In this paper, we present a new modular approach for detection of inter-ictal spikes in intracranial iEEG recordings from patients that are suffering from pharmaco-resistant form of epilepsy. This new approach is presented in the form of a detection framework consisting of three primary modules: first level detector, second level feature extractor, and third level detection classifier, where each module is responsible for a specific functionality. This detection framework can be perceived as a three slot system, where modules can be easily plugged in their slots and replaced by a different module or implementation on demand, in order to adapt the quality of detection (measured in terms of sensitivity, precision or inter-recording adaptability) and computational cost. Using complex real-world data sets it was confirmed that the proposed framework provides highly sensitive and precise detection, while it also significantly reduces the computation time.