Cardiovascular_Diseases_PPTX[1].pptxandwell

heart related diverseness

1. Cardiovascular Disease By:-Tanmay Krishna Gupta Shreyas Kumar

2. INTRODUCTION It is an introduction to diagnose cardiovascular disease using ECG reports and machine learning: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for nearly one-third of all deaths. Early and accurate diagnosis of CVDs is essential for effective treatment and prevention. Electrocardiography (ECG) is a non-invasive and widely used technique for diagnosing CVDs. However, traditional ECG interpretation relies on visual inspection and manual measurements, which can be time-consuming and subjective.

3. OBJECTIVE Machine learning (ML) is a powerful tool that can be used to automate and improve the accuracy of ECG interpretation. ML algorithms can be trained on large datasets of ECG reports and clinical data to identify patterns and predict the likelihood of CVDs. ML has the potential to revolutionize the diagnosis of CVDs by making it more accurate, efficient, and accessible.

4. METHODOLOGY Extracted from ECG images—are primarily used as inputs to a machine learning model (e.g., SVM or CNN) to classify whether a patient’s ECG is normal or indicates a specific abnormality (e.g., myocardial infarction, arrhythmia). By themselves, they do not give an instant clinical diagnosis, but combined in a trained model, they can predict if the patient’s ECG is likely-Normal (healthy heartbeat pattern),Abnormal (arrhythmias, conduction blocks),Indicative of MI (signs of infarction)

5. These are ECG images which is used to extract data using ML

6. It is the data set which was extracted from the ECG reports and were used to plot the graphs as well as was used to make diffrent ML matrix using diffrent models. Total Test Images Loaded: 186 CNN Test Accuracy: 93.01% SVM Test Accuracy: 22.04%

7. MeanHist: Average intensity of the ECG image (how “bright” the overall signal is).VarHist indicate Variance of intensities (how spread out or variable the ECG signal is) and Interpretation -A very high variance might indicate strong transitions (sharp QRS complexes), while very low variance might mean the signal is flat or low-contrast. Contrast, Correlation, Energy, Homogeneity (GLCM Texture Features) Contrast: Measures the difference between light and dark regions. Higher contrast can mean sharper wave boundaries. Correlation: Measures how correlated neighboring pixels are. High correlation can mean a more regular ECG pattern.

8. Energy: Reflects uniformity. High energy can mean repetitive or stable wave patterns. Homogeneity: Measures smoothness. High homogeneity can mean the ECG signal transitions are gentle. Interpretation: Together, these capture the shape and texture of ECG waves. For example, a myocardial infarction ECG may exhibit different texture patterns than a normal ECG. NumPeaks (Peak Count)-The number of major peaks (e.g., QRS complexes) found. Interpretation: If the ECG segment is supposed to represent a certain time window, the number of peaks can reflect heart rate or rhythm irregularities. An abnormally high or low number of peaks might indicate tachycardia or bradycardia.A machine learning model (SVM or CNN) uses these features to distinguish normal vs. abnormal ECG or classify specific pathologies (e.g., “Myocardial Infarction,” “Abnormal Heartbeat,” etc.).Direct clinical interpretation of each feature alone is limited. Instead, the model combines them (e.g., a certain pattern of histogram variance, texture features, and peak count) to produce a probabilistic prediction about the patient’s condition.

9. Key Observations & Comparisons1. Box Size Variation (Interquartile Range - IQR) • VarHist & NumPeaks → Have the largest box sizes (high variability). • Homogeneity & Energy → Have the smallest box sizes (low variability). • A larger box means the feature values are highly spread out, while a smaller box means they are closer together. KEY FINDINGS

10. 2. Median Line (Red Line in Box) • Represents the central value of each feature distribution. • Features like Homogeneity & Correlation have higher median values for all ECG classes. • Features like Contrast & NumPeaks have lower median values, especially in MI & Abnormal classes. 3. Black Lines (Whiskers) • Represent the minimum and maximum values within the range of 1.5 times the IQR. • NumPeaks & VarHist have long whiskers, indicating wide data spread. • Homogeneity & Energy have shorter whiskers, meaning less variation.

11. 4. Red Markers (Outliers) • Represent extreme values outside the whiskers. • Abnormal ECGs have more outliers in Contrast, Energy, and Homogeneity, suggesting greater fluctuation in these features. • MI & HistoryOfMI show fewer outliers, meaning these ECG types are more consistent.

12. 5. Possible Interpretation of Peaks • NumPeaks feature directly correlates with the number of peaks in the ECG signal. • MI cases show lower peaks (wide variation), while Normal cases have higher peaks (more uniformity). • Contrast & Correlation features might help distinguish Abnormal ECGs from Normal ECGs due to more variations in the spread. Final Insights for Feature Selection • Highly Significant Features for Classification: o Energy, Homogeneity, and Contrast show clear differentiation among ECG classes. o NumPeaks & VarHist show high variability and might need additional filtering. • Less Discriminative Features: o Correlation shows similar behavior across different ECG classes. o Features with high outliers (like Contrast) may introduce noise.

13. NOW FUTURE CONCLUSION Machine learning (ML) has emerged as a powerful tool for the automated analysis of ECG reports, demonstrating significant potential in improving the diagnosis and management of cardiovascular diseases (CVDs). By leveraging the ability of ML algorithms to learn complex patterns from large datasets, researchers have developed models capable of accurately detecting various CVDs, often surpassing the performance of traditional methods. The applications of ML in ECG analysis are vast and continue to expand. From identifying subtle abnormalities indicative of arrhythmias to predicting the risk of future cardiac events, ML-driven ECG interpretation offers a promising avenue for enhancing clinical decision-making. As these technologies mature and become more integrated into healthcare systems, they hold the potential to transform the landscape of CVD diagnosis, ultimately leading to improved patient outcomes.

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