ramkuma@a.cs.okstate.edu (Ramkumar P Rangana) (03/29/89)
Do anyone have some reference articles on NOISE FILTERING in EKG SIGNALS ( OR any signals) using NEURAL-NETWORK techniques? (Any application of Neural Nets to NOISE FILTERING.) Any information in this area is Welcome. Thanks in advance. (ramkuma@a.cs.okstate.edu)
mathew@jane.Jpl.Nasa.Gov (Mathew Yeates) (03/30/89)
In article <4537@okstate.UUCP> ramkuma@a.cs.okstate.edu (Ramkumar P Rangana) writes: >Do anyone have some reference articles on NOISE FILTERING >in EKG SIGNALS ( OR any signals) using NEURAL-NETWORK techniques? >(Any application of Neural Nets to NOISE FILTERING.) > >Any information in this area is Welcome. > >Thanks in advance. > >(ramkuma@a.cs.okstate.edu) I've developed a neural implementation of the sequential regression algorithm found in Widrows "Adaptive Signal Signal Processing". (its a pseudo-Newton algorithm). As it turns out, the net can also do Kalman filtering by allowing a local interdependence between synapses. Precisely, a new weight is calculated as a function of its old weight and the weight of a single neighbor. It is biologicaly intuitive that synapse changes should not occurr independently and adding this feature to my model allows for more powerful processing. If you want a copy of my "An Architecture With Neural Network Characteristics for Least Squares Problems" e-mail your request to mathew@jane.jpl.nasa.gov.
ramkuma@a.cs.okstate.edu (Ramkumar P Rangana) (03/31/89)
In article <4537@okstate.UUCP>, ramkuma@a.cs.okstate.edu (Ramkumar P Rangana) writes: > Do anyone have some reference articles on NOISE FILTERING > in EKG SIGNALS ( OR any signals) using NEURAL-NETWORK techniques? > (Any application of Neural Nets to NOISE FILTERING.) > ------------------------------------------------------------------- I am posting the replies I received from Daniel Pirone, Matt Kernel, Harry Lagenbacher, Chester and John Platt. Thanks to all of You. RAMKUMAR (ramkuma@a.cs.okstate.edu). --------------------------------------------------------------------- Just a few simple noise filters with NN's I have seen in passing, Dr. Dobb's Journal of Software Tools, #147 January 1989 p32. ( has a c code listings in back ) There are some commercial packages available, I don't seem to have any adds arround, but there is always at least one in the Neural Networks journal... Daniel Pirone ----------------------------------------------------------------------- Doyne Farmer at Los Alamos (jdf%heretic@lanl.gov) has done some work with noise reduction in dynamical systems. He has a very good preprint that talks about predicting dynamical systems with various algorithms (he doesn't use a neural net). I'm doing a thesis on prediction with neural networks, and (if I have time) will try to apply it to noise reduction. The basic idea is to make predictions a few steps in advance (using previous data) so that for some time you have more than one "guess" to what the point actually is, and you can "average" them. It turns out that reducing noise is _easier_ in a dissipative system than one would think because noise in one coordinate can be surpressed evolving in time. Matt Kennel mbkennel@phoenix.princeton.edu ---------------------------------------------------------------------- Bernard Widrow has been writing on adaptive filtering for years & years. -- my Opinions aren't anyone else's, and they probably shouldn't be - Harry Langenbacher harry%neuron1@jpl-mil.jpl.nasa.gov ----------------------------------------------------------------------- In the January, 1989, issue of Dr. Dobb's Journal of Software Tools, you will find an article titled "Neural Nets and Noise filtering" by Casimir C. Klimasauskas, president and founder of NeuralWare. In this article he uses a back-propagating network to filter EKG data. The key idea is the network has N input units (N is odd), N output units, and FEWER THAN N hidden units. N successive signal values are used both as input pattern and target pattern, so the network is being trained to reproduce the input, but because there are less than N hidden units, they are forced to do some smoothing. Once the network is trained, successive signal values are applied at the N inputs and the value of the center output unit is taken as the output of the filter. The signal values are shifted over one unit, the next signal value applied to the input unit that is now free, and the value of the center output unit is read again, etc. The only references cited in the article are Rummelhart and McClelland, "Parallel Distributed Processing," Vol. 1, and Widrow and Stearns, "Adaptive Signal Processing," Prentice-Hall, 1985. Both are books. chester@louie.udel.edu --------------------------------------------------------------------------- I wrote a paper that appeared in the 1986 Snowbird Conference on Neural Networks (American Institute of Physics Conference Proceedings 151), entitled "Analog Decoding using Neural Networks" by J.C. Platt and J.J. Hopfield... I hope this helps... I also talk about error-correcting codes in my upcoming thesis, "Constraint Methods for Neural Networks and Computer Graphics" John Platt platt@csvax.caltech.edu ---------------------------------------------------------------------------