## 1 Introduction  
Electrocardiographs, or ECGs, are crucial diagnostic and monitoring devices for diseases of the heart. ECGs provide important information related to the electrical activity of the heart. The reliability of ECG signals is vital in gauging patient results and the dependability of diagnostic equipment. Data from an ECG are usually sampled with uniform sampling techniques, which are used to collect the data points at constant intervals. It is simple and intuitive. However, this approach might compress the most diagnostically significant differences embedded within the ECG signal, particularly in the presence of noise and interference. Therefore, the increasing interest in studying non-uniform sampling lies in its priority given to the integrity of records and diagnostic efficiency. The potential application of non-uniform sampling directly reduces the space the electrocardiogram occupies in the storage or transmission bandwidth and, at the same time, enables a more thorough representation of vulnerable details in signals. Additionally, the technique is of interest to the economy and quality of diagnostics. Recent developments in neural network topology have realized practicable methods to improve the non-uniform sampling of ECG signals. The methods try to adapt themselves to time point selection to improve the similarity between the original signal and the resampledsignal. One of the methods to optimize the resemblance, for instance, the ECG signal, is to choose time points at which the most prominent characteristics of the waveform are captured. Neural networks have shown the ability to capture the critical points that optimally retained the underlying diagnostic information through the use of time series models [1]. However, careful attention should be paid to the selection of such a point in time since too much change will create an automatic loss of critical diagnostic information. The non-uniform sampling of the ECG signals substantially has the edge overuniform sampling because these techniques do not include the limitations implicit in uniform sampling, particularly when handling complex biomedical data, noise, etc. _Key features prioritization:_ Uniform sampling entails data points at evenly spaced-out intervals, which is a simple method. However, this method does not take into account the variable importance of different sections of the ECG waveform. Since ECG signals have stages of swift change (e.g., during the QRS complex) and stages of relative stability (e.g., during the T-wave or P-wave), uniform sampling might either oversample less critical areas or undersample the more critical ones. In the case of non-uniform sampling, the selection of sampling points itself is adaptive and depends on the importance of the different parts of the signal. More attention is given to those areas where the ECG signal changes abruptly, thus identifying and preserving critical features. _Noise resilience:_ In the presence of noise and interference, uniform sampling might involuntarily capture and accentuate these distortions, possibly concealing the critical features of the ECG signal. The non-uniform sampling assists in nullifying the effect of noise at the sampling points and thus cancels out the extraneous data that otherwise would have been captured. The quality of the heart's electrical activity is enhanced by the extraction of clearer and more accurate patterns. _Better reconstruction:_ Concentrating on more informative ECG sections, non-uniform sampling leads to a better reconstruction of the original signal from the sampled data. The ECG is still preserved with its essential fragments after compression or down-sampling. Two major challenges in the design of efficient non-uniform sampling methods for ECG signals are choosing the best time points at which the signal has to be sampled and ensuring the resampled signal reliably carries the original waveform [1; 2]. Improved signal reconstruction in ECG analysis has some dynamic benefits associated with diagnostic precision and patient outcomes.