A Non-Invasive Detection and Monitoring of Intracranial Pressure Using Ultrasound Sensors
Bibliographic record
Abstract
Background: Intracranial pressure (ICP) measurement is an extremely important part of the neurosurgical healthcare. Existing methods used for monitoring ICP are mainly grouped into invasive and non-invasive methods. Research into techniques for ICP monitoring is now gearing towards non-invasive methods to eliminate complications associated with invasive methods. The goal of this work was to propose an effective method for ICP monitoring. Method: The work presents a model-based approach for the analysis and characterization of the proposed method. ICP waveforms and characteristics were generated from mathematical models using computer simulation and various datasets. The simulation model of the proposed ultrasound system and biological system were developed. Results: The ICP pulse was achieved with a variance of 63.62 Pa from the reference model used. From our results, a minimum of 10 MHz with a minimum pulse width of 80 µs can be used in the development of proposed system. The cut-off frequencies for the pulse generator filter and mixed signal filter values were 40 MHz and 1 MHz respectively. Conclusion: The present study establishes a reference model for ultrasound system-biological system interaction. The study also proposes a new approach for ICP monitoring. The ICP monitoring approach in this paper has the advantage of being a simple, non-invasive and a direct method for ICP monitoring. The model presented is an effective tool in the field of research, coursework and presentations. The introduced device satisfies both the needs of the patients and that of the health personnel. J Neurol Res. 2015;5(1-2):167-180 doi: http://dx.doi.org/10.14740/jnr322w
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How this classification was reachedexpand
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 | 0.002 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".