Wireless wearable devices enable convenient acquisition of photoplethysmograph signals. A limitation of continual measurement is the resource-constrained processing and limited battery capacity. To solve the transmission power consumption problem, we propose an information reduction method, AutoPPGEncoder. The AutoPPGEncoder is an autoencoder trained with conventional mean squared error (mse) loss and mse systolic peak amplitude loss. The scheduling of impact from two losses improves the raw signal reconstruction by preserving the systolic peak in the reconstructed signal. Our system reduces the amount of transmitted BLE data from the PPG data by a ratio of 1:7.07. In terms of signal quality, the AutoPPGEncoder performs better than the recent compression technique, deep compressive sensing network (DCSNET) compression in both percentage root-mean-square difference (PRD) and peak amplitude PRD on BIDMC, SensAI, and VitalDB datasets. The AutoPPGEncoder’s power consumption was verified on an NRF54L15-DK board, which reduced the total energy consumption by 0.4 mJ per segment. This demonstrated the capability of the AutoPPGEncoder in IoT applications with power efficiency and the quality of PPG signal transmission.