This work intends to devise an efficient feature extraction scheme for identifying common cardiac abnormalities using the Fourier-Bessel (FB) expansion of RR-intervals and time-frequency based features of Electrocardiogram (ECG) signals. The Bessel basis, when used for representing the RR-intervals, meaningfully enhances the pathologically induced low-frequency changes in terms of FB coefficients. To ensure the characterization of diverse pathological variability present in the ECG signals, time-frequency domain features are also extracted using scattering transform. The multi-label classification of the ECG signals, for five different lead combinations, is performed using Gated recurrent unit into specified twenty-six categories. The experimental outcomes, for five-fold cross validation using 2021 PhysioNet/CinC Challenge dataset, demonstrates the challenge scoring metric on the twelve-lead, six-lead, four-lead, three-lead, and two-lead configurations as 0.4038, 0.4270, 0.4296, 0.4380, and 0.4482 respectively. According to the results, the proposed method justifies the use of the FB and scattering transforms together for the detection and identification of common cardiac problems using ECG signals.