  Cardiovascular activities are directly related to the response of a body in a
stressed condition. Stress, based on its intensity, can be divided into two
types i.e. Acute stress (short-term stress) and Chronic stress (long-term
stress). Repeated acute stress and continuous chronic stress may play a vital
role in inflammation in the circulatory system and thus leads to a heart attack
or to a stroke. In this study, we have reviewed commonly used machine learning
classification techniques applied to different stress-indicating parameters
used in stress monitoring devices. These parameters include Photoplethysmograph
(PPG), Electrocardiographs (ECG), Electromyograph (EMG), Galvanic Skin Response
(GSR), Heart Rate Variation (HRV), skin temperature, respiratory rate,
Electroencephalograph (EEG) and salivary cortisol, used in stress monitoring
devices. This study also provides a discussion on choosing a classifier, which
depends upon a number of factors other than accuracy, like the number of
subjects involved in an experiment, type of signals processing and
computational limitations.
