The methods used to detect pulsars have gone through many iterations over the years. Most recently, pulsar searching has adapted deep learning algorithms due to the many unique signatures of a pulsar detection and the amount of available data. Here, we present the first stage of development for a novel statistically coherent based approach to identifying pulsars in radio data. Based on five measures and strict cutoff values only, our method has been shown to have a recall of 0.8605, failure rate of 0.0017, and an F score of 0.8370 when tested on the HTRU Medlat Data Set. These values are competitive with those of several early neural networks built and tested on the same set.