This paper presents an overview of recent methods for recognition of human emotions based on Electrocardiogram (ECG) signals and related applications. The major challenges in emotion modeling (affective computing) from ECG data are finding representations that are invariant to inter- and intra-subject differences, as well as the inherent noise associated with the ECG data recordings. The most common invariant features (in frequency and time domain) extracted from the raw ECG signals are outlined. The reviewed studies reveal the great potential of ECG to decode basic human emotional states such as joy, sadness, anger, fear in combination with other physiological signals and facial expression. Major application areas cover patient monitoring, marketing, car driving.
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