Biomedical Signal Processing: Analyzing ECG, EEG, and EMG Signals

Understanding the electrical activity of the human body is crucial for modern healthcare diagnostics, yet analyzing noisy biological data can be challenging. This text-based course guides you through the foundational principles of biomedical signal processing, from raw data to actionable physiological insights. You will develop a solid understanding of how to acquire, filter, and interpret signals such as electrocardiograms (ECG) and electroencephalograms (EEG). You will learn how to apply modern computational tools to remove noise, detect features, and extract meaningful patterns from biological systems. What you will learn: Understand the physiological origins of major biomedical signals, including ECG, EEG, and EMG; Apply digital filtering techniques to remove artifacts, powerline interference, and baseline wander; Analyze signals in both the time and frequency domains using Fourier and wavelet transforms; Detect key signal features, such as QRS complexes in ECG data, using algorithmic approaches; Implement signal processing workflows using modern Python libraries like NumPy and SciPy; Explore basic pattern recognition and machine learning concepts for automated signal classification. The course begins with key terminology, biological origins of signals, and foundational mathematics of digital filters. You will then progress through step-by-step written explanations and code examples that demonstrate how to process noisy data and extract diagnostic features. This course is designed for beginners, engineering students, and healthcare technology enthusiasts, with no prior background in advanced signal processing required. Start reading today to unlock the power of biological data analysis.