Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DiffECG: Diffusion Model-Powered Label-Efficient and Personalized Arrhythmia Diagnosis
Authors: Tianren Zhou, Zhenge Jia, Dongxiao Yu, Zhaoyan Shen
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our proposed method outperforms the SOTA method by 37.9% and 23.9% in terms of generalization and personalization performance, respectively. The source code is available at: https://github.com/Auguuust/Diff EC. |
| Researcher Affiliation | Academia | Tianren Zhou , Zhenge Jia , Dongxiao Yu and Zhaoyan Shen School of Computer Science and Technology, Shandong University EMAIL, EMAIL |
| Pseudocode | No | The paper includes equations and figures illustrating the model architecture and processes, but no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at: https://github.com/Auguuust/Diff EC. |
| Open Datasets | Yes | We evaluate all the methods based on five public ECG datasets. CPSC [Goldberger et al., 2000; Liu et al., 2018] consists of ECG records with 9 different types of arrhythmias ranging from 6 to 60 seconds.Chapman [Zheng et al., 2020] consists of ECG records with 11 different types of arrhythmias with a 10-second duration for each record. PTB [Bousseljot et al., 1995; Physio Bank, 2000] dataset contains 549 records ranging from 1 to 5 seconds. Georgia [Alday et al., 2020] consists of ECG records with 56 different types of arrhythmias, with a 10second duration for each record. LTAF [Petrutiu et al., 2007; Goldberger et al., 2000] includes 84 ECG records of subjects with paroxysmal or sustained AF. |
| Dataset Splits | Yes | Specifically, we first split each dataset (i.e., CPSC, Chapman, PTB, and Georgia) into finetuning and testing sets subject-wisely (with a splitting ratio of 2:8) to ensure the subject s data is not mixed between the finetuning and testing sets. [...] Specifically, we first split each subject s data in the LTAF dataset with a 1:9 ratio, using 10% for fine-tuning and 90% for testing. |
| Hardware Specification | Yes | The training is conducted on a server equipped with four NVIDIA RTX 4090 GPUs, an Intel Xeon Platinum 8480+ CPU, and 1 TB of memory. |
| Software Dependencies | No | We use Py Torch for all methods to build networks, train models, and report detection performance. |
| Experiment Setup | Yes | All methods are pre-trained using the CPSC and Chapman datasets, respectively, fine-tuned on the other datasets with 20 epochs. The noise-adding step t of our method is fixed to 20. |