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Acquisition and Analysis of an ECG (electrocardiography) Signal
 

Acquisition and Analysis of an ECG (electrocardiography) Signal

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Electrocardiographs record cardiac activity of the heart and are used to diagnose disease, detect abnormalities, and learn about overall heart function. Electrical signals are produced by contractions in the heart walls which drive electrical currents and create different potentials throughout the body. By placing electrodes on the skin, one can detect and record this electrical activity in an ECG. ECGs are non-invasive, making them a useful tool to assess how well a patients heart is performing, such as by measuring how well blood flows to the organ.

This video will illustrate the principals of ECGs and demonstrate how to acquire, process, and analyze a typical ECG signal using a biopotential amplifier. Other biomedical applications that utilize electrical signal processing to diagnose disease will also be discussed.

To understand the principles of an ECG, let's first understand how the heart produces electrical signals. For a normal, healthy heart, at rest, an ECG displays a series of waves that reflect the different phases of a heartbeat. The ECG starts in the sinoatrial node, also known as the SA node, which is located in the right atrium and acts as a pacemaker in the heart. The electrical signals cause atrial contraction forcing blood into the ventricles. This sequence is recorded as the P wave on the ECG. This signal then passes from the atria across the ventricles, causing them to contract and pump blood to the rest of the body. This is recorded as the QRS complex.

Finally, the ventricles relax and this is recorded as the T wave. The process then begins again and is repeated for every heartbeat. Notice that the QRS wave is much larger than the P wave, this is because the ventricles are larger than the atria. Meaning they mask the relaxation of the atria or the T wave. Other processes in the body, like respiration or muscle contractions, can interfere with the ECG measurement. As can currents from the circuitry used to obtain them. Often, the electrical signals that the ECG is attempting to record are quite weak. Therefor, a biopotential amplifier is used to increase their amplitude which allows them to be further processed and recorded.

There are three main components to the biopotential amplifier, the patient protection stage, the instrumentation amplifier, and the high pass filter. As the main suggests, the patient protection circuit uses a combination of resistors and diodes to protect, both, the patient and the machine and equipment. The resistors limit the current that flows through the patient, where as the diodes keep the current flowing in the correct direction.

The next stage is the instrumentation amplifier, which amplifies the difference between the inputs from each electrode. It is composed of three operational amplifiers. Two to increase the resistance from each input, and the third to amplify the difference between the input signals.

The last stage is the high pass filter, which reduces the noise and filters out low frequency signals arising from patient movement or respiration. Now that you know how an ECG is measured, let's see how to construct a biopotential amplifier and process the data to get a clean ECG signal.

Having reviewed the main principals of electrocardiography, let's see how to build a biopotential amplifier and acquire an ECG signal. To begin, first gather a proto-board, an AD-620 instrumentation amplifier, and all necessary circuit components. Then, calculate the values of all of the resistors and capacitors in the circuit using the following equation.

For the high pass filter, the cut off frequency should be 0.5 hertz.

Then, plug in the capacitor value to determine the resistance. Next, build a biopotential amplifier according to the provided diagram. Here is what the final circuit should look like. Attach three wires with alligator clips to the binding posts of a DC power supply, then turn on the power source. Adjust the voltage to plus five volts and minus five volts, and connect the the wires, in series, to the circuit.

Now, use an alcohol prep pad to wipe the patients right wrist, left wrist, and right ankle. Add conductive adhesive gel to the electrodes before placing them on the patient. Then, connect the electrodes to the circuit using wires with alligator clips. Turn on the oscilloscope and acquire the ECG signal. Adjust the horizontal and vertical scales as needed. With these adjustments, you should be able to see the R peak of the wave form.

Connect the circuit to the PXI chassis, then open the instrumentation software and, either, use or write a program that will display the ECG signal and a wave form graph.

Configure the data acquisition interface with the following settings. Label the scale of the x-axis to display time and seconds, then display the ECG signal as a waveform. If the signal needs to be amplified, create a gain control and set it so that the amplitude of the ECG is two VP.

Now that we have demonstrated how to acquire an ECG signal, let's see how to analyze the results. Here is a representative ECG signal. The P, QRS, and T waves are barely discernible because they are obscured by noise and fluctuations. This signal needs to be filtered. To transform this signal, first, select Signal Processing then Spectral on the menu. A Fast Fourier Transform algorithm calculates and plots the spectrum of the signal displaying the frequency as discreet values on the horizontal axis. Most of the energy in the signal is at low frequencies.

But, there is a high intensity peak in the medium frequency range, which is assumed to be noise. Frequency is plotted as k on the horizontal axis and goes from zero to N minus one over two, where N is the length of the sequence. For this experiment, N equals 2,000. Calculate the analog frequency for each k value using the following equation, where f s is the sampling frequency and determine the frequency of the high intensity peak based on the FFT graph.

Then, create a low pass filter with a cutoff frequency of 100 hertz. Use, either, the Butterworth or Chebyshev function to filter the signal, which should attenuate at least 60 decibels per decade in the stop band. Connect the output signal of the data sub VI to the input of the low pass filter. This filter removes the extraneous high frequency waves of the ECG. Now, create a Bandstop filter and set the cutoff frequencies at around 55 and 70 hertz.

To remove the noisy signal, around 60 hertz. Then, connect the output of the low pass filter to the input of the Bandstop filter. Try border frequencies that are close to 60 hertz. This will reduce interference without effecting other frequencies. The ECG signal should now be clear with distinct P, QRS, and T complexes.

Now, let's determine the heart rate using the filtered ECG signal. First, use the peak detector sub VI to find the peaks of the signal. Choose the most appropriate value based on the signals amplitude of the R wave for the threshold. Then, use the Index Array sub VI to determine the location of the peaks.

Subtract the lower peak position from the higher position, then multiply this value by the sampling period, T, which is equal to one over f s. This value is the length of time between two R waves. Adjust the units to determine the beats per minute.

In this demonstration, the measured heart rate was approximately 60 beats per minute.

ECG and signal processing have important applications in, both, medicine and research. In addition to being non-invasive, ECGs are relatively inexpensive. Making it a useful and accessible tool in hospitals. ECGs can even be adapted to more complex and longterm monitoring of patients who are being treated for Acute Coronary Syndrome.

For this, 12 ECG leads are used, which can identify transient myocardial ischemia in asymptomatic patients. Signal sampling and processing is also used in electroencephalography to measure electrical signals from the brain. EEGs are commonly used in conjunction with functional MRI as a multimodal imaging technique.

The method noninvasively generates cortical maps of brain activity for many neuroimaging applications, such as after visual or motor activation.

You've just watched Jove's introduction to acquiring and analyzing ECG signals. You should now understand how an ECG signal is produced and how to create a biopotential amplifier to detect weak electrical signals. You have also seen some biomedical applications of signal processing for medical diagnosis.

Thanks for watching.

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