An Automatic Cardiac Arrhythmia Classification System With Wearable Electrocardiogram
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 paper presents an automatic wearable electrocardiogram (ECG) classification and monitoring system with stacked denoising autoencoder (SDAE). We use a wearable device with wireless sensors to obtain the ECG data, and send these ECG data to a computer with Bluetooth 4.2. Then, these ECG data are classified by the automatic cardiac arrhythmia classification system. First, the ECG feature representation is learned by the SDAE with sparsity constraint. Then, the softmax regression is used to classify the ECG beats. In the fine-tuning phase, an active learning is added to improve the performance. In the active learning phase, we use the method that relies on the deep neural networks posterior probabilities to associate confidence measures to select the most informative samples. Breaking-ties and modified breaking-ties methods are used to select the most informative samples. We validate the proposed method on the well-known MIT-BIH arrhythmia database and ECG data obtained from the wearable device. We follow the recommendations of the Association for the Advancement of Medical Instrumentation for class labeling and results presentation. The results show that the classification performance of our proposed approach outperforms the most of the state-of-the-art methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it