I only have today and tomorrow left! I begin dental school [shrieks] on Monday, and with the summer nearing its end I’m trying to wrap-up a bunch of project loose ends. Most notably, I still have to complete my microcontroller-powered prime number generator. It’s 99% complete, but if I don’t finish it now I never well! There’s also the halfway finished SQL HTTP hit logger which is fully implemented, but lacks graphing capabilities. I have two scientific manuscripts I’m trying to complete and have ready for publication soon as well. I did, however, manage to complete my DIY ECG project! It was even featured on HackADay.com – score! Just think, this summer I graduated from UCF’s School of Biomedical Science with my Master’s in molecular biology, got accepted to UF’s College of Dentistry, submitted manuscripts to the Journal of Comparative Neurology, got featured on HackADay, and got my amateur radio license, and even upgraded to general class! Oh yeah, and there’s that oscilloscope I landed for $5. Yes, this summer is one to be proud of. [smiles softly]
UPDATE: An improved ECG design was posted in August, 2016.
Check out: http://www.swharden.com/wp/2016-08-08-diy-ecg-with-1-op-amp/
Note from the Author: This page documents how I made an incredibly simple ECG machine with a minimum of parts to view the electrical activity of my own heart. Feel free to repeat my experiment, but do so at your own risk. There are similar projects floating around on the internet, but I aim to provide a more complete, well-documented, and cheaper solution, with emphasis on ECG processing and analysis, rather than just visualization. If you have any questions or suggestions please contact me. Also, if you attempt this project yourself I’d love to post your results! Good luck!
You’ve probably seen somebody in a hospital setting hooked up to a big mess of wires used to analyze their heartbeat. The goal of such a machine (called an electrocardiograph, or ECG) is to amplify, measure, and record the natural electrical potential created by the heart. Note that cardiac electrical signals are different than heart sounds, which are listened to with a stethoscope. The intrinsic cardiac pacemaker system is responsible for generating these electrical signals which serve to command and coordinate contraction of the four chambers at the heart at the appropriate intervals [atria (upper chambers) first, then the ventricles (lower chambers) a fraction of a second later], and their analysis reveals a wealth of information about cardiac regulation, as well insights into pathological conditions. Each heartbeat produces a similar pattern in the ECG signal, called a PQRST wave. [picture] The smooth curve in the ECG (P) is caused by the stimulation of the atria via the Sinoatrial (SA) node in the right atrium. There is a brief pause, as the electrical impulse is slowed by the Atrioventricular (AV) node and Purkinje fibers in the bundle of His. The prominent spike in the ECG (the QRS complex) is caused by this step, where the electrical impulse travels through the inter-ventricular septum and up through the outer walls of the ventricles. The sharp peak is the R component, and exact heart rate can be calculated as the inverse of the R-to-R interval (RRi). Fancy, huh?
The goal of this project is to generate an extremely cheap, functional ECG machine made from common parts, most of which can be found around your house. This do-it-yourself (DIY) ECG project is different than many others on the internet in that it greatly simplifies the circuitry by eliminating noise reduction components, accomplishing this via software-based data post-processing. Additionally, this writeup is intended for those without any computer, electrical, or biomedical experience, and should be far less convoluted than the suspiciously-cryptic write-ups currently available online. In short, I want to give everybody the power to visualize and analyze their own heartbeat!
The ECG of my own heart:
I know a lot of Internet readers aren’t big fans of reading. Therefore, I provided an outline of the process in video form. Check out the videos, and if you like what you see read more!
Video 1/3: Introducing my ECG machine
Video 2/3: Recording my ECG
Video 3/3: Analyzing my ECG
Measurement: The electrical signals which command cardiac musculature can be detected on the surface of the skin. In theory one could grab the two leads of a standard volt meter, one with each hand, and see the voltage change as their heart beats, but the fluctuations are rapid and by the time these signals reach the skin they are extremely weak (a few millionths of a volt) and difficult to detect with simple devices. Therefore, amplification is needed.
Amplification: A simple way to amplify the electrical difference between two points is to use a operational amplifier, otherwise known as an op-amp. The gain (multiplication factor) of an op-amp is controlled by varying the resistors attached to it, and an op-amp with a gain of 1000 will take a 1 millivolt signal and amplify it to 1 volt. There are many different types of microchip op-amps, and they’re often packaged with multiple op-amps in one chip (such as the quad-op-amp lm324, or the dual-op-amp lm358n). Any op-amp designed for low voltage will do for our purposes, and we only need one.
