Secure Transmission of EEG Data Using Watermarking Algorithm for the Detection of Epileptical Seizures
Bibliographic record
Abstract
Internet of things (IoT) has a collection of multiple network-enabled devices like sensors, gateways, smartphones, and communication links (short and long ranges). Tremendous capacity of IoT system has made possible to monitoring and detection of epileptical seizures in real time. For this purpose, various smart devices and applications, helps to transmit information securely. Amalgamation of IoT with healthcare system provides opportunity to deal issues like security, detection of seizures and real time monitoring. The proposed model of cloud-enabled Health IoT system has been presented in this paper, gives the idea about monitoring of epileptical patients. For secured transmission of Electroencephalogram (EEG) data, digital watermarking technique has been used over two dimensional EEG data obtained through one dimensional EEG data by applying Short Time Fourier Transform (STFT). In this paper, watermarking of two dimensional EEG data has been done using discrete wavelet transform - discrete cosine transform (DWT-DCT) based Bacterial Foraging Optimization (BFO) technique and its performance has been figure out. Here, satisfactory watermarking performance in terms of Peak Signal to Noise Ratio (PSNR) 49.50 for class Z and 49.61 for class S EEG data along with Normalized Cross Correlation (NCC) 0.0039 for both classes of EEG data have been achieved.
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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 | 0.000 | 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".