Neural networks in cardiac electrophysiological signal classification

The aim of this work was to develop a method by which intra-cardiac electrograms could be classified. A new algorithm for training this particular network has been established and applied to the task of finding the onset times of intra-cardiac electrograms. The algorithm is based on adding a choice function to the combination function of each neuron. The choice function enables the network to consider delays in each of its synapses. The gradient of error is then calculated with respect to the weights and delays. A synaptic delay-based artificial neural network was implemented using MATLAB and used to detect the onset times of the atrial, His and ventricular electrograms from the His catheter recordings. Results from a subset of a clinical, 12-channel electrophysiology study demonstrated the ability of the network to successfully identify peak potentials and onset times. Errors in detection of onset times were in the range of 1–2 ms. This method, which does not utilise traditional windowing and/or thresholding operations, can be effectively used to detect temporal patterns in a range of electrophysiological and biological signals.

[1]  Carlo Marchesi,et al.  Autoassociator structured neural network for rhythm classification of long term electrocardiogram , 1993, Proceedings of Computers in Cardiology Conference.

[2]  N Mahalingam,et al.  Neural networks for signal processing applications: ECG classification. , 1997, Australasian physical & engineering sciences in medicine.

[3]  P. de Chazal,et al.  Improving ECG diagnostic classification by combining multiple neural networks , 1997, Computers in Cardiology 1997.

[4]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[5]  Georg Dorffner,et al.  On using feedforward neural networks for clinical diagnostic tasks , 1994, Artif. Intell. Medicine.

[6]  P. G. J. Lisboa.  Neural Networks: Current Applications , 1992 .

[7]  Javier R. Movellan,et al.  Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks , 1991, IEEE Trans. Syst. Man Cybern..

[8]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[9]  W. Baxt,et al.  Prospective validation of artificial neural network trained to identify acute myocardial infarction , 1996, The Lancet.

[10]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[11]  Judith E. Dayhoff,et al.  Neural Network Architectures: An Introduction , 1989 .

[12]  Michael R. Davenport,et al.  Continuous-time temporal back-propagation with adaptable time delays , 1993, IEEE Trans. Neural Networks.

[13]  Omid Omidvar,et al.  Neural Networks and Pattern Recognition , 1997 .

[14]  H. K. Verma,et al.  ANN-based QRS-complex analysis of ECG. , 1998, Journal of medical engineering & technology.

[15]  Jerry M. Mendel.  A prelude to neural networks: adaptive and learning systems , 1994 .

[16]  Richard J. Duro,et al.  Temporal Dimension in Signal Processing , 1997, EUROCAST.

[17]  Erol Gelenbe,et al.  Neural Networks: Advances and Applications II , 1991 .

[18]  John J. Soraghan,et al.  An automatic neural-network based SVT/VT classification system , 1993, Proceedings of Computers in Cardiology Conference.

[19]  D. Mortara,et al.  Body Surface Detection of Delayed Depolarizations in Patients with Recurrent Ventricular Tachycardia and Left Ventricular Aneurysm , 1981, Circulation.

[20]  Jin-Shiuh Taur,et al.  Prediction-based networks with ECG application , 1993, IEEE International Conference on Neural Networks.

[21]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[22]  J.C. Principe,et al.  Neural networks for EEG signal decomposition and classification , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[23]  Y Attikiouzel,et al.  Applications of neural networks in medicine. , 1995, Australasian physical & engineering sciences in medicine.

[24]  Gerald Sommer,et al.  Pattern Recognition by Self-Organizing Neural Networks , 1994 .

[25]  Lawrence A. Rowe,et al.  A divide-and-conquer algorithm for the automatic layout of large directed graphs , 1991, IEEE Trans. Syst. Man Cybern..

[26]  W.J. Tompkins,et al.  Neural-network-based adaptive matched filtering for QRS detection , 1992, IEEE Transactions on Biomedical Engineering.

[27]  H. Nakajima,et al.  Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network , 1999, IEEE Transactions on Biomedical Engineering.

[28]  Alessandro Sperduti,et al.  Speed up learning and network optimization with extended back propagation , 1993, Neural Networks.

[29]  Eugene M. Strand,et al.  A neural network for tracking the prevailing heart rate of the electrocardiogram , 1990, [1990] Proceedings. Third Annual IEEE Symposium on Computer-Based Medical Systems.

[30]  Clifford Lau,et al.  Neural Networks: Theoretical Foundations and Analysis , 1991 .

[31]  P W Macfarlane,et al.  Methodology of ECG Interpretation in the Glasgow Program , 1990, Methods of Information in Medicine.

[32]  A. Guyton,et al.  Human Physiology and Mechanisms of Disease , 1982 .

[33]  Richard J. Duro,et al.  Discrete-time backpropagation for training synaptic delay-based artificial neural networks , 1999, IEEE Trans. Neural Networks.

[34]  Peter W. Macfarlane,et al.  Artificial neural networks for the diagnosis of atrial fibrillation , 1993 .

[35]  Alan F. Murray,et al.  IEEE International Conference on Neural Networks , 1997 .

[36]  H. T. Nagle,et al.  A comparison of the noise sensitivity of nine QRS detection algorithms , 1990, IEEE Transactions on Biomedical Engineering.

[37]  Andrea Schenone,et al.  Neural networks in biomedicine : proceedings of the Advanced School of the Italian Biomedical Physics Association, Como, Italy, 15-19 November 1993 , 1994 .

[38]  Carlos V. Regueiro,et al.  Classifying multichannel ECG patterns with an adaptive neural network. , 1998, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[39]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[40]  Alan Gevins,et al.  High resolution EEG , 2005, Brain Topography.

[41]  David E. Rumelhart,et al.  Product Units: A Computationally Powerful and Biologically Plausible Extension to Backpropagation Networks , 1989, Neural Computation.

[42]  Richard J. Duro,et al.  Synaptic delay based artificial neural networks and discrete time backpropagation applied to QRS complex detection , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[43]  W. T. Miller,et al.  CMAC: an associative neural network alternative to backpropagation , 1990, Proc. IEEE.

[44]  Qiuzhen Xue,et al.  Late potential recognition by artificial neural networks , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[45]  Sun-Yuan Kung,et al.  Digital neural networks , 1993, Prentice Hall Information and System Sciences Series.