Electronic Journal of Biology, 2016, Vol.12(2): 156-160 ISSN 1860-3122 - 156 - ECG Feature Extraction for Stress Recognition in Automobile Drivers Supriya Goel * , Pradeep Tomar, Gurjit Kaur School of ICT, Gautam Buddha University, India. *Corresponding author. Tel: 8750924100; E-mail: goel.supriya03@gmail.com Citation: Goel S, Tomar P, Kaur G, ECG Feature Extraction for Stress Recognition in Automobile Drivers. Electronic J Biol, 12:2 Received: February 23, 2016; Accepted: March 17, 2016; Published: March 24, 2016 Abstract Mental stress is one of the well-known major risk factors for many diseases such as Hypertension, Coronary Artery Disease, and Heart Attack. This research paper places the emphasis on ECG signal processing for recognition of stress. Feature extraction of ECG is done with the help of lab view and then those features which are affected are classified. Results shown that ST wave, QRS wave, T wave and isoelectric level of ECG gets affected. Keywords: BVP; ECG; EEG; GSR; HR; HRV; MA; PLI. 1. Introduction Biomedical signal processing is an important visualization and interpretation method that helps in the extraction of vital information from biomedical signals. Various methods have been proposed by considering the ways in which these components interact to recognize stress, but at the moment there isn’t any single formulation that is universally acceptable for all components. Recognition and modeling of stress with every component is a very challenging problem that has drawn a great deal of interest from the emerging field of Human- Computer Interaction (HCI). In the real world, stress recognition through HCI has vast application in the field of medicine, entertainment, education, safety etc. [1,2]. Researchers and practitioners use a large variety of signals like facial expressions, gestures, vocal traits which collectively known as behavioral modalities and physiological signals like ECG, Electroencephalograph (EEG), Galvanic Skin Response (GSR), Electromyogram (EMG) Blood Volume Pressure (BVP), Heart Rate (HR), Heart Rate Variability (HRV), Temperature (T) and Respiration Rate. A major drawback of, using behavioral modalities for emotion recognition is the uncertainty that arises in the case of individuals who either are consciously regulating their emotional manifestations or are naturally suppressive. For instance, although facial expressions can be analyzed to determine emotions, there is no guarantee that an individual will express the corresponding condition, irrespective of whether they are experiencing a certain emotion. This has serious implications in some applications such as surveillance [3]. An interesting alternative in place of behavioral modalities is the physiological signals (or bio signals) which constitute vital signs of the human body and the benefits of detecting emotions by using physiological signals which are involuntary reactions of the body and these involuntary reactions are very difficult to hide. These signals have traditionally been used for clinical diagnosis, because according to past research these signals have significant evidence to suggest that they are sensitive to and may convey information about emotional states [1-6]. These Physiological signals are captured through sensors which help in conveying the information about stress. This work considers heart as main part of the body to detect the physiological signal for stress recognition as it is affected by the emotions. So it is important to study the impact of stress recognition on the heart as the pattern of ECG signal changes during positive or negative emotion [7,8]. The human heart weighs 250-350 grams and is approximately equal to the size of the fist. It is located anterior to the vertebral column and posterior to the sternum The Heart is divided into separate right and left sections by the inter-ventricular septum. Each of these (right and left) sections is again divided into upper and lower compartments known as atria and ventricles respectively. The atria are the receiving chambers and the ventricles are the discharging chambers as shown in the Figure 1. The Tricuspid valve separates the right atrium from the right ventricle. The Mitral (also known as the Bicuspid) valve separates the left atrium from the left ventricle. Oxygen-poor blood from the whole body is received into the right atrium through large veins called the superior and inferior vena cava and flows [9-14]. The right ventricle then pumps the blood to the lungs where the blood is oxygenated. Similarly, the left Review Article