I think you answered the question yourself. From my experience and understanding (I work with signals from patients paced with a temporary cardiac pacing device/generator), these paced signals tend to be quite uniform and follow the same pattern along the time axis, depending on the paced settings. For example, if I have a patient that is paced in both atria and ventricles, it is expected that the heartbeats will follow the same trends, given that you set fixed time delays from the generator and, therefore, the atria/ventricles will be contracted at those specific intervals.

Hope this helps.

I have a feeling that this phenomenon is related to something called harmonic frequencies. If we say that the first peak is the fundamental frequency you can multiply the frequency by an integer to find the next peak (to find the next harmonic frequency). However, I am not sure if this is the case, but if it is would really like to understand the theoretical background for this.

In the attached plot I found the first peak to be at 11.7Hz and then I multiply it by 1,2,3, ... n and you see that the peaks follow this pattern quite well.

Hi Bjorn,

You are right when you discuss the Harmonic frequencies. In FFT you are taking time domain data and converting it into frequency domain data. You are assuming the FFT time domain continuing indefinitely. If you separate the ECG signal into points (you will arrive to certain number of samples) and the FFT assumes that these samples are infinitely repeated. All FFT have a frequency resolution that is equal to the sampling frequency divided by the number of samples. You will have certain sampling rate/ number of samples which would determine your frequency resolution. When you are using pacemaker, you are examining (pacemaker analyzes) frequency components of physiological signals and detect concealed properties to identify physical anomalies. Please read this paper PMID: 32051675 which would explain spectrum analysis algorithms such as Fast Fourier Transform (FFT), which are crucial for pattern detection. You have also listed common abnormal (path-physio ECG recordings) in Table 1. Values from these conditions indicate that in most common cases of pacemaker usage, the sample values encountered by the system will hold a high probability of being repeated over the course of time, hence you are able to find their main harmonic frequency and set up pacing such that it is recognized and reversed to your normal harmonics e.g. (Table 1).Article Design and implementation of an ultra-low energy FFT ASIC fo...

Exactly Paced beat are usually recorded in frequency domain in contrast to time domain for sinus Rhythm or sinus arrhytmia. But using the Fast Fourier transform (FFT) we can convert time domain into frequency domain. Usually paced beat are paced at regular interval untill intrinsic rhythm inhibit the pacing rate.

Filip Konecny, here I simulated 25 sinus waves and added them together and then FFT it. As you see it looks pretty similar to the pacemaker beats, so it must be something with the regularity of the frequency components.

xaxis = np.linspace(0,10,5000)

sim_sine = np.sin(xaxis*np.pi)

for i in range(1,25):

temp = np.sin(xaxis*np.pi*10*i)

sim_sine += temp