Detecting Defecation Premonition from the Acoustic Activity of Bowel Sounds
Bibliographic record
Abstract
This study investigates a method for detecting predefecatory signs from bowel sounds to help prevent fecal incontinence. We compared two analytical approaches: a macroanalysis of entire 8-second audio clips and a micro-analysis of individually detected bowel sound events. Using data collected from a single subject, multiple machine learning models were evaluated for their ability to classify audio as either belonging to a specific time window before defecation (ranging from 10 to 270 minutes) or to other times. The results consistently showed that the macro-analysis outperformed the micro-analysis in overall discriminative performance. A CNN-BiLSTM model achieved the highest performance in the macro-analysis, with a peak AUC of 0.76 for the 60-minute pre-defecation window. A subsequent statistical analysis revealed that this performance difference is attributable to a significant increase in the frequency and total duration of bowel sound events prior to defecation ($p<0.001$,$r=0.378$). These findings indicate that the quantitative density of bowel sound events is a more dominant indicator for detecting defecation premonition than the qualitative acoustic characteristics of individual sounds. This suggests that focusing on event frequency analysis is a more promising direction for developing non-invasive prediction systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".