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An Investigation of Signal Processing Techniques for Monitoring of the Heart Abnormalities

Ghotbi Ravandi, Amir | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 42690 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Ghorshi, Alireza
  7. Abstract:
  8. In this thesis we have investigated and improved the signal processing techniques which are used for monitoring the heart abnormalities in terms of ECG (ElectroCardioGram) signals in order to detect heart attacks before they occur. De-noising ECG signals are one of the most important research topics in computer and electrical engineering fields. There are many different algorithms for de-noising signals in various domains. It usually is needed to propose a suitable algorithm for each specific system. In some cases instead of developing a new algorithm, we could modify the available ones for de-noising in our system. ECG signals are output from an electrocardiograph which measures electrical voltage in the heart. and consists of 5 features (PQRST). P waves show the electrical impulse starting at the sinoatrial node and proceeding through the atria, causing them to contract. QRS shows the impulse going through the ventricles starting at the AV node, causing the systolic contraction. T waves show repolarization; that is, resetting of the conducting system for the next impulse. ECG signals have three characteristics; Duration, measured in fraction of a second, Amplitude, measured in millivolts (mV) and Configuration, a more subjective criterion referring to the shape and appearance of a wave. In this thesis, the main idea is to design a robust algorithm for extracting the ECG signal parameters and to de-noising the ECG signals. We used Independent Component Analysis (ICA) for this purpose. The results show that the AMUSE algorithm has higher performance to de-noise ECG signals among other algorithms. Also as the benchmark we used the MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia database which consisting of 48 half-hour ECG recordings and 3 half-hour recordings of noise typical in ambulatory ECG recordings
  9. Keywords:
  10. Signal Processing ; Cardiovascular Signals ; Electrocardiogram ; Noise Reduction ; Real Time Monitoring System

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