---
license: cc-by-nc-sa-4.0
task_categories:
- audio-classification
tags:
- medical
configs:
- config_name: default
  data_files:
  - split: csa
    path: aware_csa.csv
---
Acoustic Waveform Airway and Respiratory Examination (AWARE/PTEase) Dataset 
===================================================================================================

Guidelines
===================================================================================================
AWARE/PTEase is a smartphone-based sensing system that examines human airway's internal physiological conditions, developed by the Intelligent Systems Laboratory at University of Pittsburgh. AWARE/PTEase probes the airway with acoustic pulses through mouth, and collect the airway's reflections for analysis. Please refer to [our paper](https://doi.org/10.1145/3581791.3596854) and [github repo](https://github.com/pittisl/PTEase) for more details.

This dataset includes the raw and pre-processed acoustic data of airway measurements collected from 382 human subjects recruited at the Children’s Hospital of Pittsburgh. Demographics of the recruited human subjects are as follows：
| Category           | Characteristics      | Number         |
|--------------------|----------------------|----------------|
|    Demographics    |      Age (years)     |  22.55 ± 15.63 |
|                    |      Adults (%)      |   166 (43.46)  |
|                    |      Female (%)      |   205 (53.66)  |
|                    |     Caucasian (%)    |   287 (75.13)  |
|                    | African-American (%) |   93 (24.35)   |
|   Body Conditions  |      Height (cm)     | 159.76 ± 15.67 |
|                    |      Weight (kg)     |  65.79 ± 27.28 |
| Disease Conditions |      Healthy (%)     |   115 (30.10)  |
|                    |      Asthma (%)      |   190 (49.74)  |
|                    |  Cystic Fibrosis (%) |   64 (16.75)   |
|                    |       COPD (%)       |    8 (2.09)    |
|                    |      Others (%)      |    5 (1.31)    |

The contents of this dataset include three parts:

- Raw WAV files
	
	- "aware_raw.zip"
        
        - Subfolders are named by the subjects' ID (from 1 to 382). In each subfolder, there are three "cali_X.wav" files and multiple "test_X.wav" files
		
    		- "cali_1.wav" : Calibration step 1 - long tube calibration - consists of 12 pairs of received pulses. Each pair contains one wide-band (0-16Hz) pulse and one narrow-band (0-200Hz) pulse. The narrow-band pulses are deperacated and only the wide-band pulses are used. Transmitting period for each pair is 0.4 sec.
    		
    		- "cali_2.wav" : Calibration step 2 - standard tube close-end calibration - is the same as above except the calibrations are conducted in a different setting.
    		
    		- "cali_3.wav" : Calibration step 3 - standard tube open-end calibration - is the same as above except the calibrations are conducted in a different setting.
    		
    		- "test_X.wav" : Test number X - the subject's X-th attempt of conducting the test - follows the protocol of "Nasal breathing -> Inhale -> Exhale -> Inhale -> Exhale -> Inhale -> Exhale". Each segment contains 25 pulses collected at an interval of 0.2 sec. NOTE: Some of the samples have 50 pulses for the "Nasal breathing" phase.


- Segmented and aligned acoustic pulses

	- "aware_segmented.pkl"

		- Python pandas.DataFrame saved as a PICKLE file. Same contents as "aware_segmented.csv", but without flattening.

		- To load the data, use the following commands: 
        
            ``` python
            import pandas as pd  
            df = pd.read_pickle("aware_segmented.pkl") 
            ```
        - Column 0-45 : Basic subject information, including demographics, clinical measurements and labels.
			- Column 0 : Subject ID
			- Column 1-9 : Demographics
			- Column 10 : Disease label
			- Column 11-21 : Impulse Oscillometry Measurements
			- Column 22-30 : Spirometry measurements
			- Column 31-44 : Spirometry measurements in post-bronchodilator test
			- Column 45 : Test number