Noise: Unfortunately, the heart is not the only source of voltage on the skin. Radiation from a variety of things (computers, cell phones, lights, and especially the wiring in your walls) is absorbed by your skin and is measured with your ECG, in many cases masking your ECG in a sea of electrical noise. The traditional method of eliminating this noise is to use complicated analog circuitry, but since this noise has a characteristic, repeating, high-frequency wave pattern, it can be separated from the ECG (which is much slower in comparison) using digital signal processing computer software!
Digitization: Once amplified, the ECG signal along with a bunch of noise is in analog form. You could display the output with an oscilloscope, but to load it into your PC you need an analog-to-digital converter. Don’t worry! If you’ve got a sound card with a microphone input, you’ve already got one! It’s just that easy. We’ll simply wire the output of our ECG circuit to the input of our sound card, record the output of the op-amp using standard sound recording software, remove the noise from the ECG digitally, and output gorgeous ECG traces ready for visualization and analysis!
I’ll be upfront and say that I spent $0.00 making my ECG machine, because I was able to salvage all the parts I needed from a pile of old circuit boards. If you need specific components, check your local RadioShack. If that’s a no-go, hit-up Digikey (it’s probably cheaper too). Also, resistor values are flexible. Use mine as a good starter set, and vary them to suit your needs. If you buy everything from Digikey, the total cost of this project would be about $1. For now, here’s a list of all the parts you need:
Making the Device
Keep in mind that I’m not an electrical engineer (I have a masters in molecular biology but I’m currently a dental student if you must know) and I’m only reporting what worked well for me. I don’t claim this is perfect, and I’m certainly open for (and welcome) suggestions for improvement. With that in mind, here’s what I did!
This is pretty much it. First off is a power source. If you want to be safe, use three AAA batteries in series. If you’re a daredevil and enjoy showing off your ghettorigging skills, do what I did and grab 5v from a free USB plug! Mua ha ha ha. The power goes into the circuit and so do the leads/electrodes connected to the body. You can get pretty good results with only two leads, but if you want to experiment try hooking up an extra ground lead and slap it on your foot. More on the electrodes later. The signal from the leads is amplified by the circuit and put out the headphone cable, ready to enter your PC’s sound card through the microphone jack!
Note how I left room in the center of the circuit board. That was intentional! I wanted to expand this project by adding a microcontroller to do some on-board, real-time analysis. Specifically, an ATMega8! I never got around to it though. Its purpose would be to analyze the output of the op-amp and graph the ECG on a LCD screen, or at least measure the time between beats and display HR on a screen. (More ideas are at the bottom of this document.) Anyway, too much work for now, maybe I’ll do it one day in the future.
ECG circuit diagram:
This is the circuit diagram. This is a classical high-gain analog differential amplifier. It just outputs the multiplied difference of the inputs. The 0.1uF capacitor helps stabilize the signal and reduce high frequency noise (such as the audio produced by a nearby AM radio station). Use Google if you’re interested in learning exactly how it works.
This is how I used my LM358N to create the circuit above. Note that there is a small difference in my board from the photos and this diagram. This diagram is correct, but the circuit in some of the pictures is not. Briefly, when I built it I accidentally connected the (-) lead directly to ground, rather than to the appropriate pin on the microchip. This required me to place a 220kOhm between the leads to stabilize the signal. I imagine if you wire it CORRECTLY (as shown in these circuit diagrams) it will work fine, but if you find it too finicky (jumping quickly from too loud to too quiet), try tossing in a high-impedance resistor between the leads like I did. Overall, this circuit is extremely flexible and I encourage you to build it on a breadboard and try different things. Use this diagram as a starting point and experiment yourself!
