AI-enhanced diagnosis of atrial arrhythmia using 3D-printed origami ECG sensors
Bibliographic record
Abstract
Traditional Electrocardiogram (ECG) sensors using silver/silver chloride (Ag/AgCl) electrodes suffer from skin irritation, short shelf life, single-use limitation, and environmental waste. Here, we introduce a sustainable 3D-printed origami-structured ECG sensor featuring dry attachment, accurate measurement, reusability, and AI-powered diagnosis. The origami design combines mechanical stretchability, robustness, and self-adhesive, while the patterned carbon-based conductive ink provides high electrical conductivity (5681 ± 122.5 S/m), flexibility (bending to 2.5 mm radius), and biocompatibility, altogether offering a sustainable alternative to Ag/AgCl electrodes. The resulting sensor delivers accurate ECG signals comparable to commercial Ag/AgCl electrodes, in addition to an AI-enabled swift classification system that combines continuous wavelet transform (CWT) and a customized convolutional neural network (CNN) for real-time pre-diagnosis of one sinus rhythm and ten arrhythmias types from ECG scalogram images. This system monitors continuously for up to 34 h, promoting early detection of transient cardiac conditions and personalized health monitoring. This advancement establishes a new standard for AI-enhanced, eco-friendly ECG sensors, with significant potential for applications in remote healthcare, emergency diagnostics, and real-time cardiac monitoring.
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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 | 0.001 |
| 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 | 0.001 | 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".