Automated seismic detection of myocardial ischemia and related

Abstract

A computer-based instrument to produce a 'number' for heart performance parameters and a positive-negative diagnosis of myocardial ischemia. A seismic sensor captures a substantial series of SCG waveforms within a short time frame. Digitized waveforms are created and processed to create signals in the range of 0 to 50 hertz and 0 to 100 hertz. The waveform are processed in the time domain. The 0 to 100 hertz signal is processed to determine the heart rate which is pulse adjusted and interpolated. The SCG waveforms are processed to synchronize the start point of each waveform. The 0 to 50 hertz signal is then processed for signal segmentation to produce waveform signals, each a heart beat or period in length. The segmented signals are then processed to produce linear prediction analysis (LPA) coefficients. The coefficients establish a numerical model-based representation of the waveform. The LPA coefficients in combination contain all of the information resident in the original SCG waveform. For myocardial ischemia analysis, proper LPA coefficients are used in a pattern recognition algorithm to determine a classification of the patent's waveforms as either normal or ischemic. The Bayesian decision classifier provides an analytical framework and program for classification of SCG waveforms as represented by the LPA coefficients for myocardial ischemia, or other cardiac disease conditions represented in the SCG waveform, and produces a direct negative or positive output. For various cardiac performance parameters, estimation rather than a classification algorithm is used such as a K-Nearest Neighbor pattern recognition technology, and multiple regression estimators and produces estimation for different parameters.