Ultra low power wearable wireless patient monitoring systems will be critical for continuous remote monitoring of patients and fast transmission of data to medical personnel for timely intervention. This paper proposes a pulse based methodology for photoplethysmogram (PPG) feature discrimination using the time-based integrate and fire (IF) sampler for continuous, portable pulse oximetry. The analog PPG is transformed into a sequence of time events where the time between two events represents a constant area under the analog PPG signal, with injective mapping between the two domains. A simple and robust method is formulated that directly estimates the systolic peak time from the output of the IF sampler. The proposed processing method can be implemented in hardware using simple combinatorial logic and is comparable to performance of existing digital signal processing methods in the literature.