Personality Trait Recognition using ECG Spectrograms and Deep Learning
Muhammad Mohsin Altaf Saadat Ullah Khan Muhammad Majd Syed Muhammad Anwar
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.