Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!iuvax!rutgers!okstate!ramkuma
From: ramkuma@a.cs.okstate.edu (Ramkumar P Rangana)
Newsgroups: comp.ai.neural-nets
Subject: Re: NOISE FILTERING USING NEURAL NETWORKS
Summary: REPLY: Noise Filtering Using NN.
Keywords: NOISE FILTERING NEURAL-NETWORKS EKG SIGNALS
Message-ID: <4546@okstate.UUCP>
Date: 31 Mar 89 01:55:48 GMT
References: <4537@okstate.UUCP>
Organization: Oklahoma State Univ., Stillwater
Lines: 88

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.)
> 

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  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).
 

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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

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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


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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

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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


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  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
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