-transparent.png){kind=logo}
The computerized interpretation of the resting electrocardiogram has reached a steady-state phase: an equilibrium between sensitivity and specificity has been reached. New computer techniques, such as expert systems and artificial neural network technology, have been proposed or are currently under evaluation. Although neural network techniques are based on complex mathematical theories and their application is full of pitfalls, progress has been made in a number of subdomains, like signal filtering, electrocardiographic classification, and compression of stress electrocardiograms. Presently, the hesitating acceptance by the human user forms one of the obstacles that needs to be overcome by convincing, well-performed studies.
W R Dassen, and
R G Mulleneers, and
K den Dulk, and
J L Talmon
September 1996,
Nuclear medicine communications,
W R Dassen, and
R G Mulleneers, and
K den Dulk, and
J L Talmon
June 1998,
American journal of epidemiology,
W R Dassen, and
R G Mulleneers, and
K den Dulk, and
J L Talmon
September 2005,
Clinical physiology and functional imaging,
W R Dassen, and
R G Mulleneers, and
K den Dulk, and
J L Talmon
January 2013,
Computational and mathematical methods in medicine,
W R Dassen, and
R G Mulleneers, and
K den Dulk, and
J L Talmon
January 1993,
Journal of electrocardiology,
W R Dassen, and
R G Mulleneers, and
K den Dulk, and
J L Talmon
August 2021,
Sensors (Basel, Switzerland),
W R Dassen, and
R G Mulleneers, and
K den Dulk, and
J L Talmon
January 2010,
Urologia internationalis,
W R Dassen, and
R G Mulleneers, and
K den Dulk, and
J L Talmon
January 2010,
Urologia internationalis,
W R Dassen, and
R G Mulleneers, and
K den Dulk, and
J L Talmon
January 1998,
Orvosi hetilap,
W R Dassen, and
R G Mulleneers, and
K den Dulk, and
J L Talmon
October 2004,
Seminars in ultrasound, CT, and MR,
