Abstract: Apparatus and methods for analyzing implantable medical device (IMD) data and to distinguish specific morphologies for review. EGM waveforms of a cardiac cycle can be grouped according to waveform shape similarities, preferably accomplished using clustering algorithms. The EGM waveform can be divided into segments represented by a one dimensional vector for each segment, and nearest neighbor vector clustering used to group the waveforms according to morphological similarity. The waveforms can be further grouped at a second level into chronologically contiguous similar EGM waveforms. Interval data for a cardiac cycle may also be grouped according to interval similarity and chronology. The chronologically contiguous waveform and interval groupings may be further grouped. Large quantities of EGM data are automatically analyzed and annotated to focus the attention of a cardiologist on likely areas of concern, reducing the need to visually inspect endless streams of EGM waveforms.

Abstract: Apparatus and methods for analyzing implantable medical device (IMD) data and to distinguish specific morphologies for review. EGM waveforms of a cardiac cycle can be grouped according to waveform shape similarities, preferably accomplished using clustering algorithms. The EGM waveform can be divided into segments represented by a one dimensional vector for each segment, and nearest neighbor vector clustering used to group the waveforms according to morphological similarity. The waveforms can be further grouped at a second level into chronologically contiguous similar EGM waveforms. Interval data for a cardiac cycle may also be grouped according to interval similarity and chronology. The chronologically contiguous waveform and interval groupings may be further grouped. Large quantities of EGM data are automatically analyzed and annotated to focus the attention of a cardiologist on likely areas of concern, reducing the need to visually inspect endless streams of EGM waveforms.