What is the clinical evidence from this study?

Study design: Other. Population: Emotion recognition / Post-Traumatic Stress Disorder (PTSD). Intervention: Hybrid CNN-LSTM with ResNet-152 model vs. SVM and ANN. Primary outcome: Classification accuracy.

What does this research mean for the field?

A hybrid CNN-LSTM with ResNet-152 deep learning algorithm achieves 98% accuracy in EEG-based emotion recognition associated with Post-Traumatic Stress Disorder (PTSD). Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

October 7, 2022Open Access

EEG-based emotion recognition using hybrid CNN and LSTM classification

Key Result

The proposed hybrid CNN-LSTM with ResNet-152 deep learning algorithm achieved 98% accuracy in EEG-based emotion recognition linked to PTSD.

Structured PICO

Population

EEG signals for emotion analysis, with a focus on human behavior and Post-Traumatic Stress Disorder (PTSD)

Intervention

Automated CNN-LSTM with ResNet-152 algorithm

Comparator

Existing techniques

Outcome

Accuracy of emotion recognition

An automated CNN-LSTM with ResNet-152 algorithm achieved 98% accuracy for EEG-based emotion recognition, offering a potential tool for analyzing emotional responses in conditions like PTSD.

Limitations

Needs extension to include additional behavior/emotional disorders
Needs validation with various samples such as military personnel, IT industry people, teenagers, and college students

Abstract

Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as physical, behavioral, and mental changes. In the latest days, physiological activity has been used to study emotional reactions. This study describes the electroencephalography (EEG) signals, the brain wave pattern, and emotion analysis all of these are interrelated and based on the consequences of human behavior and Post-Traumatic Stress Disorder (PTSD). Post-traumatic stress disorder effects for long-term illness are associated with considerable suffering, impairment, and social/emotional impairment. PTSD is connected to subcortical responses to injury memories, thoughts, and emotions and alterations in brain circuitry. Predominantly EEG signals are the way of examining the electrical potential of the human feelings cum expression for every changing phenomenon that the individual faces. When going through literature there are some lacunae while analyzing emotions. There exist some reliability issues and also masking of real emotional behavior by the victims. Keeping this research gap and hindrance faced by the previous researchers the present study aims to fulfill the requirements, the efforts can be made to overcome this problem, and the proposed automated CNN-LSTM with ResNet-152 algorithm. Compared with the existing techniques, the proposed techniques achieved a higher level of accuracy of 98% by applying the hybrid deep learning algorithm.

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