DiffECG: Diffusion Model-Powered Label-Efficient and Personalized Arrhythmia Diagnosis
Authors: Tianren Zhou, Zhenge Jia, Dongxiao Yu, Zhaoyan Shen
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | 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. |