Key result
A deep convolutional neural network using time and frequency domain features from ECG signals achieved an emotion recognition accuracy of 89% to 93% for detecting happy, sad, and fear states.
Why the study?
Healthcare systems often prioritize physical aspects over emotional aspects, making psychological and expressive healthcare imperative to improve quality of life.
A deep learning approach using ECG-derived heart rate variability features and Tabu Search for feature reduction achieved 89-93% accuracy in detecting happy, sad, and fearful emotional states.
Answers are grounded in this paper and cite their sources.
May support ECG-based emotion monitoring in research; leaves open clinical validation and utility.
The design and implementation of intelligent space, global, and healthcare arrangements have developed very essential since they automatically monitor both the surroundings and the individuals in it to offer support and facilities. Furthermost offer additional provision for the physical aspects of people at the cost of emotional aspects. For that reason, providing psychological and expressive healthcare is also imperative to advance excellence of life. Sentiment recognition is important and advantageous in social computer and social machine communication presentations as emotions specify mental state and requirements. Physiological signals-based emotion identification is a significant area of study with a bright potential for applications. Multiple HRV catalogs, comprising time-domain (MEAN, SDNN, and RMSSD) and frequency-domain (LFn, HFn, and LF/HF) indices were derived using RR intermission (RRI) time sequences that were recovered from ECGs. The most effective combination of ECG mood characteristics is chosen for classification using the Tabu Exploration Procedure(Happy, Sad and Fear). In order to categorize the test data, a deep convolutional neural system is finally created.
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Anupama et al. (2023) studied Emotion Detection. Deep Convolutional Neural Network (DCNN) was evaluated on Emotion recognition accuracy. A deep convolutional neural network using time and frequency domain features from ECG signals achieved an emotion recognition accuracy of 89% to 93% for detecting happy, sad, and fear states.
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