Advanced Signal Viewer

Signal Viewer is an interactive ECG signal analysis platform built using Dash (Plotly), designed to visualize and interpret biomedical signals with precision and intelligence. The system provides multiple viewing modes — including Polar Mode, Continuous Mode, and Recurrence Mode — allowing users to explore ECG signals from different analytical perspectives. Beyond visualization, Signal Viewer integrates an AI-powered diagnostic model capable of detecting abnormalities and assisting in early-stage diagnosis. The Recurrence Mode delivers advanced nonlinear time-series analysis, enabling deeper insights into cardiac dynamics and complex signal behavior. Key Highlights: Data Analysis & Visualization: Developed comprehensive ECG signal visualization using Dash, offering dynamic plots, real-time updates, and intuitive interaction for detailed signal inspection. AI Integration: Implemented a machine learning model for ECG-based diagnosis, demonstrating skills in data preprocessing, feature extraction, and predictive modeling. Advanced Analytical Modes: Designed custom analytical views — including polar transformation and recurrence mapping — for enhanced signal understanding. Dashboard Engineering: Built a responsive, user-friendly dashboard interface for efficient data exploration, suitable for both clinical and research use cases. Technical Stack: Python, Dash, Plotly, NumPy, Pandas. Impact: This project showcases strong expertise in data science, signal processing, and machine learning for biomedical applications, as wel