Applications of Ballistocardiogram in the Diagnosis of Coronary Heart Disease: Systematic Review
Bibliographic record
Abstract
Background: Coronary heart disease (CHD) continues to account for a substantial proportion of deaths worldwide. Ballistocardiogram (BCG), a noncontact, noninvasive technique for monitoring cardiac activity, has gained increasing attention for its potential role in various medical applications, particularly in CHD. This review comprehensively explores the applications of BCG in the diagnostic evaluation of CHD. Objective: The aim of this systematic review is to evaluate the clinical applications and diagnostic capabilities of BCG in CHD, with the ultimate goal of enhancing the precision of CHD management and optimizing therapeutic decision-making pathways. Methods: A literature search was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines to identify studies evaluating the use of BCG in CHD. The initial search identified 500 studies. Based on titles, abstracts, and keywords, 266 studies were selected for further review. Following further exclusion of non-English articles, animal studies, and review articles, 38 eligible studies were included in the final analysis. Results: Among the 38 studies, 22 focused on the application of BCG in acute coronary syndrome. These studies explored various aspects, including BCG waveforms in patients with acute myocardial infarction, the diagnosis of acute coronary syndrome, and the relationship between age and the rate of abnormal BCG waveforms. The remaining studies covered the effects of drugs, emotions, exercise, and other variables on BCG recordings in patients with CHD. Sample sizes varied significantly across the studies, 36 studies explicitly reported sample sizes, encompassing a total of 9479 participants with individual study sizes ranging from 1 to 903 cases. Notably, 13 studies enrolled fewer than 50 participants, raising concerns about potential selection bias and reduced reliability of the findings. Conclusions: Overall, while BCG demonstrates significant potential in the diagnosis and prevention of CHD, several limitations remain. Variability in study design, sample size, and outcome measures poses challenges to the generalizability of findings. Nevertheless, the capability of BCG to reflect cardiac function and assist in the detection of CHD remains valuable. With continued research and technological advancement, BCG has the potential to transform current approaches to CHD diagnosis and management, ultimately improving patient outcomes and quality of life.
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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".