You can make electrodes out of anything conductive. The most recent graphs were created from wires with gator clips on them clamping onto pennies (pictured). Yeah, I know I could solder directly to the pennies (they’re copper) but gator clips are fast, easy, and can be clipped to different materials (such as aluminum foil) for testing. A dot of moisturizing lotion applied to the pennies can be used to improve conduction between the pennies and the skin, but I didn’t find this to be very helpful. If pressed firmly on the body, conduction seems to be fine. Oh! I just remembered. USE ELECTRICAL TAPE TO ATTACH LEADS TO YOUR BODY! I tried a million different things, from rubber bands to packaging tape. The bottom line is that electrical tape is stretchy enough to be flexible, sticky enough not to fall off (even when moistened by the natural oils/sweat on your skin), and doesn’t hurt that bad to peel off.
Some of the best electrodes I used were made from aluminum cans! Rinse-out a soda can, cut it into “pads”, and use the sharp edge of a razor blade or pair of scissors to scrape off the wax coating on all contact surfaces. Although a little unconformable and prone to cut skin due to their sharp edges, these little guys work great!
Hooking it Up
This part is the most difficult part of the project! This circuit is extremely finicky. The best way to get it right is to open your sound editor (In Windows I use GoldWave because it’s simple, powerful, and free, but similar tools exist for Linux and other Unix-based OSes) and view the low-frequency bars in live mode while you set up. When neither electrode is touched, it should be relatively quiet. When only the + electrode is touched, it should go crazy with noise. When you touch both (one with each hand) the noise should start to go away, possibly varying by how much you squeeze (how good of a connection you have). The whole setup process is a game between too much and too little conduction. You’ll find that somewhere in the middle, you’ll see (and maybe hear) a low-frequency burst of noise once a second corresponding to your heartbeat. [note: Did you know that’s how the second was invented? I believe it was ] Once you get that good heartbeat, tape up your electrodes and start recording. If you can’t get it no matter what you do, start by putting the ground electrode in your mouth (yeah, I said it) and pressing the + electrode firmly and steadily on your chest. If that works (it almost always does), you know what to look for, so keep trying on your skin. For short recordings (maybe just a few beats) the mouth/chest method works beautifully, and requires far less noise reduction (if any), but is simply impractical for long-term recordings. I inside vs. outside potential is less susceptible to noise-causing electrical radiation. Perhaps other orifices would function similarly? I’ll leave it at that. I’ve also found that adding a third electrode (another ground) somewhere else on my body helps a little, but not significantly. Don’t give up at this step if you don’t get it right away! If you hear noise when + is touched, your circuit is working. Keep trying and you’ll get it eventually.
Recording the ECG
This is the easy part. Keep an eye on your “bars” display in the audio program to make sure something you’re doing (typing, clicking, etc) isn’t messing up the recording. If you want, try surfing the net or playing computer games to see how your heart varies. Make sure that as you tap the keyboard and click the mouse, you’re not getting noise back into your system. If this is a problem, try powering your device by batteries (a good idea for safety’s sake anyway) rather than another power source (such as USB power). Record as long as you want! Save the file as a standard, mono, wave file.
Digitally Eliminating Noise
Now it’s time to clean-up the trace. Using GoldWave, first apply a lowpass filter at 30 Hz. This kills most of your electrical noise (> 30hz), while leaving the ECG intact (< 15Hz). However, it dramatically decreases the volume (potential) of the audio file. Increase the volume as necessary to maximize the window with the ECG signal. You should see clear heartbeats at this point. You may want to apply an auto-gain filter to normalize the heartbeats potentials. Save the file as a raw sound file (.snd) at 1000 Hz (1 kHz) resolution.
Presentation and Analysis
Now you’re ready to analyze! Plop your .snd file in the same folder as my [ecg.py script], edit the end of the script to reflect your .snd filename, and run the script by double-clicking it. (Keep in mind that my script was written for python 2.5.4 and requires numpy 1.3.0rc2 for python 2.5, and matplotlib 0.99 for python 2.5 – make sure you get the versions right!) Here’s what you’ll see!
This is a small region of the ECG trace. The “R” peak is most obvious, but the details of the other peaks are not as visible. If you want more definition in the trace (such as the blue one at the top of the page), consider applying a small collection of customized band-stop filters to the audio file rather than a single, sweeping lowpass filter. Refer to earlier posts in the DIY ECG category for details. Specifically, code on Circuits vs. Software for noise reduction entry can help. For our purposes, calculating heart rate from R-to-R intervals (RRIs) can be done accurately with traces such as this.
