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The main aim of this paper is to design and identify stress using machine learning approach. Proper detection of stress can conveniently prevent many psychological as well as physiological problems such as cardiovascular sicknesses, arrhythmia, diabetes. In our project we will be using three important features from ECG signal to detect stress - QT interval, RR interval and EDR. Using machine learning the model is trained with a set of combinations of ECG features and different SVM types. The model which gives better accuracy is selected. This project will be helpful in biomedical applications if the required performance level is achieved. Until now, only HRV(heart rate variability) is used as the main feature to detect stress. We will be using three important features from ECG signal to detect stress-QT interval, RR interval, EDR. The outcome of this project will be to select the best model with greater accuracy using all the features.
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