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  • Re: Summary 'filtering ECG from EMG'

    Hi all,
    As usual a fantastic response. Thanks to all who replied. You have given me
    some great ideas.
    Wendy.

    Original Message:

    I have collected some abdominal EMG which for the electrodes in the upper
    abdominal area have significant cardiac ECG signals in it. This ECG signal
    is interfering greatly with the ability to determine onsets etc. Therefore
    we would like to get rid of it. I realise that filtering will remove all the
    signal, and hence some EMG, at whatever frequency we use. However at the
    moment we cannot determine an onset at all that makes sense.
    My question is:
    Has anybody tried this?
    What filter and frequency did you use?
    Have you published this anywhere?

    Summary of replies:


    From: "Rod Barrett NHS"
    Organization: Griffith University Gold Coast

    Redfern, MS. (1987). Elimination of EKG contamination of torso
    electromyographic signals. In Asfour, SS (Ed) Trend in
    Ergonomics/Human Factors IV (pp. 725-729). Amsterdam: Elsevier
    Science Publishers BV.
    ++++++++++++++++++++++++++++++++=

    From: burden
    Adrian Burden
    Chelsea School
    University of Brighton

    and
    From: ErgoHughes@aol.com
    Richard Hughes

    Redfern, M.S., Hughes, R.E. and Chaffin, D.B. (1993). High -pass filtering
    to remove electrocardiographic interference from torso EMG recordings.
    Clinical Biomechanics, 8:44-48.
    +++++++++++++++++++++++++++++++++++++++

    From: Rolf Moe-Nilssen
    Organisation: University of Bergen

    In stead of filtering in the frequency domain, you could try the easy
    solution of making a histogram of the distribution of EMG amplitudes.
    Most likely you will get a bimodal distribution, where the upper mode
    represents the signals containing ECG artifact. If the dip between the
    two modes is sufficiently deep (which it most likely is if the EMG
    activity is low), you can make this a cut off value for the amplitudes
    you would like to include. Thus you dispose of all samples containing
    ECG without distorting the rest. At least you get rid of the QRS-complex
    of the ECG signal this way. It's like going between heartbeats to do
    your analysis. If you need exact onset times you should retain the
    removed samples as missing values. I did this on resting EMG from the
    upper (left) trapezius with good results. Unfortunately I only have the
    results written in Norwegian, but if you want to have a look, I could
    easily fax you some relevant figures showing the EMG signals with and
    without corrections for ECG artifact.
    +++++++++++++++++++++++++++++++++++++++

    From: lhsome@luga.latrobe.edu.au
    Owen Evans, La Trobe University

    This is a characteristic problem of emgs recorded from the trunk. It
    differs according to site and individual. The only satisfactory method of
    reducing it that I have used is direct recording of the EKG, and digital or
    analog subtraction. But that's a long time ago, and may seem crude in this
    day and age.
    ++++++++++++++++++++++++++++++++++++===


    From: (Karl Kraus)
    Organisation: Tufts University

    You are getting far field from the heart, obviously. Usually this means you
    are doing surface recording. Analog filtering is not the answer. You need to
    position your active, reference and ground such that the heart signal gets to
    your active and reference electrodes at the same time, usually this means
    close together. Ground closer to the heart. If you can, use bipolar EMG
    needles. This is painful, however. If this is impossible, try digital
    filtering and FFT.
    ++++++++++++++++++++++++++++++++++++

    From: l.abraham@mail.utexas.edu (Larry Abraham)
    Organisation: The University of Texas at Austin

    I don't think normal filtering techniques will do what you want. I suspect
    you will get much better results by "subtracting" the ECG waveform, since
    it is fairly discreet and regular. There are commercial software packages
    which do this, but they are a bit pricey. You can probably write your own
    code to do this particular job. The general idea is as follows:

    First, using triggered averaging, collect an average waveform of the ECG.
    You can do this by aligning segments of the data which contain the
    waveform, using the most salient feature (largest peak), and averaging.
    This should remove the "random" abdominal EMG and leave you with a
    representative waveform of the offending ECG.

    Then, go back through the data and "subtract" the composite waveform from
    the raw data at each heartbeat. What is left should be a relatively pure
    EMG signal.
    ++++++++++++++++++++++++++++++++++

    From: Richard Shiavi
    Organisation: Vanderbilt University

    Without having done this my best guess is that you will have to do something
    similar to what folks have done separating the maternal and fetal ECG
    signals. First detect the ECG QRS complex and then subtract it from the
    original measurement. After that some high pass filtering with cutoff
    frequency around 20 Hz should remove most of the energy in the P and T waves
    and retain most of the EMG signal.

