paper available on patient-adaptive ECG classification

[Raymond L Watrous]

	FTP-HOST: scr.siemens.com
	FTP-filename: /pub/learning/Papers/watrous/cic_95.ps.Z

The following paper (4 pages, 3 figures) is now available via
anonymous ftp:

  A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm

	      Raymond Watrous, Geoffrey Towell

		 Siemens Corporate Research
		 755 College Road East
		 Princeton, NJ 08540

			Abstract

A new, patient-adaptive ECG Patient Monitoring algorithm is
described. The algorithm combines a patient-independent neural network
classifier with a three-parameter patient model.  The patient model is
used to modulate the patient-independent classifier via multiplicative
connections. Adaptation is carried out by gradient descent in the
patient model parameter space.

The patient-adaptive classifier was compared with a well-established
baseline algorithm on six major databases, consisting of over 3
million heartbeats. When trained on an initial 77 records and tested
on an additional 382 records, the patient-adaptive algorithm was found
to reduce the number of Vn errors on one channel by a factor of 5, and
the number of Nv errors by a factor of 10. We conclude that patient
adaptation provides a significant advance in classifying normal
vs. ventricular beats for ECG Patient Monitoring.

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The paper will appear in the proceedings of Computers in Cardiology,
September 10-13, 1995, Vienna, Austria.

We regret that we are unable to provide hard copies.

Raymond Watrous

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Learning Systems Department		Phone: (609) 734-6596
Siemens Corporate Research		FAX:   (609) 734-6565
755 College Road East
Princeton, NJ 08540

watrous at learning.scr.siemens.com