A class of algorithms has been developed which detects QRS complexes in the electrocardiogram (ECG). The algorithms employ nonlinear transforms derived from multiplication of backward differences (MOBD). The algorithms are evaluated with the American Heart Association ECG database, and comparisons are made with the algorithms reported by Okada and by Hamilton and Tompkins. The MOBD algorithms provide a good performance tradeoff between accuracy and response time, making this type of algorithm desirable for real-time microprocessor-based implementation.

This study presents a novel approach to modeling the electrocardiogram (ECG): the Gaussian pulse decomposition. Constituent waves of the ECG are decomposed into and represented by Gaussian pulses using an iterative algorithm: the chip away decomposition (ChAD) algorithm. At each iteration, a nonlinear minimization method is used to fit a portion of the ECG waveform with a single Gaussian pulse, which is then subtracted from the ECG waveform. The process iterates on the resulting residual waveform until the normalized mean square error is below an acceptable level. Three different minimization methods were compared for their applicability to the ChAD algorithm; the Nelder-Mead simplex method was found to be more noise-tolerant than the Newton-Raphson method or the steepest descent method. Using morphologically different ECG waveforms from the MIT-BIH arrhythmia database, it was demonstrated that the ChAD algorithm is capable of modeling not only normal beats, but also abnormal beats, including those exhibiting a depressed ST segment, bundle branch block, and premature ventricular contraction. An analytical expression for the spectral contributions of the constituent waves was also derived to characterize the ECG waveform in the frequency domain. The Gaussian pulse model, providing an intuitive representation of the ECG constituent waves by use of a small set of meaningful parameters, should be useful for various purposes of ECG signal processing, including signal representation and pattern recognition.

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We propose methodologies to automatically classify time-varying warning signals from an acoustic monitoring system that indicate the potential catastrophic structural failures of reinforced concrete structures. Since missing even a single warning signal may prove costly, it is imperative to develop a classifier with high probability of correctly classifying the warning signals. Due to the time-varying nature of these signals, various time-frequency classifiers are considered. We propose a new time-frequency decomposition-based classifier using the modified matching pursuit algorithm for an actual acoustic monitoring system. We investigate the superior performance of the classifier and compare it with existing classifiers for various sets of acoustic emissions, including warning signals from real-world faulty structures. Furthermore, we study the performance of the new classifier under different test conditions.

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