EGC and waves $p-s$ for an earthquake: functions examples using Taylor's expansion We know that the electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart and in medicine plays an indispensable role. ECG is one of the indicators of the total, as well as the current state of the human organism is therefore an important diagnostic benefit. Same approach for an earthquake.
A physical process can be described either in the time domain, by the values of some quantity
$h$ as a function of time $t$, e.g., $h(t)$, or in the frequency domain, where the process is specified by giving its amplitude $H$ (generally a complex number) as a function of frequency $\nu$, that is $H(\nu)$, with $−∞ < \nu < ∞$. For many purposes it is useful to consider $h(t)$ and $H(\nu)$ as two different representations of the same function.
The Fourier transform $H(\nu)$ converts waveform data in the time domain into the frequency domain $(1)$.
The Inverse Fourier transform $h(t)$ converts the frequency domain components back into the original
time-domain signal $(2)$:
$$h(t)=\int^∞_{−∞}H(\nu)e^{−2πi \ \nu \ t} d\nu \tag 1$$
$$H(\nu) = \int^∞_{−∞} h(t)e^{2πi \ \nu \ t} dt \tag 2$$

Is it possible to approximate a graph of an ECG or an earthquake waves without to use Fourier transformers, for example with a Taylor's expansion thus can I use it for high school students?

 A: There are several studies dealing with this topic. The ECG morphology is the result of very complex interaction of physiological, electrical, and biochemical factors, and the component waves in ECG signals represent an individual  characteristic. Different alternative mathematical models have been proposed, even without Fourier transformers, for example to represent the distribution of different morphologies of QRS wave - the most evident component of ECG tracing, reflecting ventricular depolarization - including Gaussian, Mexican-Hat, and Rayleigh probability density functions. Previous studies searched for the optimal parameters to minimize the normalized RMS error between mathematical models and QRS shapes and distributions. In some cases, simulators were utilized to generate  synthetic  signals  in the context of dynamic  models accounting for variations of  physiological parameters. Other studies focused on the shape of T wave and the behaviour of QT interval - the ECG components reflecting ventricular repolarization - or on the shape of the P wave, reflecting atrial depolarization.
A pratical application of mathematical modeling of ECG waveforms is  automatic detection and pattern quantification  of different  ECG wave morphologies to discriminate  some  types  of  arrhythmias  and conduction abnormalities.
