This work proposes an automated epileptic seizure detection pipeline using generated rhythmicity spectrograms and Generic Convolutional Neural Networks (CNN). 1D multi-channel, scalp-EEG signals taken from a publically available EEG database (CHB-MIT Scalp EEG database, version: 1.0.0) were converted to time-variable, non-overlapped and one-sided rhythmicity spectrograms (2D images) through Short-time Fourier transform (STFT) method for patient no. chb01, chb02, chb03 and chb05. A two class, supervised classification between seizure (ictal) and non-seizure images was performed. Thorough cross-patient test set analysis has been presented along with evaluation metrics such as precision, recall, F1-score, and loss and accuracy of the model on the test set. The generic model achieved an average training, validation and test set accuracy up-to 91.89, 88.17 and 61% respectively. An automated epileptic seizure detection system can escalate the process of diagnosis and early decision to surgery which may aid in quality of life (QOL) of diseased patients.