Assessing autonomic function using heart rate variability (HRV) in critically ill patients can provide valuable information about prognosis and treatment options. We implemented a novel convolutional neural network (CNN) algorithm to categorize the status of the autonomic nervous system (ANS) into three classes: basal state (BS) without any system dominance, sympathetic nervous system dominance state (SDS), and parasympathetic nervous system dominance state (PDS). For the BS class, we achieved an accuracy of 89%, a sensitivity of 92%, and a specificity of 89%. For the SDS class, we obtained an accuracy of 98%, a sensitivity of 95%, and a specificity of 98%. And for the PDS class, an accuracy of 87%, a sensitivity of 83%, and a specificity of 91% was obtained. We also developed a model to differentiate ANS activity between survivors and deceased during the first 24 h of hospitalization, which was evaluated using data from the MIMIC-III database. Results showed that in the deceased group the PDS decreased by 9.70% (p < 0.05) in contrast to a 8.02% increase in the SDS class (p < 0.05). This work provides a promising tool for implementation in the ICU, complementing fast and reliable decision-making by physicians during hospitalization.