A3 Vertaisarvioitu kirjan tai muun kokoomateoksen osa
ECG-based emotion recognition using CWT and deep learning
Tekijät: Tokmak, Fadime; Bulbul, Ayse Kosal; Qaisar, Saeed Mian; Subasi, Abdulhamit
Toimittaja: Subasi, Abdulhamit; Qaisar, Saeed Mian, Nisar, Humaira
Kustantaja: Elsevier
Julkaisuvuosi: 2025
Kokoomateoksen nimi: Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction
Tietokannassa oleva lehden nimi: Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction
Sarjan nimi: Artificial Intelligence Applications in Healthcare and Medicine
Aloitussivu: 227
Lopetussivu: 245
ISBN: 978-0-443-29150-0
eISBN: 978-0-443-29151-7
DOI: https://doi.org/10.1016/B978-0-443-29150-0.00014-7
Verkko-osoite: https://doi.org/10.1016/B978-0-443-29150-0.00014-7
Emotion recognition can enhance the Human-Machine Interaction (HMI) in several aspects such as an enriched personalization with intention-based adaptation and responsiveness. In this context, several valuable studies have been conducted by exploring the physiological signals. This chapter discusses the significance of assessing the Autonomic Nervous System (ANS) through physiological indicators like electrocardiogram (ECG), Galvanic Skin Response (GSR), Blood Pressure (BP), and respiration rates, with particular emphasis on ECG and GSR due to their insights into various pathological and psychophysiological conditions. While ECG provides detailed heart electrical activity information, GSR reflects ANS activity through sweat gland function. The simplicity, effectiveness, affordability, and noninvasiveness of these measures make them preferable, although automatic interpretation is crucial for accurately identifying patterns associated with specific mental and physiological states. This chapter aims to improve healthcare applications and human-computer interaction by investigating the possibilities of emotion recognition using AI algorithms applied to ECG and GSR signals. Machine learning and deep learning algorithms evaluate ECG and GSR data to categorize emotions. These techniques have shown promise in various fields, including affective computing, mental health assessment, and human-computer interaction. To validate their effectiveness in differentiating emotions for multiple applications, the chapter shows how to create an effective ecosystem for real-time emotion recognition from ECG and GSR signals using a blend of wavelet transform, convolutional neural networks, and transfer learning.
