The presented work approaches the topic of method development for automated feature extraction and ECG analysis. The MATLAB-based toolbox ecgFEAT (ECG Feature Extraction and Analysis Toolbox) includes a comprehensive set of algorithms to extract temporal, morphological and statistical information for time analysis and heart rate variability (HRV) analysis. The implemented feature extraction concept provides optimized results that are embedded within a graphical interface for the representation of the performed analysis. An evaluation of the implemented algorithms was carried out using 20 subject-data from an experimental setup and could demonstrate the toolbox functionality. The ecgFEAT toolbox delivers important information for the study of the cardiac physiology and covers considerable potential for automated identification of cardiovascular reactivity to stress and emotional states.