https://github.com/dioptx/inhalesense
A deep learning approach for respiratory audio discovery and classification.
https://github.com/dioptx/inhalesense
Last synced: 7 months ago
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A deep learning approach for respiratory audio discovery and classification.
- Host: GitHub
- URL: https://github.com/dioptx/inhalesense
- Owner: dioptx
- License: mit
- Created: 2019-04-14T17:40:14.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-09-30T12:57:13.000Z (over 1 year ago)
- Last Synced: 2025-02-15T19:16:23.408Z (over 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 49.9 MB
- Stars: 7
- Watchers: 4
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# InhaleSense
The goal of this project is to produce a deep learning model that discovers and classifies sections of interest in audio files.
The dataset used for the initial stage of the implementation is not provided.
[Research Gate & Publication Link](https://www.researchgate.net/publication/335135907_Recognition_of_breathing_activity_and_medication_adherence_using_LSTM_Neural_Networks)
### Authors of the publication:
- Dionisis Pettas (dennis.petta@gmail.com)
- Stavros Nousias (nousias.stavros@gmail.com)
- Evangelia Zacharaki (ezachar@upatras.gr)
### Some other notes:
- The approach makes use of a simple LSTM model in order to discover inhalations, exhalations and Drug administration in
.wav audio files.
- A trained model can be found in the data section.
- The preparation and processing files house utility functions whereas the Jupyter notebooks provide some usecases based on the dataset
described in the paper:
__Recognition of breathing activity and medication
adherence using LSTM Neural Networks - BIBE 2019__
- The environment for this project can be replicated with the environment.yml file provided.
- The dataset used for this paper is only accessible by request, but the model works with any .wav file containing respiratory sounds.
### Demo with dataset included
An extended version of the code also including and comparing other methods is made available:
- https://codeocean.com/capsule/8383844/tree/
- https://github.com/snousias/Revisiting-Content-Based-Audio-Classification-for-Asthma-Medication-Adherence/tree/master
### Dataset on IEEE Dataport
The dataset is available in IEEE Dataport and the dataset format can be summarized as follows:
Link:
- https://ieee-dataport.org/documents/respiratory-and-drug-actuation-dataset
Generic format:
```
Filename, Class, Sample index at the beginning of the acoustic event, Sample index at the end of the acoustic event
```
Example:
```
rec2018-01-22_17h41m33.475s.wav,Exhale,6015,17437
rec2018-01-22_17h41m33.475s.wav,Inhale,20840,31655
rec2018-01-22_17h41m33.475s.wav,Drug,31898,37610
rec2018-01-22_17h41m33.475s.wav,Exhale,43686,59969
rec2018-01-22_17h41m49.809s.wav,Inhale,5043,17316
rec2018-01-22_17h41m49.809s.wav,Drug,18288,24364
rec2018-01-22_17h41m49.809s.wav,Exhale,31412,46724
rec2018-01-22_17h42m07.718s.wav,Exhale,303,9782
rec2018-01-22_17h42m07.718s.wav,Inhale,16951,28010
```
#
_Feel free to fork and use it with your dataset._