Clustering Parkinson’s and Age-Related Voice Impairment Signal Features for Unsupervised Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study focuses on the possibility of remote monitoring and screening of Parkinson’s and age-related voice impairment for the general public using self-recorded data on readily available or emerging technologies such as Smartphone and IoT devices. While most studies use professionally recorded voice in a controlled environment, this study uses self-recorded sustained vowel /a/ recordings using iPhone. Each healthy control (HC) and people with Parkinson’s (PWP) group has 57 age-matching mixed-gender subjects. The control subjects can have age-related voice impairment. Without severity labels, features extracted from the recordings were grouped by their similarity in voice using unsupervised learning with various clustering methods. The optimal number of clusters ([Formula: see text]) was estimated using direct and statistical methods. The estimated [Formula: see text] does not agree with the defined Unified Parkinson’s Disease Rating Scale-Speech (UPDRS-3.1) scales. Using [Formula: see text], five hierarchical and one partition-based clustering were used for comparison and cross-checking. The hierarchical-based methods are Hierarchical Cluster (HCluster), Hierarchical K-Means (HKMeans), Agglomerative Nesting (AGNES), Divisive Analysis (DIANA), and neural network-based Self-Organized Tree Algorithm (SOTA). The partition-based method is Clustering Large Applications (CLARA). Three internal validation indices: connectivity, Dunn index and silhouette width, were used to measure the compactness of the clusters and their separations. The validation result, ordered from the best, is AGNES, HCluster, DIANA, HKMeans, CLARA, and SOTA. Majority vote was applied to the results from AGNES, HCluster and DIANA to obtain the final grouping. Five groups were defined representing outliers, severely impaired voice, minor impaired, healthier voice, and cannot be grouped. All methods identified the same two outliers except SOTA. The clustering and voting have successfully identified the 2 outliers, 5 more severely impaired, 82 minor impaired, and 22 healthier voice. Only 3 could not be grouped. Feature extraction has reduced the data size by a factor of 518. It is possible to first reduce the data size for transmission and perform unsupervised learning at the receiving end for remote monitoring and screening.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it