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.

翻译：本文提出了一种创新方法，利用深度学习技术对心电图信号进行分析，以实现人格特质的识别。研究基于大五人格特质模型（涵盖外向性、神经质、宜人性、尽责性和开放性）的检测框架，探索了ECG谱图作为信息特征的有效性。通过确定谱图生成的最优窗口尺寸，采用卷积神经网络（具体为Resnet-18）和视觉变换器进行特征提取与人格特质分类。本研究使用了公开的ASCERTAIN数据集，该数据集包含58名受试者在观看按效价和唤醒度分类的视频刺激时采集的多种生理信号（包括ECG记录）。研究结果表明，人格特质分类取得了显著性能，不同窗口尺寸和人格特质下的F1分数均稳定超过0.9。这些结果验证了ECG谱图作为人格特质识别有效模态的可行性，其中Resnet-18在区分不同人格特质方面展现出优异效果。