A Novel 12-Lead Electrocardiographic System for Home Use: Development and Usability Testing
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality worldwide. Early diagnosis is of pivotal importance for patients with cardiac arrhythmias and ischemia to minimize the consequences like strokes and myocardial infarctions. The chance of capturing signals of arrhythmias or ischemia is substantially high when a 12-lead electrocardiogram (ECG) can be recorded at the moment when a patient experiences the symptoms. However, until now, available diagnostic systems (Holter monitors and other wearable ECG sensors) have not enabled patients to record a reliable 12-lead ECG at home. OBJECTIVE: The objective of this project was to develop a user-friendly system that enables persons with cardiac complaints to record a reliable 12-lead ECG at home to improve the diagnostic process and, consequently, reduce the time between the onset of symptoms and adequate treatment. METHODS: Using an iterative design approach, ECGraph was developed. The system consists of an ECG measurement system and a mobile app, which were developed with the help of several concept tests. To evaluate the design, a prototype of the final design was built and a final technical performance test and usability test were executed. RESULTS: The ECG measurement system consists of a belt and 4 limb straps. Ten wet Ag/AgCl electrodes are placed in the belt to optimize skin-electrode contact. The product is controlled via an app on the mobile phone of the user. Once a person experiences symptoms, he or she can put on the belt and record ECGs within a few minutes. Short instructions, supported by visualizations, offer guidance during use. ECGs are sent wirelessly to the caregiver, and the designated expert can quickly interpret the results. Usability tests with the final prototype (n=6) showed that the participants were able to put on the product within 8 minutes during first-time use. However, we expect that the placement of the product can be executed faster when the user becomes more familiar with the product. Areas of improvement focus mainly on confidence during product use. In the technical performance test, a 12-lead ECG was made and reproduced 6 times. CONCLUSIONS: We developed a new 12-lead ECG system for home use. The product is expected to be more user-friendly than current hospital ECG systems and is designed to record more reliable data than current ECG systems for home use, which makes it suitable for expert interpretation. The system has great potential to be incorporated into an outpatient practice, so that arrhythmias and ischemia can be diagnosed and treated as early as possible.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".