OBJECTIVE: The present study proposes a cerebral infarction detection algorithm based on heart rate variability (HRV). METHODS: It has been reported that infarction affects HRV. Therefore, infarction could be detected at an acute stage by monitoring HRV. This study uses multivariate statistical process control (MSPC), which is a well-known anomaly monitoring method. HRV data shortly after infarction onsets are collected by using the middle cerebral artery occlusion (MCAO) model in rats. This study prepares 11 MCAO-operated rats and 11 sham-operated rats. Three sham-operated rats\' data are used for model construction of MSPC, and the other 19 rats\' data are used for its validation. RESULTS: The sensitivity and specificity of the proposed algorithm were 82 % and 75 %, respectively. CONCLUSION: An infarction onset could be detected at an acute stage by monitoring HRV.