Deep Embedded Clustering for Data-Driven ECG Exploration Using Continuous Wavelet Transforms
Bibliographic record
Abstract
Due to the length, complexity, and inter-subject variation of physiological signals acquired non-invasively, datadriven analysis is increasingly valuable in Smart Health, precision medicine, and in studying physiological dynamics. Knowledge discovery in medical research requires that domain experts analyze complex, high-dimensional data and signals, which is facilitated by dimensionality reduction and clustering. The current paper presents an unsupervised machine learning approach to clustering time-frequency features from ECG records using deep embedded clustering, which optimizes a clustering metric that maps high-dimensional time-frequency data to a lower dimensional latent space. These clusters can be obtained in arbitrarily low dimensions, and are subsequently analyzed with visual analytics to uncover structures and patterns. This technique is applied to time segments of continuous wavelet transforms of ECG records, representing a variety of conditions. Preliminary results on publicly-available ECG records indicate that deep embedded clustering produces a low-dimensional learned representation of time-frequency characteristics that facilitates signal exploration and improves interpretability.
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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".