Your heart rate fluctuates a lot over time! By plotting the inverse of your RRIs, you can see your heart rate as a function of time. Investigate what makes it go up, go down, and how much. You’d be surprised by what you find. I found that checking my email raises my heart rate more than first-person-shooter video games. I get incredibly anxious when I check my mail these days, because I fear bad news from my new university (who knows why, I just get nervous about it). I wonder if accurate RRIs could be used to assess nervousness for the purposes of lie detection?
This is the RRI plot where the value of each RRI (in milliseconds) is represented for each beat. It’s basically the inverse of heart rate. Miscalculated heartbeats would show up as extremely high or extremely low dots on this graph. However, excluding points above or below certain bounds means that if your heart did double-beat, or skip a beat, you wouldn’t see it. Note that I just realized my axis label is wrong (it should be sec, not ms). Oh well =o
A Poincare Plot is a commonly-used method to visually assess heart rate variability as a function of RRIs. In this plot, each RRI is plotted against the RRI of the next subsequent beat. In a heart which beats at the same speed continuously, only a single dot would be visible in the center. In a heart which beats mostly-continuously, and only changes its rate very slowly, a linear line of dots would be visible in a 1:1 ratio. However, in real life the heart varies RRIs greatly from beat to beat, producing a small cloud of dots. The size of the cloud corresponds to the speed at which the autonomic nervous system can modulate heart rate in the time frame of a single beat.
The frequency of occurrence of various RRIs can be expressed by a histogram. The center peak corresponds to the standard heart rate. Peaks to the right and left of the center peak correspond to increased and decreased RRIs, respectively. A gross oversimplification of the interpretation of such data would be to state that the upper peak represents the cardio-inhibitory parasympathetic autonomic nervous system component, and the lower peak represents the cardio-stimulatory sympathetic autonomic nervous system component.
Taking the Fast Fourier Transformation of the data produces a unique trace whose significance is extremely difficult to interpret. Near 0Hz (infinite time) the trace heads toward ∞ (infinite power). To simplify the graph and eliminate the near-infinite, low-frequency peak we will normalize the trace by multiplying each data point by its frequency, and dividing the vertical axis units by Hz to compensate. This will produce the following graph…
This is the power spectrum density (PSD) plot of the ECG data we recorded. Its physiological interpretation is extraordinarily difficult to understand and confirm, and is the subject of great debate in the field of autonomic neurological cardiac regulation. An oversimplified explanation of the significance of this graph is that the parasympathetic (cardio-inhibitory) branch of the autonomic nervous system works faster than the sympathetic (cardio-stimulatory) branch. Therefore, the lower peak corresponds to the sympathetic component (combined with persistent parasympathetic input, it’s complicated), while the higher-frequency peak corresponds to the parasympathetic component, and the sympathetic/parasympathetic relationship can be assessed by the ratio of the integrated areas of these peaks after a complicated curve fitting processes which completely separates overlapping peaks. To learn more about power spectral analysis of heart rate over time in the frequency domain, I recommend skimming this introduction to heart rate variability website and the article on Heart Rate Variability following Myocardial Infarction (heart attack). Also, National Institute of Health (NIH) funded studies on HRV should be available from pubmed.org. If you want your head to explode, read Frequency-Domain Characteristics and Filtering of Blood Flow Following the Onset of Exercise: Implications for Kinetics Analysis for a lot of good frequency-domain-analysis-related discussion and rationalization.
Please, if you try this don’t die. The last thing I want is to have some kid calling me up and yelling at me that he nearly electrocuted himself when he tried to plug my device directly into a wall socket and now has to spend the rest of his life with two Abraham Lincolns tattooed onto his chest resembling a second set of nipples. Please, if you try this use common sense, and of course you’re responsible for your own actions. I provide this information as a description of what I did and what worked for me. If you make something similar that works, I’ve love to see it! Send in your pictures of your circuit, charts of your traces, improved code, or whatever you want and I’ll feature it on the site. GOOD LUCK!
If you want to try this, go for it! Briefly, this circuit uses 6 op-amps to help eliminate effects of noise. It’s also safer, because of the diodes interconnecting the electrodes. It’s the same circuit as on [this page].
Last minute thoughts:
- More homemade ECG information can be found on my earlier posts in the DIY ECG category, however this page is the primary location of my most recent thoughts and ideas.
