Development of a real‐time adaptive delta compression algorithm for photoplethysmography system
Bibliographic record
Abstract
Photoplethysmography (PPG) is a simple and low‐cost optical technique that measures the blood volume change of the microvascular bed. The multichannel PPG recording system enables researchers to study the characteristics of the PPG signal from multiple body sites. Long‐time, continuous PPG signal monitoring helps doctors in medical diagnosis and treatment. However, the development of the PPG signal acquisition system is limited by storage capacity and computational and hardware capability. Limitation in storage capacity and communication efficiency can be addressed by introducing data compression algorithms. In this paper, a single‐channel, high‐resolution (24‐bit) wireless PPG data acquisition system using Bluetooth connectivity with the adaptive delta compression algorithm is presented. A graphical user interface was developed using MATLAB to interface with the system using a serial port profile. The PPG signals were decompressed and stored in the local hard drive for future analysis. The performance of adaptive data compression algorithm was evaluated with 12 healthy subjects. Results show that the compression ratio achieved is 41.5. The mean percentile root‐mean‐square (RMS) difference at the sampling frequency of 100 Hz is 0.13%. The mean normalized percentile RMS difference value is 9.78%. The system was successfully tested in a continuous acquisition mode for up to 60 min. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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 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 | 0.000 | 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 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".