Electrocardiogram Analysis for Kratom Users Utilizing Deep Residual Learning Network and Machine Learning
Bibliographic record
Abstract
Kratom (Mitragyna speciosaKorth) is a common tropical plant found in Southeast Asia. Its leaves possess medicinal properties and are used to treat various ailments. However, the effects of kratom extract in terms of biological domains are still concerning. Although considerable studies have been conducted on the effects of kratom usage over the last few years, no study using in silico analysis of kratom users’ electrocardiogram (ECG) has been reported to date. This study aims to examine the long-term effects of kratom consumption using the ECG signals and deep learning (DL) network and machine learning techniques. Raw ECG signals were used as input for training and detecting abnormalities, and a deep residual learning network (DRLN) model was implemented to develop a feature extractor from single-lead datasets; the extracted features were used to train conventional machine learning classifiers. The confounding ECG abnormality factors, namely, age, sex, smoking, alcohol consumption, and exercise, were analyzed for association using the chi-square test. The main results of our study showed that kratom usage is not associated with ECG abnormalities. However, the ECG signal was affected more by gender than by the other factors; it exhibited the highest sensitivity and specificity (score$=$0.63). While this study is limited to ECG abnormalities, the results indicate that long-term usage of kratom for its health benefits may be considered a safe and natural practice.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".