- You can use moisturizing lotion between the electrodes and your skin to increase conduction. However, keep in mind that better conduction is not always what you want. You’ll have to experiment for yourself.
- Variation in location of electrodes will vary the shape of the ECG. I usually place electrodes on each side of my chest near my arms. If your ECG appears upside-down, reverse the leads!
- Adding extra leads can improve grounding. Try grounding one of your feet with a third lead to improve your signal. Also, if you’re powering your device via USB power consider trying battery power – it should be less noisy.
- While recording, be aware of what you do! I found that if I’m not well-grounded, my ECG is fine as long as I don’t touch my keyboard. If I start typing, every keypress shows up as a giant spike, bigger than my heartbeat!
- If you get reliable results, I wonder if you could make the device portable? Try using a portable tape recorder, voice recorder, or maybe even minidisc recorder to record the output of the ECG machine for an entire day. I haven’t tried it, but why wouldn’t it work? If you want to get fancy, have a microcontroller handle the signal processing and determine RRIs (should be easy) and save this data to a SD card or fancy flash logger.
- The microcontroller could output heart rate via the serial port.
- If you have a microcontroller on board, why not display heart rate on a character LCD?
- While you have a LCD on there, display the ECG graphically!
- Perhaps a wireless implementation would be useful.
- Like, I said, there are other, more complicated analog circuits which reduce noise of the outputted signal. I actually built Jason Nguyen’s fancy circuit which used 6 op-amps but the result wasn’t much better than the simple, 1 op-amp circuit I describe here once digital filtering was applied.
- Arrhythmic heartbeats (where your heart screws-up and misfires, skips a beat, double-beats, or beats awkwardly) are physiological (normal) and surprisingly common. Although shocking to hear about, sparse, single arrhythmic heartbeats are normal and are a completely different ball game than chronic, potentially deadly heart arrhythmias in which every beat is messed-up. If you’re in tune with your body, you might actually feel these occurrences happening. About three times a week I feel my heart screw up a beat (often when it’s quiet), and it feels like a sinking feeling in my chest. I was told by a doctor that it’s totally normal and happens many times every day without me noticing, and that most people never notice these single arrhythmic beats. I thought it was my heart skipping a beat, but I wasn’t sure. That was my motivation behind building this device – I wanted to see what my arrhythmic beats looked like. It turns out that it’s more of a double-beat than a skipped beat, as observed when I captured a single arrhythmic heartbeat with my ECG machine, as described in this entry.
- You can improve the safety of this device by attaching diodes between leads, similar to the more complicated circuit. Theory is that if a huge surge of energy does for whatever reason get into the ECG circuit, it’ll short itself out at the circuit level (conducting through the diodes) rather than at your body (across your chest / through your heart).
- Alternatively, use an AC opto-isolator between the PC sound card and the ECG circuit to eliminate the possibility of significant current coming back from the PC.
- On the Hackaday post, Flemming Frandsen noted that an improperly grounded PC could be dangerous because the stored charge would be manifest in the ground of the microphone jack. If you were to ground yourself to true ground (using a bench power supply or sticking your finger in the ground socket of an AC wall plug) this energy could travel through you! So be careful to only ground yourself with respect to the circuit using only battery power to minimize this risk.
- Do not attempt anything on this page. Ever. Don’t even read it. You read it already! You’re sill reading it aren’t you? Yeah. You don’t follow directions well do you?
SAMPLE FILTERED RECORDING:
I think this is the same one I used in the 3rd video from my single op-amp circuit. [scottecg.snd] It’s about an hour long, and in raw sound format (1000 Hz). It’s already been filtered (low-pass filtered at 30Hz). You can use it with my code below!
