Stress has been classified as the health epidemic of the 21st century with an increasingly active research interest within the fields of psychology, neuroscience, medicine, and more recently affective computing. At present, stress is identified through cortisol levels in saliva but there is no unanimously accepted standard for continuous stress evaluation. With recent development in wearable sensors, many scientists are interested in stress identification through physiological signals such as the Heart rate variability (HRV). In this paper, we present a supervised machine learning-based algorithm to detect stress from HRV derived from electrocardiograms (ECG) as well as photoplethysmograms (PPG), as a low cost alternative to ECG. HRV features from ECG and PPG signals of 46 healthy subjects were analysed and used to separately train and test a subject-independent Random Forest algorithm. In both datasets, stress was accurately identified with more than 80% F1-score and 90% AUC. Results show that PPG is a good surrogate to ECG for HRV analysis and stress detection. The proposed algorithm has the potential to assist researchers and clinicians in the automated continuous analysis of stress.