This paper presents a machine learning classification processor for accurate DoA estimation irrespective of the patient's age and anesthetic drug. The classification is solely based on six features extracted from EEG signal, i.e., spectral edge frequency, beta ratio, and four bands of spectral energy. A machine learning fine decision tree classifier is adopted to achieve a four-class DoA classification. The proposed DoA processor is implemented using a 65 nm CMOS technology. The processor achieves an average accuracy of 92.2% for all DoA states, with a latency of 1s The 0.09 mm 2 DoA processor consumes 140nJ/classification.