print "importing libraries..." import numpy, pylab print "DONE" class ECG: def trim(self, data,degree=100): print 'trimming' i,data2=0, while i<len(data): data2.append(sum(data[i:i+degree])/degree) i+=degree return data2 def smooth(self,list,degree=15): mults= s= for i in range(degree): mults.append(mults[-1]+1) for i in range(degree): mults.append(mults[-1]-1) for i in range(len(list)-len(mults)): small=list[i:i+len(mults)] for j in range(len(small)): small[j]=small[j]*mults[j] val=sum(small)/sum(mults) s.append(val) return s def smoothWindow(self,list,degree=10): list2= for i in range(len(list)): list2.append(sum(list[i:i+degree])/float(degree)) return list2 def invertYs(self): print 'inverting' self.ys=self.ys*-1 def takeDeriv(self,dist=5): print 'taking derivative' self.dys= for i in range(dist,len(self.ys)): self.dys.append(self.ys[i]-self.ys[i-dist]) self.dxs=self.xs[0:len(self.dys)] def genXs(self, length, hz): print 'generating Xs' step = 1.0/(hz) xs= for i in range(length): xs.append(step*i) return xs def loadFile(self, fname, startAt=None, length=None, hz=1000): print 'loading',fname self.ys = numpy.memmap(fname, dtype='h', mode='r')*-1 print 'read %d points.'%len(self.ys) self.xs = self.genXs(len(self.ys),hz) if startAt and length: self.ys=self.ys[startAt:startAt+length] self.xs=self.xs[startAt:startAt+length] def findBeats(self): print 'finding beats' self.bx,self.by=, for i in range(100,len(self.ys)-100): if self.ys[i]<15000: continue # SET THIS VISUALLY if self.ys[i]<self.ys[i+1] or self.ys[i]<self.ys[i-1]: continue if self.ys[i]-self.ys[i-100]>5000 and self.ys[i]-self.ys[i+100]>5000: self.bx.append(self.xs[i]) self.by.append(self.ys[i]) print "found %d beats"%(len(self.bx)) def genRRIs(self,fromText=False): print 'generating RRIs' self.rris= if fromText: mult=1 else: 1000.0 for i in range(1,len(self.bx)): rri=(self.bx[i]-self.bx[i-1])*mult #if fromText==False and len(self.rris)>1: #if abs(rri-self.rris[-1])>rri/2.0: continue #print i, "%.03ft%.03ft%.2f"%(bx[i],rri,60.0/rri) self.rris.append(rri) def removeOutliers(self): beatT= beatRRI= beatBPM= for i in range(1,len(self.rris)): #CHANGE THIS AS NEEDED if self.rris[i]<0.5 or self.rris[i]>1.1: continue if abs(self.rris[i]-self.rris[i-1])>self.rris[i-1]/5: continue beatT.append(self.bx[i]) beatRRI.append(self.rris[i]) self.bx=beatT self.rris=beatRRI def graphTrace(self): pylab.plot(self.xs,self.ys) #pylab.plot(self.xs[100000:100000+4000],self.ys[100000:100000+4000]) pylab.title("Electrocardiograph") pylab.xlabel("Time (seconds)") pylab.ylabel("Potential (au)") def graphDeriv(self): pylab.plot(self.dxs,self.dys) pylab.xlabel("Time (seconds)") pylab.ylabel("d/dt Potential (au)") def graphBeats(self): pylab.plot(self.bx,self.by,'.') def graphRRIs(self): pylab.plot(self.bx,self.rris,'.') pylab.title("Beat Intervals") pylab.xlabel("Beat Number") pylab.ylabel("RRI (ms)") def graphHRs(self): #HR TREND hrs=(60.0/numpy.array(self.rris)).tolist() bxs=(numpy.array(self.bx[0:len(hrs)])/60.0).tolist() pylab.plot(bxs,hrs,'g',alpha=.2) hrs=self.smooth(hrs,10) bxs=bxs[10:len(hrs)+10] pylab.plot(bxs,hrs,'b') pylab.title("Heart Rate") pylab.xlabel("Time (minutes)") pylab.ylabel("HR (bpm)") def graphPoincare(self): #POINCARE PLOT pylab.plot(self.rris[1:],self.rris[:-1],"b.",alpha=.5) pylab.title("Poincare Plot") pylab.ylabel("RRI[i] (sec)") pylab.xlabel("RRI[i+1] (sec)") def graphFFT(self): #PSD ANALYSIS