Long Short Term Memory Based Recurrent Neural Network for Wheezing Detection in Pulmonary Sounds
Bibliographic record
Abstract
This paper presents a new technique for wheezes detection in respiratory sounds using a Long Short-Term Memory (LSTM), a specific type of Recurrent Neural Networks (RNNs). The purpose of this work is to develop an LSTM-based system and compare its classification performances to those obtained by a Multilayer Perceptron (MLP) feed-forward network. The MLP is a widely used neural network that has proven its efficiency in respiratory sound classification. Feed-forward networks do not consider time dependencies, while RNNs reach their limit in detecting dependencies when they occur at long time intervals. Because wheezing occurs over several consecutive intervals, we assume that LSTM takes into account the changing characteristics better than MLP and provides better results. Pulmonary sounds are characterized using the Mel-Frequency Cepstral Coefficients (MFCC) method before applying the LSTM-based classifier. As expected, the experimental tests show that LSTM takes advantage of the long-term dependencies observed in wheezing sounds to lead to better classification performances (accuracy of 91%).
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".