Wearable sensors enable remote, continuous patient monitoring at home, offering a promising approach for early detection of postoperative complications. However, analyzing continuous long-term physiological data remains challenging, particularly in the absence of precisely labeled deterioration events. Unsupervised change point detection methods can address this issue by identifying physiological deviations without requiring predefined event labels. This study investigates the feasibility of using a Long-Short-Term Memory (LSTM) autoencoder for detecting postoperative complications from continuous heart rate and respiration rate data using a wearable patch sensor while monitoring patients in their homes. The autoencoder was applied to identify physiological deviations that may indicate potential complications after major abdominal oncological surgeries in ten patients. The model was trained on data from seven patients to recognize deviations from normal physiological patterns and evaluated on three patients. The proposed model detected change points preceding the clinically documented complication time in two test patients, identifying these deteriorations an average of 3.25 hours earlier than the standard Remote Early Warning Score (REWS) alarm system. These findings suggest that LSTM autoencoder-based change point detection could be a valuable tool for identifying postoperative complications early in remote patient monitoring settings, to support timely intervention and potentially improving patient outcomes.