Title :
Automatic neural detection of anomalies in electrocardiogram (ECG) signals
Author_Institution :
NEURON Res., Morges, Switzerland
fDate :
27 Jun- 2 Jul 1994
Abstract :
A two-stage architecture is proposed for recognition of five types of ill complexes in ECG signals. First, a classical signal processing method allows detection of relevant portions of the signal (QRS complexes), and reduction of the information needed for classification. Second, a neural network architecture is used for classification, implying a Kohonen map and a perceptron, with the cardiologist as a supervisor. Two types of troubles are perfectly recognized, while the three others remain hard to detect as such
Keywords :
electrocardiography; medical diagnostic computing; medical signal processing; patient diagnosis; pattern classification; perceptrons; self-organising feature maps; ECG signal processing; Kohonen map; QRS complexes; automatic neural detection; electrocardiogram; neural network; pattern classification; perceptron; Cardiac disease; Cardiology; Electrocardiography; Frequency; Heart beat; Morphology; Neural networks; Neurons; Signal processing; Supervised learning;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374907
