Although long term (24-Hour) electrocardiographic monitoring of ambulatory patients has become an integral part of medical diagnosis, analysis of recorded waveforms has advanced little since introduction of the screening procedure by Holter in 1957. Research into computerized systems using traditional pattern recognition techniques and interval analysis has not yielded a clinically reliable automated method for Holter analysis. This laboratory has employed syntactical pattern recognition in conjunction with an expert system to successfully analyze most ventricular arrhythmias. However, reliable computer detection of the P wave has limited the syntactic approach in analyzing atrial arrhythmias. P Wave detection is difficult because it is low in amplitude and easily obscured by artifact common in ambulatory records. Recently, we have tested a technique called """"""""hidden Markov modeling"""""""" (HMM), used quite successfully in computerized identification of human speech. These preliminary tests have shown it to be surprisingly effective in computer identification of low amplitude waves of the ECG (such as the P wave) even in the presence of sufficient noise to visually obscure them. Our intent is to investigate this technique in the computerized analysis of arrhythmias in Holter records. Our eventual goal is development of a fully computerized system for arrhythmia analysis using both HMM, should our investigation verify applicability.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL040525-03
Application #
3357744
Study Section
Cardiovascular and Pulmonary Research A Study Section (CVA)
Project Start
1988-04-01
Project End
1991-03-31
Budget Start
1990-04-01
Budget End
1991-03-31
Support Year
3
Fiscal Year
1990
Total Cost
Indirect Cost
Name
Allegheny-Singer Research Institute
Department
Type
DUNS #
033098401
City
Pittsburgh
State
PA
Country
United States
Zip Code
15212
Coast, D A; Stern, R M; Cano, G G et al. (1990) An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Trans Biomed Eng 37:826-36