Novel electrocardiogram segmentation algorithm using a multiple model adaptive estimator

Presents an electrocardiogram (ECG) processing algorithm design based on a multiple model adaptive estimator (MMAE) for a physiological monitoring system. Twenty ECG signals from the MIT ECG database were used to develop system models for the MMAE. The P-wave, QRS complex, and T-wave segments from the characteristic ECG waveform were used to develop hypothesis filter banks. The MMAE robustly locates these key temporal landmarks in the ECG signal, extracting crucial patient treatment information from the often distorted or unstable ECG waveform. By adding a threshold filter-switching algorithm to the conventional MMAE implementation, the device mimics the way a human analyzer searches the complex ECG signal for a useable temporal landmark and then branches out to find the other key wave components and their timing. Using a conditional hypothesis-testing algorithm, the MMAE correctly identified the ECG signal segments corresponding to the hypothesis models with a 96.8% accuracy-rate for the 11539 possible segments tested. The robust MMAE algorithm also detected any misalignments in the filter hypotheses and automatically restarted filters within the MMAE to synchronize the hypotheses with the incoming signal. Finally, the MMAE selects the optimal filter bank based on incoming ECG measurements. The algorithm also provides critical heart-related information such as heart rate, QT, and PR intervals from the ECG signal.