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Electrocardiogram-based short-term risk stratification for acute kidney injury using time-frequency deep learning
Weiqiao Wang1, Yiyang Cen1, Yuanzhao Li1
1School of Medical Technology, Beijing Institute of Technology, Beijing, China.
Computers in Biology and Medicine
|June 4, 2026
Summary
A novel deep learning model, FFT-ECG, shows promise for early acute kidney injury (AKI) risk prediction using electrocardiograms. While internally validated, external performance highlights the need for further development before widespread clinical use.
Area of Science:
- Cardiology and Nephrology
- Artificial Intelligence in Medicine
- Biomedical Signal Processing
Background:
- Acute kidney injury (AKI) is a critical complication in critically ill patients.
- Current AKI diagnosis methods (serum creatinine, urine output) detect injury after it has occurred.
- Non-invasive physiological signals offer potential for earlier AKI risk assessment.
Purpose of the Study:
- To develop and validate an electrocardiogram (ECG)-based deep learning framework (FFT-ECG) for short-term AKI risk stratification.
- To predict any-stage AKI within a 24-hour window using time-frequency ECG analysis.
- To evaluate the model's performance in binary classification and survival analysis for AKI risk.
Main Methods:
- Developed the FFT-ECG model using a dual-path convolutional architecture on 12-lead ECG segments from the MIMIC-IV database.
- Employed time-domain and frequency-domain signal processing with BiGRU and attention modules.
- Validated the model internally on MIMIC-IV and externally on VitalDB, assessing binary AKI prediction and survival risk.
Main Results:
- Internally, the 12-lead FFT-ECG model achieved an AUROC of 0.736 for AKI prediction.
- A single-lead model showed comparable internal performance but significantly attenuated results in external validation (AUROC 0.550).
- Attention analysis localized key ECG regions (R-wave, Q wave, ST segment) influencing predictions.
Conclusions:
- FFT-ECG shows preliminary internal efficacy for ECG-based AKI risk stratification.
- External validation revealed limited model transportability, necessitating recalibration and domain adaptation.
- ECG-based AKI assessment is feasible but requires further multicenter validation for clinical implementation.
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