Stress Classification from ECG Signals Using Vision Transformer

• URL: http://arxiv.org/abs/2603.26721v1
• Date: Thu, 19 Mar 2026 01:47:50 GMT
• Title: Stress Classification from ECG Signals Using Vision Transformer
• Abstract summary: We transform the raw ECG data into 2D spectrograms using short time Fourier transform (STFT)<n>We also perform experiments with 1D CNN and ResNet-18 (CNN model)<n> Experiments show that vision transformer handles the effect of intersubject variability much better than CNN-based models.
• Score: 3.1368611610608856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision Transformers have shown tremendous success in numerous computer vision applications; however, they have not been exploited for stress assessment using physiological signals such as Electrocardiogram (ECG). In order to get the maximum benefit from the vision transformer for multilevel stress assessment, in this paper, we transform the raw ECG data into 2D spectrograms using short time Fourier transform (STFT). These spectrograms are divided into patches for feeding to the transformer encoder. We also perform experiments with 1D CNN and ResNet-18 (CNN model). We perform leave-onesubject-out cross validation (LOSOCV) experiments on WESAD and Ryerson Multimedia Lab (RML) dataset. One of the biggest challenges of LOSOCV based experiments is to tackle the problem of intersubject variability. In this research, we address the issue of intersubject variability and show our success using 2D spectrograms and the attention mechanism of transformer. Experiments show that vision transformer handles the effect of intersubject variability much better than CNN-based models and beats all previous state-of-the-art methods by a considerable margin. Moreover, our method is end-to-end, does not require handcrafted features, and can learn robust representations. The proposed method achieved 71.01% and 76.7% accuracies with RML dataset and WESAD dataset respectively for three class classification and 88.3% for binary classification on WESAD.

Related papers

• Pose Matters: Evaluating Vision Transformers and CNNs for Human Action Recognition on Small COCO Subsets [0.0]
  This study explores human recognition using a three-class subset of the COCO image corpus.<n>The binary Vision Transformer (ViT) achieved 90% mean test accuracy.
  arXiv  Detail & Related papers  (2025-06-13T11:16:50Z)
• FactoFormer: Factorized Hyperspectral Transformers with Self-Supervised Pretraining [36.44039681893334]
  Hyperspectral images (HSIs) contain rich spectral and spatial information. Current state-of-the-art hyperspectral transformers only tokenize the input HSI sample along the spectral dimension. We propose a novel factorized spectral-spatial transformer that incorporates factorized self-supervised pretraining procedures.
  arXiv  Detail & Related papers  (2023-09-18T02:05:52Z)
• Masked Transformer for Electrocardiogram Classification [7.229662895786343]
  Masked Transformer for ECG classification (MTECG) is a simple yet effective method which significantly outperforms recent state-of-the-art algorithms in ECG classification. We construct the Fuwai dataset comprising 220,251 ECG recordings with a broad range of diagnoses, annotated by medical experts.
  arXiv  Detail & Related papers  (2023-08-31T09:21:23Z)
• More for Less: Compact Convolutional Transformers Enable Robust Medical Image Classification with Limited Data [0.0]
  This study investigates the efficacy of Compact Convolutional Transformers for robust medical image classification with limited data. A hybrid of transformers and convolutional layers, CCTs demonstrate high accuracy on modestly sized datasets.
  arXiv  Detail & Related papers  (2023-07-01T03:31:30Z)
• Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs [55.78588835407174]
  Vision Transformers (ViTs) have not been applied to this task despite their high classification performance on generic images. ViTs do not rely on convolutions but on patch-based self-attention and in contrast to CNNs, no prior knowledge of local connectivity is present. Our results show that while the performance between ViTs and CNNs is on par with a small benefit for ViTs, DeiTs outperform the former if a reasonably large data set is available for training.
  arXiv  Detail & Related papers  (2022-08-17T09:07:45Z)
• Iwin: Human-Object Interaction Detection via Transformer with Irregular Windows [57.00864538284686]
  Iwin Transformer is a hierarchical Transformer which progressively performs token representation learning and token agglomeration within irregular windows. The effectiveness and efficiency of Iwin Transformer are verified on the two standard HOI detection benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-20T12:04:50Z)
• SepTr: Separable Transformer for Audio Spectrogram Processing [74.41172054754928]
  We propose a new vision transformer architecture called Separable Transformer (SepTr) SepTr employs two transformer blocks in a sequential manner, the first attending to tokens within the same frequency bin, and the second attending to tokens within the same time interval. We conduct experiments on three benchmark data sets, showing that our architecture outperforms conventional vision transformers and other state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-17T19:48:43Z)
• Towards Data-Efficient Detection Transformers [77.43470797296906]
  We show most detection transformers suffer from significant performance drops on small-size datasets. We empirically analyze the factors that affect data efficiency, through a step-by-step transition from a data-efficient RCNN variant to the representative DETR. We introduce a simple yet effective label augmentation method to provide richer supervision and improve data efficiency.
  arXiv  Detail & Related papers  (2022-03-17T17:56:34Z)
• ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond [76.35955924137986]
  We propose a Vision Transformer Advanced by Exploring intrinsic IB from convolutions, i.e., ViTAE. ViTAE has several spatial pyramid reduction modules to downsample and embed the input image into tokens with rich multi-scale context. We obtain the state-of-the-art classification performance, i.e., 88.5% Top-1 classification accuracy on ImageNet validation set and the best 91.2% Top-1 accuracy on ImageNet real validation set.
  arXiv  Detail & Related papers  (2022-02-21T10:40:05Z)
• A Transformer Architecture for Stress Detection from ECG [7.559720049837459]
  We present a deep neural network based on convolutional layers and a transformer mechanism to detect stress using ECG signals. Our experiments show that the proposed model achieves strong results, comparable or better than the state-of-the-art models for ECG-based stress detection.
  arXiv  Detail & Related papers  (2021-08-22T14:34:44Z)
• Vision Transformers are Robust Learners [65.91359312429147]
  We study the robustness of the Vision Transformer (ViT) against common corruptions and perturbations, distribution shifts, and natural adversarial examples. We present analyses that provide both quantitative and qualitative indications to explain why ViTs are indeed more robust learners.
  arXiv  Detail & Related papers  (2021-05-17T02:39:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.