Seizures are one of the serious neuropsychiatric disorders that affect 50 million people across the world, and 80% of them live in the developing countries like India. In such kind of critical care, delays in the diagnosis of neurological conditions can have a significant impact on patients with seizure. To address this treatment gap, we propose an early identificationof pre-ictal period of seizure and brain lobes for seizure detection by using electroencephalogram (EEG) biomarkers. Five subjects were voluntarily involved in this study. The raw EEG signals were filtered and decomposed using wavelet techniques and the features such as relative delta energy, relative theta energy, total beta energy, heart rate, and neuronal activity were extracted for analysis. The results showed that the relative theta activity and the neuronal activity are found to be the better features in early predicting of period of pre-ictal seizures, as both the features can be seen with significant differences among the three different time periods in all the electrode positions, which are taken into consideration. It is also evident that the occipital lobe is better in indicating the pre-ictal period earlier, as the observed data shows expected outcome from the electrode positions O1 and O2 (occipital lobe). On national level, this study will enable the primary health centres to fulfil its dream of providing basic medical facilities to serve huge sections of population with seizure. © Published under licence by IOP Publishing Ltd.