Diffusion-based Conditional ECG Generation with Structured State Space Models

• URL: http://arxiv.org/abs/2301.08227v2
• Date: Thu, 15 Jun 2023 10:30:31 GMT
• Title: Diffusion-based Conditional ECG Generation with Structured State Space Models
• Authors: Juan Miguel Lopez Alcaraz and Nils Strodthoff
• Abstract summary: We propose SSSD-ECG for the generation of synthetic 12-lead electrocardiograms conditioned on more than 70 ECG statements. Due to a lack of reliable baselines, we also propose conditional variants of two state-of-the-art unconditional generative models.
• Score: 2.299617836036273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data. Recently, diffusion models have set new standards for generative models for different data modalities. Also very recently, structured state space models emerged as a powerful modeling paradigm to capture long-term dependencies in time series. We put forward SSSD-ECG, as the combination of these two technologies, for the generation of synthetic 12-lead electrocardiograms conditioned on more than 70 ECG statements. Due to a lack of reliable baselines, we also propose conditional variants of two state-of-the-art unconditional generative models. We thoroughly evaluate the quality of the generated samples, by evaluating pretrained classifiers on the generated data and by evaluating the performance of a classifier trained only on synthetic data, where SSSD-ECG clearly outperforms its GAN-based competitors. We demonstrate the soundness of our approach through further experiments, including conditional class interpolation and a clinical Turing test demonstrating the high quality of the SSSD-ECG samples across a wide range of conditions.

Related papers

• SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers [3.9018723423306003]
  We propose a novel tokenization strategy tailored for structured EHR data. We benchmark the fidelity, utility, and privacy of the generated data against state-of-the-art models.
  arXiv  Detail & Related papers  (2024-11-20T16:11:20Z)
• SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation [0.0]
  Diffusion models have made significant progress in recent years, creating the possibility for synthesizing data comparable to the real one. We propose the SSSD-ECG-nle architecture based on SSSD-ECG with a modified conditioning mechanism and demonstrate its efficiency on downstream tasks.
  arXiv  Detail & Related papers  (2024-07-15T16:31:25Z)
• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model [5.677138915301383]
  We present a novel generative model based on diffusion models that is the first successful application on electronic health records. Our model proposes a mechanism to perform class-conditional sampling to preserve label information.
  arXiv  Detail & Related papers  (2023-02-08T22:06:34Z)
• ECGAN: Self-supervised generative adversarial network for electrocardiography [11.460692362624533]
  High-quality synthetic data can support the development of effective predictive models for biomedical tasks. These limitations, for instance, negatively impact open access to electrocardiography datasets about arrhythmias. This work introduces a self-supervised approach to the generation of synthetic electrocardiography time series.
  arXiv  Detail & Related papers  (2023-01-23T15:48:02Z)
• PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings [6.029263679246354]
  We present PHYSIOGAN, a generative model to produce high fidelity synthetic physiological sensor data readings. We evaluate it against the state-of-the-art techniques using two different real-world datasets: ECG classification and activity recognition from motion sensors datasets.
  arXiv  Detail & Related papers  (2022-04-25T07:38:43Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data [125.7135706352493]
  Generative adversarial networks (GANs) typically require ample data for training in order to synthesize high-fidelity images. Recent studies have shown that training GANs with limited data remains formidable due to discriminator overfitting. This paper introduces a novel strategy called Adaptive Pseudo Augmentation (APA) to encourage healthy competition between the generator and the discriminator.
  arXiv  Detail & Related papers  (2021-11-12T18:13:45Z)
• Conditional Hybrid GAN for Sequence Generation [56.67961004064029]
  We propose a novel conditional hybrid GAN (C-Hybrid-GAN) to solve this issue. We exploit the Gumbel-Softmax technique to approximate the distribution of discrete-valued sequences. We demonstrate that the proposed C-Hybrid-GAN outperforms the existing methods in context-conditioned discrete-valued sequence generation.
  arXiv  Detail & Related papers  (2020-09-18T03:52:55Z)
• Partially Conditioned Generative Adversarial Networks [75.08725392017698]
  Generative Adversarial Networks (GANs) let one synthesise artificial datasets by implicitly modelling the underlying probability distribution of a real-world training dataset. With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset. In this work, we argue that standard Conditional GANs are not suitable for such a task and propose a new Adversarial Network architecture and training strategy.
  arXiv  Detail & Related papers  (2020-07-06T15:59:28Z)

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.