Using Time Domain Characteristics to Discriminate Physiologic and Parkinsonian Tremors
Bibliographic record
Abstract
Tremor amplitude and frequency do not always clearly differentiate subjects with particular pathologies from control subjects or from subjects with other pathologies, especially in early stages of a disease. For patients with early stages of Parkinson's disease (PD) the discriminative power of amplitude was compared with that of other time domain characteristics of tremor recordings that are probably not evident clinically. Postural tremor with and without visual feedback and rest tremor were recorded in both hands of a group of patients with Parkinson's disease (n = 21) and a group of healthy control subjects (n = 30) using displacement lasers. Velocity and acceleration data were derived from displacement data. Twelve time domain characteristics were calculated on each recording and the discriminating power of each was evaluated using the worse hand in each case. Postural tremor with no visual feedback separates the two groups of subjects most efficiently, especially in velocity and acceleration. Tremor in Parkinson's disease (in comparison to normal physiologic tremor) has a specific morphology, has a distinctive histogram, is more periodic, and contains indications of nonlinearity in the underlying dynamics. There may also be greater difference in amplitude between the two hands and time asymmetry in tremor of patients with PD. A series of finger flexions seems to enhance normal tremor but not tremor in PD and may thus aid in discrimination. Discrimination of tremor attributable to PD from normal physiologic tremor can be enhanced by measuring time domain characteristics subtler than amplitude, particularly when amplitude itself is not large. Tremor measurement should not be limited to acceleration data because some information is more visible in other variables.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".