Personality Trait Recognition using ECG Spectrograms and Deep Learning (2402.04326v1)

Abstract: This paper presents an innovative approach to recognizing personality traits using deep learning (DL) methods applied to electrocardiogram (ECG) signals. Within the framework of detecting the big five personality traits model encompassing extra-version, neuroticism, agreeableness, conscientiousness, and openness, the research explores the potential of ECG-derived spectrograms as informative features. Optimal window sizes for spectrogram generation are determined, and a convolutional neural network (CNN), specifically Resnet-18, and visual transformer (ViT) are employed for feature extraction and personality trait classification. The study utilizes the publicly available ASCERTAIN dataset, which comprises various physiological signals, including ECG recordings, collected from 58 participants during the presentation of video stimuli categorized by valence and arousal levels. The outcomes of this study demonstrate noteworthy performance in personality trait classification, consistently achieving F1-scores exceeding 0.9 across different window sizes and personality traits. These results emphasize the viability of ECG signal spectrograms as a valuable modality for personality trait recognition, with Resnet-18 exhibiting effectiveness in discerning distinct personality traits.

• The paper introduces a novel method that transforms ECG signals into spectrograms to recognize personality traits using deep learning.
• It employs Resnet-18 and visual transformer models, with Resnet-18 achieving F1-scores above 0.9, outperforming traditional approaches.
• The study highlights the potential of integrating physiological signals with advanced deep learning for objective personality assessment.

ECG Spectrograms and Deep Learning: A New Frontier in Personality Trait Recognition

Introduction

In the broad and intricate domain of personality research, the quest for more precise and non-biased methods of personality trait recognition continues to gain momentum. The exploration of physiological signals, particularly ECG (electrocardiogram) signals, presents a promising frontier in this field. ECG, a measure of the heart's electrical activity, has been investigated for its potential to reveal insights into personality traits. The breakthrough paper utilizes ECG-derived spectrograms, applying advanced deep learning (DL) techniques, notably Resnet-18 and visual transformer (ViT) models, to classify the big five personality traits: extraversion, neuroticism, agreeableness, conscientiousness, and openness, with remarkable accuracy.

Methodology Overview

The methodology centers on the transformation of ECG signals into spectrograms—visual representations capturing the signals' frequency and time information. This transformation is critical for leveraging the spatial pattern recognition prowess of convolutional neural networks (CNNs) and ViTs, enabling the accurate classification of personality traits based on physiological data. The paper's primary dataset, ASCERTAIN, comprises ECG signals collected from participants while they were exposed to video stimuli, making it a rich resource for analyzing the link between physiological responses and personality traits. By applying meticulous windowing strategies and leveraging the capabilities of pre-trained DL models, the research achieves a high degree of classification accuracy.

Experimental Insights

The paper's empirical results underscore the efficacy of ECG spectrograms when combined with deep learning models in recognizing personality traits. The usage of Resnet-18 model, in particular, demonstrated superior performance, with F1-scores exceeding 0.9 across all personality traits analyzed. This indicates a strong potential for ECG spectrograms as a reliable and accurate modality in personality trait recognition tasks. Additionally, the paper highlights the comparative advantage of Resnet-18 over the ViT model, attributing the former's success to its convolutional architecture that might be more adept at capturing the nuanced patterns present in ECG spectrograms.

Comparative Analysis

A notable aspect of this research is the comparative analysis conducted against traditional personality recognition methods, such as Support Vector Machines (SVM) and Naive Bays, utilizing the same dataset. The findings reveal a significant margin of improvement in classification accuracy, emphasizing the advanced capability of DL models in handling complex, high-dimensional data like ECG spectrograms. This comparison not only validates the efficacy of the proposed method but also sets a new benchmark for future studies in the field.

Conclusion and Future Directions

The paper presents a significant advance in the domain of personality research, offering a novel, objective, and data-rich approach to personality trait recognition using ECG spectrograms and deep learning models. While the results are highly promising, the research opens several avenues for future exploration, including the potential application of self-supervised learning for ViT models to further refine and enhance classification accuracy. Moreover, the paper's success encourages the broader adoption of physiological signals in psychological assessment, paving the way for more personalized and accurate understandings of human personality and behavior.

Considering the practical and theoretical implications, this research not only contributes an innovative methodology to the field of psychology but also emboldens the interdisciplinary collaboration between artificial intelligence and psychological sciences to explore the uncharted territories of the human mind.