CALVIN: A Rule Based Expert System for Improving Arrhymia Detector Performance During Noisy ECGS
Name
MIT-LCS-TR-406.pdf
Size
2.56 MB
Format
Adobe PDF
Checksum (MD5)
49551ee97b05692333e64c5912eb2b87
Author(s)
Muldrow, Warren K.
Date Issued
September 1987
Series/Report no.
MIT-LCS-TR-406
Abstract
Human experts far outperform automated arrhythmia detectors in analyzing ECG data corrupted by noise and artifact. Humans make use of considerable a priori knowledge about cardiac electrophysiology and knowledge acquired from the specific ECG under analysis. R-R interval, coupling intervals of ectopic beats, and commonly occurring beat patterns observed during noise-free ECG segments form a knowledge base which is used in accurately detecting and classifying true QRS complexes in the presence of severe noise.
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