Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence- A Systematic Review

Simple Summary We reviewed the literature on the publicly available datasets used to automatically recognise emotion and affect using artificial intelligence (AI) techniques. We were particularly interested in databases with cardiovascular (CV) data. Additionally, we assessed the quality of the included papers. We searched the sources until 31 August 2020. Each step of identification was carried out independently by two reviewers to maintain the credibility of our review. In case of disagreement, we discussed them. Each action was first planned and described in a protocol that we posted on the Open Science Framework (OSF) platform. We selected 18 works focused on providing datasets of CV signals for automated affect and emotion recognition. In total, data for 812 participants aged 17 to 47 were analysed. The most frequently recorded signal was electrocardiography. The authors most often used video stimulation. Noticeably, we did not find much necessary information in many of the works, resulting in mainly low quality among included papers. Researchers in this field should focus more on how they carry out experiments. Our review aimed to assess the current state and quality of publicly available datasets used for automated affect and emotion recognition (AAER) with artificial intelligence (AI), and emphasising cardiovascular (CV) signals. The quality of such datasets is essential to create replicable systems for future work to grow. We investigated nine sources up to 31 August 2020, using a developed search strategy, including studies considering the use of AI in AAER based on CV signals. Two independent reviewers performed the screening of identified records, full-text assessment, data extraction, and credibility. All discrepancies were resolved by discussion. We descriptively synthesised the results and assessed their credibility. The protocol was registered on the Open Science Framework (OSF) platform. Eighteen records out of 195 were selected from 4649 records, focusing on datasets containing CV signals for AAER. Included papers analysed and shared data of 812 participants aged 17 to 47. Electrocardiography was the most explored signal (83.33% of datasets). Authors utilised video stimulation most frequently (52.38% of experiments). Despite these results, much information was not reported by researchers. The quality of the analysed papers was mainly low. Researchers in the field should concentrate more on methodology.