Automated, Portable, and Low-Cost System for Home Screening of Peripheral Arterial Disease
Bibliographic record
Abstract
Peripheral artery disease (PAD) is the manifestation of atherosclerosis where peripheral arteries are narrowed by the deposition of lipid and cholesterol and formation of fat fiber plaques on their walls. Occlusion and narrowing of the arteries may lead to several cardiovascular diseases and therefore in-time diagnosis and treatment of PAD is essential. There are several invasive and noninvasive methods to diagnosis of PAD, however, still an automated, easy-to-use, and affordable device for in-home monitoring of cardiovascular health is lacking. In this study, an oscillometric system is used to record arterial wall oscillations at different external pressures in lower and upper limbs. Wavelet transform is used to extract features from the amplitude of the recorded arterial wall oscillations and an adaptive neuro-fuzzy inference system (ANFIS) is used to identify PAD based on the extracted features. Because of the ANFIS high computational cost, linear discriminant analysis (LDA) is applied to the features to reduce their dimension before feeding them to ANFIS. The performance of proposed method is compared versus the conventional ankle-brachial test on a dataset of oscillometric recording obtained from 14 patients with PAD and 14 healthy individuals. Based on a five-fold cross validation, our method achieved an accuracy of 82% in detecting PAD. The results show promise toward the development of in-home cardiovascular monitors for cardiovascular health assessment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".