Online ECG quality assessment for context-aware wireless body area networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it