ECG Data Analysis
Sept 2024 – Dec 2024Processed and cleaned raw ECG signals in Python/Pandas, applying digital filters and performing feature engineering (time-domain and frequency-domain metrics) for anomaly detection.
PythonPandasscikit-learnSVMRandom ForestMatplotlibSeaborn
Highlights
- —Processed and cleaned raw ECG signals in Python/Pandas, applying digital filters and feature engineering (time-domain and frequency-domain metrics) for anomaly detection
- —Developed and validated machine learning classifiers (SVM, Random Forest) in scikit-learn to identify arrhythmias, achieving over 90% accuracy
- —Visualized results with Matplotlib and Seaborn for comprehensive analysis
Results
- —Achieved over 90% accuracy in arrhythmia classification