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Screening of Ischemic Heart Disease based on PPG Signals using Machine Learning Techniques
Insights
A new low-cost method using finger-tip photoplethysmography (PPG) signals can effectively screen for Ischemic Heart Disease (IHD). This technique offers a vital, accessible tool for remote populations, improving cardiac health monitoring.
Area of Science:
- Biomedical Engineering
- Cardiology
- Signal Processing
Background:
- Cardiac ailments are rising, necessitating accessible diagnostic tools.
- Current heart disease detection methods are expensive and require specialized equipment.
- There is a critical need for low-cost, easily deployable screening techniques.
Purpose of the Study:
- To explore the potential of finger-tip photoplethysmography (PPG) signals for Ischemic Heart Disease (IHD) detection.
- To develop an accessible and affordable screening method for IHD.
- To evaluate machine learning classifiers for IHD identification using PPG signals.
Main Methods:
- Collected and analyzed time-domain features from finger-tip PPG signals.
- Employed various machine learning algorithms including Decision Trees, Discriminant Analysis, Logistic Regression, Support Vector Machine, KNN, and Boosted Trees for classification.
- Utilized confusion matrix to assess ten performance metrics, including accuracy, sensitivity, and specificity.
Main Results:
- The Boosted Trees classifier achieved high performance metrics: 0.94 accuracy, 0.95 sensitivity, 0.95 specificity, and 0.97 precision.
- Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) were calculated to validate classification robustness.
- The study demonstrated the effectiveness of PPG signal analysis for IHD patient identification.
Conclusions:
- Finger-tip PPG signal analysis presents a promising, low-cost, and accessible method for IHD screening.
- This technique can significantly benefit individuals in remote and underserved regions.
- Machine learning classifiers, particularly Boosted Trees, show strong potential for reliable IHD detection using PPG data.
Abstract:
The increasing rate of cardiac ailments has led to the rise in the scrutinization of ones cardiac health. The prevalent techniques for detecting heart diseases are costly and require expert supervision as well as modern equipment. Thus there is a need for an alternative low cost and easily available technique. Finger-tip photoplethysmography (PPG) signals can be used for identifying Ischemic Heart Disease (IHD). This technique of screening the disease will be very helpful to the inhabitants of remote, underdeveloped and unprivileged areas. Time-domain analysis of the signal was done for extracting different features. Segregation of diseased and healthy subjects was performed using Decision Trees, Discriminant Analysis, Logistic Regression, Support Vector Machine, KNN, and Boosted trees. Ten different performance metrics was studied using the confusion matrix. After analysis, the accuracy, sensitivity, specificity, and precision of 0.94, 0.95, 0.95 and 0.97 respectively was obtained using Boosted tress classifier. ROC and AUC were calculated to establish the robustness of the classification methods for determining IHD patients.
