A new real-time algorithm for obtaining the ECG-derived respiration (EDR) signal from the electrocardiogram (ECG)

The purpose of the work reported in this thesis was to develop and implement a realtime algorithm for estimating the baseline wander noise and subsequently estimate the ECG-Derived Respiration signal (EDR) from a single lead electrocardiogram (ECG). The objective was achieved using digital signal processing techniques (DSP). In this work, DSP techniques were used to analyze the ECG waveform and filter out various different noise components that ride on the ECG signal. Related computation was performed using numerical analysis techniques such as interpolation to estimate the baseline noise and the EDR signal.; In this work, the author developed a real-time algorithm (EDR_T_P_knot.m) using pseudo serial-shift registers (finite length arrays) that estimates and removes the baseline wander noise, and estimates the EDR signal. The author implemented the algorithm in MATLAB for its user-friendly graphical user-interface (GUI). This algorithm could also be implemented in other high level programming languages such a C, C++, etc.; The new T-P knot algorithm has three major sections. The first section uses the Pan & Tompkins algorithm [3] to obtain the QRS proximity. The second section, estimates the baseline wander noise based on 3rd-order interpolation of the baseline at midpoints in each R-R interval. The final section estimates the EDR signal using 3rd-order interpolation of the ECG R-Wave respiration modulation calculated using the final estimate of the R-Wave peaks. The implemented algorithm was tested on real ECG data from the PhysioBank FANTASIA database for its efficacy. The test results confirm that the implemented algorithm successfully detected the QRS complexes, provided an accurate estimate of the baseline wander noise and consequently an accurate estimate of the respiration rate from the derived respiration signal.; The results confirm that the author successfully achieved the desired objective of developing and implementing a real-time algorithm for estimating the baseline noise and hence the ECG-Derived Respiration (EDR) signal.