Detection of Abnormalities in the Signal Averaged Electrocardiogram: A Subspace System Identification Approach

This paper addresses the detection and classification of low amplitude signals within the QRS complex of the signal-averaged electrocardiogram. The raw data is used to fit a state-space model using the N4SID algorithm and the residual from the model are then used for detection. The fundamental assumption behind the state-space model is that the residuals are a white noise process. Therefore, anything that cannot be modeled with the state-space model will show up in the residuals as flow amplitude signal+noise. Compared to typical residuals, the low amplitude signal behaves as influential observations and can be treated as outliers. Diagnostic tests and analysis on the residuals will then lead to detection and classification of abnormalities in the intra-QRS complex. Residual analysis in this paper includes whiteness and Gaussian tests, statistical process control, and the use of a tracking signal. The end result is a tool to aid the physician in diagnosing the heart condition of a patient.