A method for non-apnea sleep arousal detection, the method comprising a processor implemented steps of: obtaining a plurality of physiological signals from a plurality of subjects to generate a training data set; segmenting the plurality of physiological signals into instances of a fixed duration; pre-processing the obtained instances of the plurality of physiological signals for removing noise, artefacts and sensor interference; extracting a plurality of features from each instance of the pre-processed plurality of physiological signals; ranking the plurality of features using a minimum redundancy maximum relevancy (mRMR) algorithm; selecting a first set of features out of the plurality of features, wherein the first set of features is a fixed number of top ranked features out of the plurality of features; training a plurality of random forest classifier models using the selected first set of features, wherein each random forest model is trained with balanced instances of non-arousal class and non-apnea arousal class, wherein the non-apnea arousal class is the minority class and all of its instances are used in training of each of the plurality of classifiers and the non-arousal class is the majority class and distinct random instances of this class are used in training of the plurality of classifiers; obtaining the plurality of physiological signals from a test subject to generate a test sample; segmenting the plurality of physiological signals of the test sample into instances of the same duration as training dataset; pre-processing the instances of the plurality of physiological signals of the test sample for removing noise, artefacts and sensor interference; extracting the selected first set of features from the instances of the pre-processed plurality of physiological signals; providing the extracted first set of features of the instances of the test sample to the plurality of trained random forest classifier models for the detection of non-apnea sleep arousal; generating a prediction probability by each of the plurality of classifiers for each instance of the test sample; calculating a mean of the generated prediction probabilities of the plurality of classifiers for each instance to get a single prediction probability value for every particular instance of the test sample; stitching the prediction probabilities of all the instances of the test sample to generate a time series of the prediction probability values; and smoothening the stitched prediction probability values to get rid of spurious detections, wherein the smoothened prediction probability values time series is used as the probabilities of non-apnea sleep arousal in the test sample. obtaining a plurality of physiological signals from a plurality of subjects to generate a training data set; segmenting the plurality of physiological signals into instances of a fixed duration; pre-processing the obtained instances of the plurality of physiological signals for removing noise, artefacts and sensor interference; extracting a plurality of features from each instance of the pre-processed plurality of physiological signals; ranking the plurality of features using a minimum redundancy maximum relevancy (mRMR) algorithm; selecting a first set of features out of the plurality of features, wherein the first set of features is a fixed number of top ranked features out of the plurality of features; training a plurality of random forest classifier models using the selected first set of features, wherein each random forest model is trained with balanced instances of non-arousal class and non-apnea arousal class, wherein the non-apnea arousal class is the minority class and all of its instances are used in training of each of the plurality of classifiers and the non-arousal class is the majority class and distinct random instances of this class are used in training of the plurality of classifiers; obtaining the plurality of physiological signals from a test subject to generate a test sample; segmenting the plurality of physiological signals of the test sample into instances of the same duration as training dataset; pre-processing the instances of the plurality of physiological signals of the test sample for removing noise, artefacts and sensor interference; extracting the selected first set of features from the instances of the pre-processed plurality of physiological signals; providing the extracted first set of features of the instances of the test sample to the plurality of trained random forest classifier models for the detection of non-apnea sleep arousal; generating a prediction probability by each of the plurality of classifiers for each instance of the test sample; calculating a mean of the generated prediction probabilities of the plurality of classifiers for each instance to get a single prediction probability value for every particular instance of the test sample; stitching the prediction probabilities of all the instances of the test sample to generate a time series of the prediction probability values; and smoothening the stitched prediction probability values to get rid of spurious detections, wherein the smoothened prediction probability values time series is used as the probabilities of non-apnea sleep arousal in the test sample.