		- Column 46-55 : Acoustic signals (16-bit PCM, sample rate = 48000) arranged by different phases. Each calibration phase contains 12 measurements and each testing phase contains 25 measurements. Each measurement lasts for 0.1 sec (4800 sample points).
			- Column 46 : Phase "Calibration #1". Each element is a [12,4800] NumPy ndarray
			- Column 47 : Phase "Calibration #2". Each element is a [12,4800] NumPy ndarray
			- Column 48 : Phase "Calibration #3". Each element is a [12,4800] NumPy ndarray
			- Column 49 : Phase "Nasal breathing". Each element is a [25,4800] NumPy ndarray
			- Column 50 : Phase "Inhale #1". Each element is a [25,4800] NumPy ndarray
			- Column 51 : Phase "Inhale #2". Each element is a [25,4800] NumPy ndarray
			- Column 52 : Phase "Inhale #3". Each element is a [25,4800] NumPy ndarray
			- Column 53 : Phase "Exhale #1". Each element is a [25,4800] NumPy ndarray
			- Column 54 : Phase "Exhale #2". Each element is a [25,4800] NumPy ndarray
			- Column 55 : Phase "Exhale #3". Each element is a [25,4800] NumPy ndarray

	- "aware_segmented.csv" and "headers.txt"
        
        - NOTE: Directly loading this CSV file is not recommended. It could be time-consuming and buggy. Try "aware_segmented.pkl" first.
   
        - NOTE: "aware_segmented.csv" has no headers. See column definition below.

		- Column 0-45 : Basic subject information, including demographics, clinical measurements and labels.
			- Column 0 : Subject ID
			- Column 1-9 : Demographics
			- Column 10 : Disease label
			- Column 11-21 : Impulse Oscillometry Measurements
			- Column 22-30 : Spirometry measurements
			- Column 31-44 : Spirometry measurements in post-bronchodilator test
			- Column 45 : Test number

		- Column 46-1012845 : Acoustic signals (16-bit PCM, sample rate = 48000) arranged by different phases. Each calibration phase contains 12 measurements and each testing phase contains 25 measurements. Each measurement lasts for 0.1 sec (4800 sample points).
			- Column 46-57645 : Phase "Calibration #1". Flattened from [12,4800] by row-major order.
			- Column 57646-115245 : Phase "Calibration #2". Flattened from [12,4800] by row-major order.
			- Column 115246-172845 : Phase "Calibration #3". Flattened from [12,4800] by row-major order.
			- Column 172846-292845 : Phase "Nasal breathing". Flattened from [25,4800] by row-major order.
			- Column 292846-412845 : Phase "Inhale #1". Flattened from [25,4800] by row-major order.
			- Column 412846-532845 : Phase "Inhale #2". Flattened from [25,4800] by row-major order.
			- Column 532846-652845 : Phase "Inhale #3". Flattened from [25,4800] by row-major order.
			- Column 652846-772845 : Phase "Exhale #1". Flattened from [25,4800] by row-major order.
			- Column 772846-892845 : Phase "Exhale #2". Flattened from [25,4800] by row-major order.
			- Column 892846-1012845 : Phase "Exhale #3". Flattened from [25,4800] by row-major order.

- Processed airway cross-sectional area (CSA) curves inferred from the acoustic signals

	- "aware_csa.csv"

        - NOTE: Not all raw samples are used for airway CSA reconstructions. Low-quality data is discarded, thus the sample size of this part is smaller than above
        
        - Column 0-45 : Basic subject information. Same as "aware_segmented.csv".
        
        - Column 46-129: Reconstructed airway cross-sectional area at different depths from mouth.


For more information about this dataset please contact: eric.yin@pitt.edu

Citation
===================================================================================================
Use of this dataset in publications must be acknowledged by referencing the following publication: 

```
@inproceedings{yin2023ptease,
    title={PTEase: Objective Airway Examination for Pulmonary Telemedicine using Commodity Smartphones},
    author={Yin, Xiangyu and Huang, Kai and Forno, Erick and Chen, Wei and Huang, Heng and Gao, Wei},
    booktitle={Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services},
    pages={110--123},
    year={2023},
    url = {https://doi.org/10.1145/3581791.3596854},
    doi = {10.1145/3581791.3596854}
}
```

Other Related Publications:
===================================================================================================
```
@inproceedings{yin2022out,
    title={Out-Clinic Pulmonary Disease Evaluation via Acoustic Sensing and Multi-Task Learning on Commodity Smartphones},
    author={Yin, Xiangyu and Huang, Kai and Forno, Erick and Chen, Wei and Huang, Heng and Gao, Wei},
    booktitle={Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems},
    pages={1182--1188},
    year={2022},
    url = {https://doi.org/10.1145/3560905.3568437},
    doi = {10.1145/3560905.3568437}
}
```

License
===================================================================================================
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This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].

[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]

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