fft=numpy.fft.fft(numpy.array(self.rris)*1000.0) fftx=numpy.fft.fftfreq(len(self.rris),d=1) fftx,fft=fftx[1:len(fftx)/2],abs(fft[1:len(fft)/2]) fft=self.smoothWindow(fft,15) pylab.plot(fftx[2:],fft[2:]) pylab.title("Raw Power Sprectrum") pylab.ylabel("Power (ms^2)") pylab.xlabel("Frequency (Hz)") def graphFFT2(self): #PSD ANALYSIS fft=numpy.fft.fft(numpy.array(self.rris)*1000.0) fftx=numpy.fft.fftfreq(len(self.rris),d=1) fftx,fft=fftx[1:len(fftx)/2],abs(fft[1:len(fft)/2]) fft=self.smoothWindow(fft,15) for i in range(len(fft)): fft[i]=fft[i]*fftx[i] pylab.plot(fftx[2:],fft[2:]) pylab.title("Power Sprectrum Density") pylab.ylabel("Power (ms^2)/Hz") pylab.xlabel("Frequency (Hz)") def graphHisto(self): pylab.hist(self.rris,bins=20,ec='none') pylab.title("RRI Deviation Histogram") pylab.ylabel("Frequency (count)") pylab.xlabel("RRI (ms)") #pdf, bins, patches = pylab.hist(self.rris,bins=100,alpha=0) #pylab.plot(bins[1:],pdf,'g.') #y=self.smooth(list(pdf[1:]),10) #x=bins[10:len(y)+10] #pylab.plot(x,y) def saveBeats(self,fname): print "writing to",fname numpy.save(fname,[numpy.array(self.bx)]) print "COMPLETE" def loadBeats(self,fname): print "loading data from",fname self.bx=numpy.load(fname) print "loadded",len(self.bx),"beats" self.genRRIs(True) def snd2txt(fname): ## SND TO TXT ## a=ECG() a.loadFile(fname)#,100000,4000) a.invertYs() pylab.figure(figsize=(7,4),dpi=100);pylab.grid(alpha=.2) a.graphTrace() a.findBeats() a.graphBeats() a.saveBeats(fname) pylab.show() def txt2graphs(fname): ## GRAPH TXT ## a=ECG() a.loadBeats(fname+'.npy') a.removeOutliers() pylab.figure(figsize=(7,4),dpi=100);pylab.grid(alpha=.2) a.graphHRs();pylab.subplots_adjust(left=.1,bottom=.12,right=.96) pylab.savefig("DIY_ECG_Heart_Rate_Over_Time.png"); pylab.figure(figsize=(7,4),dpi=100);pylab.grid(alpha=.2) a.graphFFT();pylab.subplots_adjust(left=.13,bottom=.12,right=.96) pylab.savefig("DIY_ECG_Power_Spectrum_Raw.png"); pylab.figure(figsize=(7,4),dpi=100);pylab.grid(alpha=.2) a.graphFFT2();pylab.subplots_adjust(left=.13,bottom=.12,right=.96) pylab.savefig("DIY_ECG_Power_Spectrum_Weighted.png"); pylab.figure(figsize=(7,4),dpi=100);pylab.grid(alpha=.2) a.graphPoincare();pylab.subplots_adjust(left=.1,bottom=.12,right=.96) pylab.savefig("DIY_ECG_Poincare_Plot.png"); pylab.figure(figsize=(7,4),dpi=100);pylab.grid(alpha=.2) a.graphRRIs();pylab.subplots_adjust(left=.1,bottom=.12,right=.96) pylab.savefig("DIY_ECG_RR_Beat_Interval.png"); pylab.figure(figsize=(7,4),dpi=100);pylab.grid(alpha=.2) a.graphHisto();pylab.subplots_adjust(left=.1,bottom=.12,right=.96) pylab.savefig("DIY_ECG_RR_Deviation_Histogram.png"); pylab.show(); fname='publish_05_10min.snd' #CHANGE THIS AS NEEDED #raw_input("npress ENTER to analyze %s..."%(fname)) snd2txt(fname) #raw_input("npress ENTER to graph %s.npy..."%(fname)) txt2graphs(fname)
Salil notified me that he used a similar concept to create an ECG machine using some fancier circuitry to eliminate noise with hardware rather than rely on software. Way to go Salil! Here’s a video of his project: http://youtu.be/uV8UyEQxVII
Tonight’s brief entry is nothing more than a rant against a disturbing trend in the PC industry. I sit here half past nightnight screaming at my computer screen. To make a long story short, I need to do an incredibly simple task: burn a video file to a DVD. It’s a ~30min slideshow in AVI format. This should be incredibly easy, right? In linux it’s only a process of typing a few commands into a console. No biggie.
