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Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review.

Muhammad Anas Hasnul¹, Nor Azlina Ab Aziz¹, Salem Alelyani^2,3

¹Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia.

Sensors (Basel, Switzerland)

|August 10, 2021

Summary

This review explores electrocardiogram (ECG) use in emotion recognition systems for affective computing. It highlights high-accuracy architectures and discusses benefits for mental health and healthcare applications.

Keywords:

affective computing electrocardiogram (ECG)emotion recognition system healthcare

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Area of Science:

Affective computing and artificial intelligence
Biomedical signal processing
Human-computer interaction

Background:

Affective computing integrates human emotions with AI for enhanced systems.
Emotion recognition systems are crucial for affective computing applications.
Electrocardiograms (ECGs) offer a promising physiological signal for emotion detection.

Purpose of the Study:

To review research on emotion recognition using electrocardiograms (ECGs).
To analyze unimodal and multimodal approaches incorporating ECGs.
To identify high-accuracy architectures and available ECG-inclusive emotion databases.

Main Methods:

Systematic review of literature on ECG-based emotion recognition.
Critical analysis of data collection, pre-processing, feature extraction, and classification.
Review of validation techniques and available affective databases.

Main Results:

Identified architectures achieving over 90% accuracy in ECG-based emotion recognition.
Reviewed and analyzed the popularity of various ECG-inclusive affective databases.
Summarized the benefits of emotion recognition systems for healthcare.

Conclusions:

ECG-based emotion recognition is a viable approach within affective computing.
The review provides a foundation for future research and application development.
Significant potential exists for improving mental health and healthcare through these systems.