    The ECG detection is well documented and i could send you some MATLAB script
    if you decided to use this approach.
    +++++++++++++++++++++++++++++++++++++==


    From: atyler@saunix.sau.edu (Amy E. Tyler)
    Organisation: St. Ambrose University

    I had similar problems when collecting dissertation data a few years back.
    I ended up recording abdominal muscle activity in the lower abdominal area
    (further away from the heart) and orienting the bipolar electrodes so that
    they were aligned perpendicular to the direction of the heart (in an
    attempt to have the electrodes "subtract" the ECG).

    I'm now using a different emg setup with a cut off frequency of either 20
    or 70 Hz. Using the 70Hz cut off helps a lot, but doesn't completely
    eliminate the problem, which is unfortunate because onsets/offsets are then
    impossible to confidently identify as you've indicated.

    It sounds as if you already have the data and you are trying to filter out
    the ECG after data collection. Is there some way of estimating the
    magnitude and frequency of the ECG and somehow subtracting that from your
    data? I'd be interested in any responses you get about such ideas as I
    suspect I will continue to struggle with ECG artifact as you have.
    +++++++++++++++++++++++++++++++++++++++


    From: Peter Meyer
    Organisation: NeuroMuscular Research Center, Boston University

    This is just off the top of my head with very little thought involved,
    but what if you used a differential amplifier to subtract out the ECG?
    If you placed the ECG electrodes in an orientation such that the EMG and
    ECG electrodes pick up similar projections of the cardiac vector, you
    could run the outputs through a diff amp. It wouldn't be perfect, since
    you can't get the exact same cardiac vector projection at a different
    spot, but it might be better that filtering.

    Good luck. You have an interesting problem.
    +++++++++++++++++++++++++++++++++++++=

    From: "Paul Guy"
    Organization: University of Waterloo

    Yep, we've done filtering and found it highly successful at
    removing ECG artifacts from EMG. The part you won't like is how we
    did it. In effect, we use a 'brick wall' high pass filter, whose
    corner frequency was at 30-40 Hz.
    To achieve that type of filter I used a Fourier transform, that
    gave BOTH real and imaginary terms, zero'd out the frequencies I
    didn't want, and then did an inverse Fourier Transform. Since there
    is a one-to-one transformation between time and frequency domain
    using FFT's, this is a valid approach.
    In practice, I used Microsoft Excel to do it. I brought the data
    into a spreadsheet, and used macros I have developed to perform the
    FFT and inverse FFT. Those macros are publicly available from our FTP
    site - gailab1.uwaterloo.ca in directory: /pub/foryou/excelstuff as
    pmacros.xls . If you want them, do it fast as the powers that be want
    that Unix machine trashed to make more room for their junk.
    For the same reason, if you want to get in touch with me, don't
    use gaitlab1.uwaterloo.ca, use pguy@math.uwaterloo.ca . You can post
    this reply to the newsgroup if you want, as I have only replied to
    you personally.
    I haven't published this, I don't believe this would be worthy of
    a quality article, as it is just a mere technique to get the job done.
    ++++++++++++++++++++++++++++++++======

    From: Joel L Lanovaz

    University of Saskatchewan

    I don't deal with ECG or EMG signals specifically, but I have worked with
    a data analysis technique which may be of some interest to you. Have you
    tried filtering your data using Wavelets? Wavelet smoothing (often called
    "de-noising") has the unique ability to eliminate noise at a given
    frequency level while retaining real signal components at the same
    frequency. If your real EMG signal has a great enough signal power
    with respect to the interference signal, wavelet smoothing might work
    quite well.

    I have only used "home grown" wavelet software, so I can't give you names
    of any software packages. A quick search on the web should show quite a
    few free packages (usually in MATLAB or C). As far as references go, I
    don't have my full list handy, but one you might start with is:

    Wavelets: Theory, algorithms and applications (1994), Edited by Chui,
    Academic Press, San Diego.
    +++++++++++++++++++++++++++++++++++++++


    Wendy Gilleard E-mail: w.gilleard@cchs.usyd.edu.au
    Dept. Biomedical Science Tel: Int +61- (0)2 - 935 19528
    Faculty of Health Science Fax: Int +61- (0)2 - 935 19520
    University of Sydney
    Post: P.O. Box 170, Lidcombe, N.S.W. 2141, Australia
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