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- W4220664530 abstract "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." @default.
- W4220664530 created "2022-04-03" @default.
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- W4220664530 date "2022-03-25" @default.
- W4220664530 modified "2023-10-18" @default.
- W4220664530 title "Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence— A Systematic Review" @default.
- W4220664530 cites W1470195681 @default.
- W4220664530 cites W1595810167 @default.
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- W4220664530 cites W1978445814 @default.
- W4220664530 cites W1980497570 @default.
- W4220664530 cites W2002055708 @default.
- W4220664530 cites W2008662928 @default.
- W4220664530 cites W2019312772 @default.
- W4220664530 cites W2026142948 @default.
- W4220664530 cites W2044153985 @default.
- W4220664530 cites W2060754050 @default.
- W4220664530 cites W2061687428 @default.
- W4220664530 cites W2075708150 @default.
- W4220664530 cites W2077697924 @default.
- W4220664530 cites W2078671978 @default.
- W4220664530 cites W2098476033 @default.
- W4220664530 cites W2107638293 @default.
- W4220664530 cites W2122098299 @default.
- W4220664530 cites W2139728537 @default.
- W4220664530 cites W2151432963 @default.
- W4220664530 cites W2164368909 @default.
- W4220664530 cites W2167557160 @default.
- W4220664530 cites W2173536757 @default.
- W4220664530 cites W2413053268 @default.
- W4220664530 cites W2521641300 @default.
- W4220664530 cites W2547146855 @default.
- W4220664530 cites W2560438049 @default.
- W4220664530 cites W2599124244 @default.
- W4220664530 cites W2731964405 @default.
- W4220664530 cites W2747506362 @default.
- W4220664530 cites W2755126542 @default.
- W4220664530 cites W2758660699 @default.
- W4220664530 cites W2778978785 @default.
- W4220664530 cites W2779812635 @default.
- W4220664530 cites W2785704959 @default.
- W4220664530 cites W2789346393 @default.
- W4220664530 cites W2789556792 @default.
- W4220664530 cites W2793993424 @default.
- W4220664530 cites W2802802541 @default.
- W4220664530 cites W2804436848 @default.
- W4220664530 cites W2807447523 @default.
- W4220664530 cites W2810418809 @default.
- W4220664530 cites W2810625036 @default.
- W4220664530 cites W2886543314 @default.
- W4220664530 cites W2890679134 @default.
- W4220664530 cites W2901027086 @default.
- W4220664530 cites W2907554860 @default.
- W4220664530 cites W2907756620 @default.
- W4220664530 cites W2911220936 @default.
- W4220664530 cites W2915893085 @default.
- W4220664530 cites W2919854899 @default.
- W4220664530 cites W2946526173 @default.
- W4220664530 cites W2947002526 @default.
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- W4220664530 cites W2978795816 @default.
- W4220664530 cites W2996715881 @default.
- W4220664530 cites W2997678843 @default.
- W4220664530 cites W3004330901 @default.
- W4220664530 cites W3005228027 @default.
- W4220664530 cites W3005396609 @default.
- W4220664530 cites W3007088653 @default.
- W4220664530 cites W3011425517 @default.
- W4220664530 cites W3011797309 @default.
- W4220664530 cites W3014487746 @default.
- W4220664530 cites W3022903699 @default.
- W4220664530 cites W3030021786 @default.
- W4220664530 cites W3033523981 @default.
- W4220664530 cites W3041624078 @default.
- W4220664530 cites W3087220290 @default.
- W4220664530 cites W3101469666 @default.
- W4220664530 cites W3110931335 @default.
- W4220664530 cites W3110946289 @default.
- W4220664530 cites W3125041301 @default.
- W4220664530 cites W3156576211 @default.
- W4220664530 cites W3172494099 @default.
- W4220664530 cites W3196070745 @default.
- W4220664530 cites W4206754294 @default.
- W4220664530 cites W4220664530 @default.
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- W4220664530 doi "https://doi.org/10.3390/s22072538" @default.
- W4220664530 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35408149" @default.
- W4220664530 hasPublicationYear "2022" @default.
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