ECG Parameter Extraction and Motion Artifact Detection




Li, Tianyang

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Cardiovascular disease is the leading cause of death in the world. Long-term monitoring of heart condition through electrocardiogram (ECG) will provide vital information for prevention, early warning and detection of fatal heart disease. Recently, we developed a portable real-time ECG monitoring system which patients can use during their daily activity. The large amount of ECG data recorded needs to be processed for automatic detection and classi cation. This report focuses on the extraction of essential ECG parameters from the ECG waveforms and the detection of motion artifacts. Algorithms to calculate heart rate, QRS complex and duration, ST elevation, are designed. Due to body motion, ECG signals are often perturbed by motion induced artifacts. Motion artifacts (MA) severely a ect the detection accuracy of ECG parameters and any meaningful interpretation of ECG waveforms. Though there have been a number of methods to eliminate motion artifacts, few of them are suitable for our portable device due to their high computational complexity or extra reference signal requirement. In this project, a new algorithm to detect motion artifacts is proposed, which applies Machine Learning based on the features we extracted. Experimental results show that this algorithm correctly detects over 90% of the motion artifacts. Finally, parameter extraction and MA detection algorithms are integrated into an Android application that reads in the raw ECG signal received by a smartphone from an ECG sensor and processes the real-time signal with these algorithms to generate an ECG report.



Digital Signal Processing, Machine Learning, Motion Artifact, ECG, Java Implementation