Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models.

Stress is an increasingly prevalent mental health condition across the world. In Europe, for example, stress is considered one of the most common health problems, and over USD 300 billion are spent on stress treatments annually. Therefore, monitoring, identification and prevention of stress are of the utmost importance.

Comparison of Stress Detection through ECG and PPG signals using a Random Forest-based Algorithm.

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).

Preprocessing Methods for Ambulatory HRV Analysis Based on HRV Distribution, Variability and Characteristics (DVC).

Thanks to wearable devices joint with AI algorithms, it is possible to record and analyse physiological parameters such as heart rate variability (HRV) in ambulatory environments. The main downside to such setups is the bad quality of recorded data due to movement, noises, and data losses. These errors may considerably alter HRV analysis and should therefore be addressed beforehand, especially if used for medical diagnosis.