A Novel Approach to Classify Electrocardiogram Signals Using Deep Neural Networks
Bibliographic record
Abstract
Atrial fibrillation (AF) is an unusual heart rhythm condition which is caused by sporadic firing of electrical impulses from multiple places in the atria. AF is considered to be one of the leading cause of stroke in the world. AF is usually screened manually with the help of Electrocardiodiagram (ECG) reading. The manual process of reading ECG is a tedious and time-consuming task which is laden with human errors. Therefore, an automated process is quintessential. However, discerning anomaly in heart function using an efficient automated process has been a challenging task for quite some time. In this paper, the authors propose an intricate Neural Network architecture, CNN+LSTM, for the classification amongst four types of heart condition-Normal, Atrial Fibrillation, Noisy Sinus Rhythm and Alternative Rhythms using a dataset from PhysioNet/2017challenge. Volunteers in PhysioNet/2017 challenge dataset came from diverse backgrounds and had a wide window of variation in their physical attributes, making the dataset sufficiently reliable. In addition, the number of samples in this dataset exceeded any other before it on this topic, which further adds to the comprehensiveness of this dataset. The authors' method reached a summit accuracy of 91.19% using the proposed model.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".