A noninvasive hyperkalemia monitoring system for dialysis patients based on a 1D-CNN model and single-lead ECG from wearable devices
Bibliographic record
Abstract
This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis patients with chronic kidney disease. Hyperkalemia, common in dialysis patients, can lead to life-threatening arrhythmias or sudden death if untreated. Therefore, real-time monitoring of hyperkalemia in this population is crucial. We propose a wearable single-lead ECG monitoring system, offering enhanced comfort and feasibility for extended use. The key innovation of this system is the design of a compact, multi-channel convolutional neural network. This model offers high stability, strong performance, and exceptional computational efficiency, making it ideal for seamless integration into wearable devices for real-time monitoring applications. The model automatically extracts features from ECG signals at different frequencies through multiple convolutional channels, eliminating the need for manual feature extraction before data input. Data is input using a non-overlapping sliding window approach, reducing preprocessing complexity while maintaining model performance. We investigated the optimal window length and the number of convolution channels for ECG signal input. Experimental results indicate that the model achieves optimal performance with a 1200 ms window length and four parallel convolutional branches, yielding an accuracy of 98.25% (4.52%), F1-score of 98.31% (3.26%), sensitivity of 98.63% (2.41%), and specificity of 97.88% (5.13%). This system holds significant potential for improving patient monitoring comfort and real-time responsiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".