Online ECG quality assessment for context-aware wireless body area networks
Bibliographic record
Abstract
Electrocardiogram (ECG) signals are commonly used in wireless body area networks (WBAN), particularly for patient monitoring applications. ECGs, however, are sensitive to various types of noise sources, including but not limited to: powerline interference, movement, muscle and breathing artefacts. Such sensitivity is increased when burgeoning lower-cost sensors, such as textile ECG sensors, are used. Transmission of noisy ECGs can be troublesome for various reasons. For example, it consumes bandwidth, battery life, and storage space with signals that convey little cardiac information. Moreover, noisy signals may cause false alarms in automated patient monitoring systems, thus increasing the burden on medical personnel. In this paper, we describe a new ECG quality index based on the so-called modulation spectral signal representation. Two classifiers are tested to discriminate between usable and non-usable ECG segments. When applied within a quality-aware WBAN application, we show savings of up to 65% in storage space relative to a traditional scheme.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".