Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning

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dc.contributor.authorSepulveda, Axel
dc.contributor.authorCastillo, Francisco
dc.contributor.authorPalma, Carlos
dc.contributor.authorRodriguez-Fernandez, Maria
dc.date.accessioned2025-01-20T22:19:02Z
dc.date.available2025-01-20T22:19:02Z
dc.date.issued2021
dc.description.abstractAffect detection combined with a system that dynamically responds to a person's emotional state allows an improved user experience with computers, systems, and environments and has a wide range of applications, including entertainment and health care. Previous studies on this topic have used a variety of machine learning algorithms and inputs such as audial, visual, or physiological signals. Recently, a lot of interest has been focused on the last, as speech or video recording is impractical for some applications. Therefore, there is a need to create Human-Computer Interface Systems capable of recognizing emotional states from noninvasive and nonintrusive physiological signals. Typically, the recognition task is carried out from electroencephalogram (EEG) signals, obtaining good accuracy. However, EEGs are difficult to register without interfering with daily activities, and recent studies have shown that it is possible to use electrocardiogram (ECG) signals for this purpose. This work improves the performance of emotion recognition from ECG signals using wavelet transform for signal analysis. Features of the ECG signal are extracted from the AMIGOS database using a wavelet scattering algorithm that allows obtaining features of the signal at different time scales, which are then used as inputs for different classifiers to evaluate their performance. The results show that the proposed algorithm for extracting features and classifying the signals obtains an accuracy of 88.8% in the valence dimension, 90.2% in arousal, and 95.3% in a two-dimensional classification, which is better than the performance reported in previous studies. This algorithm is expected to be useful for classifying emotions using wearable devices.
dc.fuente.origenWOS
dc.identifier.doi10.3390/app11114945
dc.identifier.eissn2076-3417
dc.identifier.urihttps://doi.org/10.3390/app11114945
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/94585
dc.identifier.wosidWOS:000659599000001
dc.issue.numero11
dc.language.isoen
dc.revistaApplied sciences-basel
dc.rightsacceso restringido
dc.subjectaffective computing
dc.subjectemotion recognition
dc.subjectfeature extraction
dc.subjectmachine learning
dc.subjectwavelet transform
dc.titleEmotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
dc.typeartículo
dc.volumen11
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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