I’d use this method, but I only have one linux box with a DVD burner and it’s flaking out very badly. I try to burn DVDs, but it has power issues and it fails 9 out of 10 times. I should throw away the drive. The only functional DVD drive is on my wife’s PC, a computer running Microsoft Windows Vista.
How do you burn an AVI to a DVD in Windows? You can’t. Well, not easily. Sure, there are simple commands for Linux and it can do everything using free, small software only a few MB in size. Apparently the only way to do similar things for windows is to use pirated software, or fool around with a million steps to use a combination of multiple quasi-free programs. I know I’m being somewhat irrational about its complexity on Windows, but I’m mad, so I’m forgoing logic at this point. I knew that Nero 9 should be able to do this. I looked for it on a popular torrent website (yes, it seems the only way to be productive in Windows is to use pirated software) and downloaded it.
I can’t wrap my head around what Nero 9 does that requires almost 1GB of my hard drive space, requiring over 45 minutes to install on my wife’s 64-bit dual-core2.4 GHz Vista-running PC with 4GB of ram. Have software companies COMPLETELY abandoned the principals of simplicity, speed, reliability, and functionality? Why is it that common common software distributions (think about Microsoft Office programs and virtually every Adobe product) increase in size exponentially with every release, offer virtually no improvement in speed, and with questionable feature upgrades?
FREAKING UPDATE: After going through an hour of frustration, I concluded that Nero 9 is incapable of creating a DVD from my AVI file. My wife reminded me that my linux-running laptop has a DVD burner. I hopped on it, typed
apt-get install devede; devede and burned my DVD in 5 minutes with a 14MB program. Soon, people will start rising up and demanding lite, cheap, and FUNCTIONAL products for Windows. Until they do, I will just avoid it when I can.
Bah! I need to go to bed. I’m getting mad at my computer -_-
UPDATE: An improved ECG design was posted in August, 2016.
Check out: http://www.swharden.com/wp/2016-08-08-diy-ecg-with-1-op-amp/
Although I made a functional ECG circuit, it was extremely finnicky. If you attached the electrodes too weakly (not a good enough connection), you would get no signal. If you attached the electrodes too tightly (too good of a connection), you wouldn’t get a signal either. You had to have just the right resistance between the electrodes and the body for them to work. I tried some things and finally discovered that a resistor between the circuit and me (on the ground lead) significantly improved the situation, but requries a really good body connection. My leads (2) were made from wires (non-shielded) with gator clips at the end clamping onto pennies. I added a dab of moisterizer to the pennies to get a really good connection and used electical tape to attach them to my chest (+) and leg (GND). I recorded heart data in 10 minute blocks, and it worked amazingly well! Here is a video if me recording my ECG while playing Counter-Strike.
If you look closely you can see my heartbeat as the two leftmost bars on the display of the laptop. I’m continuing to work on this circuit, and will release details when it’s a little more complete. My plan is to write it up formally, provide a ton of examples/documentation, and really dive into the analysis aspect of it (RRI calculations, variability analysis, etc) and post it to Hackaday. DIY ECGs are nothing new, but no one who’s made one has really gone deep into its interpretation. My goal is to have this complete by next week! For now, enjoy the pretty videos.
I’m super-busy tonight and only have a couple minutes to write. To make a long story short, I’m converting images from various formats to various other formats (could I be any more vague?) preparing for insertion into a scientific manuscript which [crosses fingers] I hope will be published soon. While working, I was listening to sky.fm‘s classical stream and a song popped on that completely knocked my mind (and productivity) off track. The song is Johann Sebastian Bach’s Air from the Suite No 3 (in D major). Even if you don’t know it by name, you’d recognize its tune. Daaaa da da da da da da da da daaa da da daaa… sorry, I’m not a good singer. Anyhow, the second I heard it my brain instantly loaded-up images of Japanese schoolchildren violently murdering eachother. Yes, I’m referring to Battle Royale. I read the book and even saw the movie back when I was a teenager. I highly recommend both, but it’s been years since I’ve read/seen either one. In the movie, I love the way they inserted incredibly peaceful music at the most disturbing of times. Below is the clip of the film that I will forever image in my head whenever I hear Bach’s famous air.