Video games have evolved beyond their traditional role as forms of entertainment, becoming increasingly utilized as tools with diverse applications in health field. This study investigates cardiac variability by analyzing cardiac pulse signals to understand players’ emotional responses during gaming experience. For this purpose, 60 participants have been recruited and divided into two groups: video gamers (VG) and non-video gamers (NVG). Each participant is immersed in experience of playing “Super Mario World™” while their heartbeat is monitored continuously. A conditioning process is applied to the resulting heartbeat signals to extract relevant features, which are then used to train a multilayer perceptron (MLP) to classify participants as VG or NVG. Results revealed that final experimental setup achieved an F1-score of 76.6%, thus demonstrating effectiveness of this biosignal in differentiating between two groups. An adequate selection of features and optimization of neural network contributed significantly to enhanced classification performance. This study shows potential of biosignals, such as tachogram, as an objective and non-invasive tool for analyzing emotional responses during gaming experience.