mlsanityData to 7 Oct 2026

TransConv-DDPM: Enhanced Diffusion Model for Generating Time-Series Data in Healthcare

arXiv 2602.07033 ↗First posted 3 Feb 2026cs.LGcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

The lack of real-world data in clinical fields poses a major obstacle in training effective AI models for diagnostic and preventive tools in medicine. Generative AI has shown promise in increasing data volume and enhancing model training, particularly in computer vision and natural language processing (NLP) domains. However, generating physiological time-series data, a common type in medical AI applications, presents unique challenges due to its inherent complexity and variability. This paper introduces TransConv-DDPM, an enhanced generative AI method for biomechanical and physiological time-series data generation. The model employs a denoising diffusion probabilistic model (DDPM) with U-Net, multi-scale convolution modules, and a transformer layer to capture both global and local temporal dependencies. We evaluated TransConv-DDPM on three diverse datasets, generating both long and short-sequence time-series data. Quantitative comparisons against state-of-the-art methods, TimeGAN and Diffusion-TS, using four performance metrics, demonstrated promising results, particularly on the SmartFallMM and EEG datasets, where it effectively captured the more gradual temporal change patterns between data points. Additionally, a utility test on the SmartFallMM dataset revealed that adding synthetic fall data generated by TransConv-DDPM improved predictive model performance, showing a 13.64% improvement in F1-score and a 14.93% increase in overall accuracy compared to the baseline model trained solely on fall data from the SmartFallMM dataset. These findings highlight the potential of TransConv-DDPM to generate high-quality synthetic data for real-world applications.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 18 of its 51 references that are in the map. First posted on arXiv 3 Feb 2026.

Builds on

18 of 51 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers24 Jul
  2. 2023Synthetic ECG Signal Generation using Probabilistic Diffusion Models4 Mar

Other references · 16

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2019An Introduction to Variational Autoencoderscited by 997
  4. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 225
  5. 2021TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generationcited by 139
  6. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
  7. 2017Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networkscited by 130
  8. 2020COT-GAN: Generating Sequential Data via Causal Optimal Transportcited by 70
  9. 2021PSA-GAN: Progressive Self Attention GANs for Synthetic Time Seriescited by 58
  10. 2023Causal Recurrent Variational Autoencoder for Medical Time Series Generationcited by 44
  11. 2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
  12. 2023Diffusion-based Conditional ECG Generation with Structured State Space Modelscited by 35
4 more
  1. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
  2. 2024Diffusion Models for Tabular Data Imputation and Synthetic Data Generationcited by 15
  3. 2019Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questionscited by 12
  4. 2022Time-series Transformer Generative Adversarial Networkscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Class-Conditioned Gaussian Mixture Modeling for Imbalanced Time Series Quantificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
  2. 2025Forecasting-based Biomedical Time-series Data Synthesis for Open Data and Robust AI
  3. 2024An overview of diffusion models for generative artificial intelligence
  4. 2024Stochastic Diffusion: A Diffusion Probabilistic Model for Stochastic Time Series Forecasting
  5. 2023Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models
  6. 2024TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation
  7. 2024Diffusion-TS: Interpretable Diffusion for General Time Series Generation
  8. 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review