A processor implemented method, comprising: obtaining a respiratory signal corresponding to one or more users , wherein the respiratory signal is obtained using one or more sensors at a first time interval within a controlled environment; sequentially performing a mean subtraction technique, a low pass filtering technique, and a baseline drift detection and removal technique on the respiratory signal to obtain a pre-processed respiratory signal for each of the one or more users , wherein the baseline drift detection and removal technique is applied on an output obtained upon applying the low filtering technique to filter baseline drift by (i) identifying one or more troughs in the output, wherein a spline is fitted through the identified one or more troughs and (ii) correcting time series data above the spline; applying a window approach technique on time series data of the pre-processed respiratory signal to extract one or more morphological and one or more statistical features , wherein the one or more statistical features are extracted from a higher order dynamics of the pre-processed respiratory signal for each of the one or more users; ranking, using a feature selection technique, the one or more extracted morphological and the one or more statistical features to obtain a set of unique combinational features for each of the one or more users ; adjusting the set of unique combinational features using Synthetic Minority Oversampling Technique (SMOTE) to obtain a set of synthetically balanced features; and classifying mental state of the one or more users as one of a normal state or an abnormal state by applying one or more classifiers on the set of synthetically balanced features obtained based on the set of unique combinational features . obtaining a respiratory signal corresponding to one or more users , wherein the respiratory signal is obtained using one or more sensors at a first time interval within a controlled environment; sequentially performing a mean subtraction technique, a low pass filtering technique, and a baseline drift detection and removal technique on the respiratory signal to obtain a pre-processed respiratory signal for each of the one or more users , wherein the baseline drift detection and removal technique is applied on an output obtained upon applying the low filtering technique to filter baseline drift by (i) identifying one or more troughs in the output, wherein a spline is fitted through the identified one or more troughs and (ii) correcting time series data above the spline; applying a window approach technique on time series data of the pre-processed respiratory signal to extract one or more morphological and one or more statistical features , wherein the one or more statistical features are extracted from a higher order dynamics of the pre-processed respiratory signal for each of the one or more users; ranking, using a feature selection technique, the one or more extracted morphological and the one or more statistical features to obtain a set of unique combinational features for each of the one or more users ; adjusting the set of unique combinational features using Synthetic Minority Oversampling Technique (SMOTE) to obtain a set of synthetically balanced features; and classifying mental state of the one or more users as one of a normal state or an abnormal state by applying one or more classifiers on the set of synthetically balanced features obtained based on the set of unique combinational features . <EOS>