ECG Signal Implementation and Applications

This article discusses immediately applicable ECG (Electrocardiogram) signals and their implementations. These signals can be processed using various digital signal processing techniques, typically involving MATLAB or Python libraries like BioSPPy for filtering (bandpass: 0.5-40 Hz), R-peak detection using Pan-Tompkins algorithm, and feature extraction. The processed signals serve multiple critical applications: they enable real-time cardiac monitoring through QRS complex detection algorithms, facilitate automated diagnosis of arrhythmias using machine learning classifiers (e.g., SVM for ECG pattern recognition), and interface with medical devices like pacemakers through API-driven control systems. Furthermore, researchers have integrated ECG signals with rehabilitation robotics, implementing adaptive control algorithms that use heart rate variability (HRV) metrics to adjust therapy intensity dynamically. The signal data structure typically includes voltage amplitudes (mV), sampling rates (250-500 Hz), and timestamp arrays, allowing direct integration with healthcare IoT platforms. In summary, readily deployable ECG signals offer extensive applications in medical technology, supported by robust DSP pipelines and interoperability frameworks.