Sensor Medium Access Control (SMAC)-based epilepsy patients monitoring system
Bibliographic record
Abstract
With the increasing number of the epilepsy patients (EPs) and their growing need for continuous monitoring and immediate respond to their seizures, the Epilepsy Patients Monitoring System (EPMS) is proposed to be the fit solution for the healthcare monitoring applications especially for the EPs monitoring application. This paper focuses on the use of Wireless Sensor Networks (WSNs) for the healthcare monitoring applications. The main objective of this system is to decrease the response time for the sudden seizure, protect patients from possible severe consequences and help them become comfortable with the monitoring process. Our EPMS consists of five regular nodes placed at specific sites on the patient's body, as well as a coordinator node and a receiver node. The regular nodes detect seizures and forward the data to the coordinator, which collects the data and transmits it to the receiver. The Sensor Medium Access Control (SMAC) (a new MAC protocol specifically designed for WSNs) protocol has been used to decrease the generated delays and to achieve the highest throughput for the proposed system. We evaluate our proposed system via NS-2 simulations, and based on the numerical results, we show that SMAC-based system gives appropriate response time which achieves the expected objectives.
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.
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 | 0.001 | 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 | 0.001 | 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".