High-frequency physiological data poses unique challenges for signal processing and computational analysis. This dataset was obtained from a cohort of Macaca mulatta and Macaca fascicularis infected with Plasmodium knowlesi using a customized telemetry system. The data set is comprised of electrocardiogram signals collected at a sampling rate of 1 kHz, temperature measures collected at 1 Hz, accelerometer data collected at 10 Hz, and blood pressure readings collected at 500 Hz. The implanted telemetry devices allowed nearly uninterrupted time series data acquisition over the course of an infection experiment. Machine learning algorithms using daily features were used to accurately classify a day of telemetry data between pre-infection and liver stage over 90% of the time. The experimental design, analysis, and framework used to address challenges in multi-scale telemetry signal processing will be discussed.