholesterol levels has significant clinical implications, as it would enable the development of non-invasive methods for diagnosing cardiovascular diseases. This could reduce the need for traditional blood tests, improving accessibility and decreasing healthcare costs. Additionally, it could facilitate continuous patient monitoring, which is crucial in preventing and managing chronic cardiovascular diseases.<b>Interdisciplinary Collaboration</b>This dataset paves the way for future collaborations across different fields. Researchers in biomedical engineering could utilize the data to enhance PPG signal acquisition and processing techniques, while cardiologists can validate clinical results related to cholesterol and cardiovascular health. Computer scientists and machine learning engineers could develop advanced predictive models for estimating biomarkers like cholesterol based on non-invasive signals. This interdisciplinary approach strengthens our data's potential to serve as a platform for joint studies, contributing to advancements in non-invasive disease detection.<b>References</b>Allen, J. (2007). <i>Photoplethysmography and its application in clinical physiological measurement</i>. Physiological Measurement, 28(3), R1-R39.Awasthi, D. K., &amp; Dixit, A. (2021). <i>International Journal of Engineering Technology Research and Management</i>.Chatterjee, A., &amp; Roy, U. K. (2018). <i>Biomedical and Pharmacology Journal</i>, 11(4), 1885-1893.Ma, H., &amp; Shieh, K. J. (2006). <i>Cholesterol and Human Health</i>. The Journal of American Science, 2(1), 46-50.Maeda, Y., Sekine, M., &amp; Tamura, T. (2011). <i>The advantages of wearable green reflected photoplethysmography</i>. Journal of Medical Systems, 35, 829-834.Maqsood, S., et al. (2021). <i>IEEE Access</i>, 9, 138817-138833.Zhang, Y., et al. (2022). <i>IEEE Journal of Biomedical and Health Informatics</i>, 26(12), 5907-5917.