TABLE OF CONTENTS Franc Jager
In this chapter we describe general signal processing techniques that robustly generate diagnostic and morphologic feature-vector time series of ECG. These techniques allow efficient, accurate, and robust extraction, representation, monitoring, examination, and characterization of ECG diagnostic and morphologic features. In particular, an emphasis is made on the efficient and accurate automated analysis of transient ST segment changes. Traditional time-domain approaches and an orthonormal function model approach using principal components are explored.
Figure 9.1 shows typical ECG data from an ambulatory ECG (AECG) record. A transient ST segment episode compatible with ischemia (ischemic ST episode) begins in the second part of the third data segment shown. Two abnormal beats can be observed in the final strip. In the field of arrhythmia detection, we are mostly interested in the global beat morphology (i.e., normal or abnormal morphology). However, in the field of ST segment change analysis, wave measurements and robust construction of ECG diagnostic and morphologic feature time series are of direct interest. Due to enormous amount of data in long-term AECG records, standard visual analysis of raw ECG waveforms does not readily permit assessment of the features that allow one to detect and classify QRS complexes, to analyze many types of transient ECG events, to distinguish ischemic from nonischemic ST changes, nor possibly to distinguish among ischemic and heart rate related ST change episodes. Questions concerning representation, characterization, monitoring, automatic analysis of ECG waveforms, and detection and differentiation of different types of transient...
