Fourier transform (FT) is one of the transformation techniques to convert the time-domain signal into the frequency-domain signal. Due to its easy usage, it is applied in many engineering approaches where the data are made of periodic components. Electrocardiography (ECG) is an imaging modality which represents the data of electrophysiological activities of heart. ECG data are gathered from the electrodes that are placed on the specific locations on chest, and electrical activities of heart generally produce periodically shaped time series data. However, this periodicity can be perturbed, or side oscillations can occur due to certain abnormal activities in hearts. From our preliminary studies, we have found that the implementation of the FT multiple times in ECG datasets can be useful in the detection of main and hidden periodicities in autonomous applications. Especially when they are hard to be observed with the first FT. Hereby, this study aims to improve the accuracy of successful detection of such perturbations in ECG data by applying multiple FT and finding the relationship between the side oscillations and also detection of some features of the n^th FT in datasets of various cardiac disorders