A Feasibility Study on Improving Emotion Recognition from ECG Signals and HRV Features Through Baseline Clusterization

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Reference:

Sajno, E., Rossi, A., Pappalardo, L., Dagostin, F., & Riva, G. (2024). A Feasibility Study on Improving Emotion Recognition from ECG Signals and HRV Features Through Baseline Clusterization (No. 13098). EasyChair.

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Electrocardiography (ECG) has the potential for bringing Affective Computing outside laboratories, thanks to the spread of wearable and inexpensive instrumentation. Nevertheless, intra individual variability could influence Machine Learning (ML) models’ accuracy. To assess this issue, we propose to group the participants according to their general cardiovascular status, through the clusterization of HRV baseline features. A specific ML model aimed at classifying emotional responses was developed for each baseline cluster. This processing will lead to cardiac-state specific classification models to mitigate ML performance issues. We experimented this data analytics framework on the Mahnob HCI database containing ECG paired with emotional self-report assessment. Baseline data was clustered using k-means, dividing the dataset into two parts. Successively, classification models were separately applied to each group to predict arousal, valence, and dominance levels from ECG features. Classifiers applied after clustering outperformed those without clustering, reaching higher scores and lower randomness. Clustering ECG baselines to create individualized classifiers may alleviate intra-individual variability and improve emotion recognition performance, making affective computing more applicable.

XAI in Affective Computing: a Preliminary Study

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Reference:

Sajno, E., Rossi, A., De Gaspari, S., Sansoni, M., Brizzi, G., & Giuseppe, R. I. V. A. (2023). XAI in Affective Computing: a Preliminary Study. ANNUAL REVIEW OF CYBERTHERAPY AND TELEMEDICINE 2023, 40. 

Affective computing is a rapidly growing field that aims to understand human emotions through Artificial Intelligence. One of the most promising ways to achieve this goal is the use of physiological data (e.g. electrocardiogram - ECG) and Machine Learning (ML) algorithms to classify affective states. ECG correlates, such as Heart Rate Variability (HRV) and its features, are reported as viable indicators in both dimensional approaches, especially for valence, and in detecting discrete emotions. In this preliminary study, we used the ECG data from the open-source HCI Tagging Database, which includes physiological data and self-referred feedback from 30 subjects who watched videos designed to elicit different emotions.

The subjects evaluated their reactions using a three-dimensional affective space defined by arousal, valence, and dominance levels and reported the emotions they felt. To classify the affective states, we trained and tested different classification algorithms on the HRV features, using as labels, each self-reported feedback (i.e., valence, arousal, dominance, and emotions). The results showed that HRV features, when combined with normalization methods and ML algorithms, were effective in recognizing emotions as experienced by individuals. In particular, the study showed

that Decision Tree was the best-performing algorithm for predicting emotions based on HRV data. Additionally, an Explainable AI (XAI) model provided insights into the weight of these features in the ML discrimination phases. Overall, the study highlights the potential of HRV as a valid and unobtrusive source for detecting emotional states.

Machine learning in biosignals processing for mental health: A narrative review

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Reference:

Sajno, E., Bartolotta, S., Tuena, C., Cipresso, P., Pedroli, E., & Riva, G. (2023). Machine learning in biosignals processing for mental health: A narrative review. Frontiers in Psychology, 13, 1066317.

Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain–computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.