This paper proposes a novel approach for accurate DoA estimation based on Stationary Wavelet Transform (SWT) spectral features while using Multilayer Perceptron (MLP) Regressor Network for the prediction of DoA index. The classifier utilizes an optimized temporal, fractal, and spectral feature set to identify the patient\'s conscious level irrespective of age and the type of anesthetic agent. The proposed algorithm is validated on 95 patients (age 5 months-67 years), (weight: 6 - 90 Kg). The anesthetic agents used in this study include Propofol, Sevoflurane, Isoflurane, Fentanyl, Ketamine, and Caudal. The proposed DoA regressor outperforms the state-of-the-art DoA prediction algorithms by predicting highly accurate DoA indexes with an overall Mean Absolute Error (MAE) of 0.014 and Mean Squared Error (MSE) of 0.02 while utilizing minimized feature set and a deep learning-based MLP regression network. The minimized feature set allows efficient implementation on-chip.