Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurological disease that progresses further over time. Individuals suffering from this condition have a deficiency of dopamine, a neurotransmitter found in the brain's nerve cells that is critical for coordinating body movement. In this study, a new approach is proposed for the diagnosis of PD. Common Average Reference (CAR), Median Common Average Reference (MCAR), and Weighted Common Average Reference (WCAR) methods were primarily utilized to eliminate noise from the multichannel recorded walking signals in the resulting PhysioNet dataset. Statistical features were obtained from the clean walking signals following the Local Binary Pattern (LBP) transformation application. Logistic Regression (LR), Random Forest (RF), and K-nearest neighbor (Knn) methods were utilized in the classification stage. A high success rate with a value of 92.96% was observed with Knn. It was also determined that signals on which foot and the signals obtained from which point of the sole of the foot were effective in PD diagnosis in the study. In light of the findings, it was observed that noise reduction methods increased the success rate of PD diagnosis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".