Research on Local Field Potential Signal Classification Algorithm Based on Transfer Learning
Bibliographic record
Abstract
The local field potential signals (LFPs), as a vital signal for studying the mechanisms of deep brain stimulation (DBS) and constructing adaptive DBS contain information related to the motor symptoms of Parkinson's disease (PD). This paper proposed a Parkinson's disease state recognition algorithm based on the idea of transfer learning.The algorithm uses continuous wavelet transform (CWT) to convert one-dimensional LFPs into two-dimensional gray-scalogram images and color images respectively, and adds a Bayesian optimized random forest (RF) classifier to replace the three fully connected layers used in the classification task in the VGG16 model, to realize the pathologic status identification of PD and normal state of parkinsonian patients. It was found that consistently superior performance of gray-scalogram images over color images. The proposed algorithm achieved an impressive accuracy of 97.76%, outperforming feature extractors such as VGG19, InceptionV3, ResNet50, and the lightweight network MobileNet. This algorithm has high accuracy and can monitor the status of patients in real time without manual feature extraction, and only apply DBS stimulation when in PD state, effectively improving the closed-loop adaptive DBS treatment effect.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".