I’ve done a lot of random things the last few months, but few things were as random, cool, or googled-for as my Do-It-Yourself Electrocardiography project . My goal was to produce an effective ECG machine which interfaced the computer sound card for as little cost as possible. I started out small with an extremely simple circuit which technically worked, but required a lot of custom-written software to do a ton of math to decipher the ECG signal from the noise (such as inverse fast flourier transformations after band-stopping several bands of predictable, high-frequency noise). I later started building more complicated circuits in an attempt to minimize the noise, which worked well but were much more difficult to construct. For some reason, my nice ECG circuit died (burned? broke? don’t know why) right after I started to actually generate useful data about my occasional double-beats (which apparently are common, normal, and even expected during basal physiological states).
UPDATE: [2am, nextday] Here’s some video of the prototype briefly demonstrating the concept of how to use a minimum of parts to generate a great ECG trace using digital signal processing on the PC side.

I’ve decided to revitalize this project quickly and effectively, going back to its roots and focusing on cost-minimal solutions, and using software (rather than complicated analog circuitry) to eliminate the noise. This will be a beautiful marriage of biomedical analog circuitry with software-based processing and linear data analysis, all on the cheap. If there were ever a project that represented my early 20s life, this would be it. Briefly, I built a circuit with only 3 components (!) which produces extraordinary results (above). That’s the signal after minimal processing.

Check it out yourself! I’ll provide data file for this trace (snd2.zip) along with the Python code to graph it (below) which requires numpy and matplotlib in addition to the Python scripting language. I’ll post the circuity along with some more intricate code when my project progresses a little further.

import numpy, pylab

def trim(data,degree=100):
    i,data2=0,[]
    while i&ltlen(data):
        data2.append(sum(data[i:i+degree])/degree)
        i+=degree
    return data2

def genXs(length,trim=100,hz=44100):
    step = 1.0/(hz/trim)
    xs=[]
    for i in range(length):
        xs.append(step*i)
    return xs

data = numpy.memmap("ecg2.snd", dtype='h', mode='r')
data = trim(data)
pylab.grid(alpha=.2)
pylab.plot(genXs(len(data)),data)
pylab.title("Simplified ECG Circuit Output")
pylab.xlabel("Time (seconds)")
pylab.ylabel("Potential (Au)")
pylab.show()