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Wearable technologies that store, monitor and analyse a range of biosignals are an area of significant growth and interest for both industry and academia. The rate of data generation in these devices poses a considerable challenge with regards to the bandwidths of wireless transmission protocols, local storage capacities and the on-board power consumption requirements. This issue is particularly acute for frequency-rich biosignals containing significant higher frequency components that are un-served by conventional compression techniques. This paper proposes a low-complexity predictor, based on a low-order infinite impulse response bandpass filter, to accurately predict such biosignals for use in lossless compression. Experimental evaluation of the method demonstrates that it outperforms conventional predictors with an average 25 % reduction in predictor residual standard deviation. The predictor described here enables high-bandwidth wearable sensors that can be employed in systems with reduced power consumption for transmission, storage and compression leading to considerable improvements in user experience by reducing device mass and increasing battery life.