[Improved learning capacity and discrimination performance of neural networks in pattern recognition of biosignals].
Pattern recognition was an important goal in the early work on artificial neural networks. Without arousing dramatic speculation, the paper describes, how a "natural" method of dealing with the configuration of the input layer can considerably improve learning behaviour and classification rate of a modified multi-layered perception with backpropagation of the error learning rule. Using this method, recognition of complex patterns in electrophysiological signals can be performed more accurately, without rules or complicated heuristic procedures. The proposed technique is demonstrated using recognition of the J-point in the ECG as an example.
Herrmann
L
L
Bundesanstalt für Arbeitsmedizin, Berlin.
Rienäcker
U
U
D004562:Electrocardiography / Q000295:instrumentation; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*
Verbesserung von Lernverhalten und Diskriminationsleistung neuronaler Netze bei der Mustererkennung in Biosignalen.