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--- |
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license: cc-by-nc-sa-4.0 |
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task_categories: |
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- audio-classification |
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tags: |
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- medical |
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configs: |
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- config_name: default |
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data_files: |
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- split: csa |
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path: aware_csa.csv |
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--- |
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Acoustic Waveform Airway and Respiratory Examination (AWARE/PTEase) Dataset |
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=================================================================================================== |
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Guidelines |
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=================================================================================================== |
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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. |
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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： |
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| Category | Characteristics | Number | |
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|--------------------|----------------------|----------------| |
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| Demographics | Age (years) | 22.55 ± 15.63 | |
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| | Adults (%) | 166 (43.46) | |
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| | Female (%) | 205 (53.66) | |
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| | Caucasian (%) | 287 (75.13) | |
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| | African-American (%) | 93 (24.35) | |
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| Body Conditions | Height (cm) | 159.76 ± 15.67 | |
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| | Weight (kg) | 65.79 ± 27.28 | |
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| Disease Conditions | Healthy (%) | 115 (30.10) | |
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| | Asthma (%) | 190 (49.74) | |
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| | Cystic Fibrosis (%) | 64 (16.75) | |
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| | COPD (%) | 8 (2.09) | |
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| | Others (%) | 5 (1.31) | |
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The contents of this dataset include three parts: |
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- Raw WAV files |
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- "aware_raw.zip" |
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- 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 |
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- "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. |
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- "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. |
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- "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. |
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- "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. |
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- Segmented and aligned acoustic pulses |
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- "aware_segmented.pkl" |
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- Python pandas.DataFrame saved as a PICKLE file. Same contents as "aware_segmented.csv", but without flattening. |
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- To load the data, use the following commands: |
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``` python |
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import pandas as pd |
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df = pd.read_pickle("aware_segmented.pkl") |
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``` |
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- Column 0-45 : Basic subject information, including demographics, clinical measurements and labels. |
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- Column 0 : Subject ID |
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- Column 1-9 : Demographics |
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- Column 10 : Disease label |
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- Column 11-21 : Impulse Oscillometry Measurements |
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- Column 22-30 : Spirometry measurements |
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- Column 31-44 : Spirometry measurements in post-bronchodilator test |
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- Column 45 : Test number |
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- 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). |
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- Column 46 : Phase "Calibration #1". Each element is a [12,4800] NumPy ndarray |
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- Column 47 : Phase "Calibration #2". Each element is a [12,4800] NumPy ndarray |
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- Column 48 : Phase "Calibration #3". Each element is a [12,4800] NumPy ndarray |
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- Column 49 : Phase "Nasal breathing". Each element is a [25,4800] NumPy ndarray |
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- Column 50 : Phase "Inhale #1". Each element is a [25,4800] NumPy ndarray |
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- Column 51 : Phase "Inhale #2". Each element is a [25,4800] NumPy ndarray |
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- Column 52 : Phase "Inhale #3". Each element is a [25,4800] NumPy ndarray |
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- Column 53 : Phase "Exhale #1". Each element is a [25,4800] NumPy ndarray |
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- Column 54 : Phase "Exhale #2". Each element is a [25,4800] NumPy ndarray |
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- Column 55 : Phase "Exhale #3". Each element is a [25,4800] NumPy ndarray |
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- "aware_segmented.csv" and "headers.txt" |
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- NOTE: Directly loading this CSV file is not recommended. It could be time-consuming and buggy. Try "aware_segmented.pkl" first. |
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- NOTE: "aware_segmented.csv" has no headers. See column definition below. |
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- Column 0-45 : Basic subject information, including demographics, clinical measurements and labels. |
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- Column 0 : Subject ID |
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- Column 1-9 : Demographics |
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- Column 10 : Disease label |
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- Column 11-21 : Impulse Oscillometry Measurements |
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- Column 22-30 : Spirometry measurements |
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- Column 31-44 : Spirometry measurements in post-bronchodilator test |
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- Column 45 : Test number |
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- 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). |
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- Column 46-57645 : Phase "Calibration #1". Flattened from [12,4800] by row-major order. |
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- Column 57646-115245 : Phase "Calibration #2". Flattened from [12,4800] by row-major order. |
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- Column 115246-172845 : Phase "Calibration #3". Flattened from [12,4800] by row-major order. |
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- Column 172846-292845 : Phase "Nasal breathing". Flattened from [25,4800] by row-major order. |
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- Column 292846-412845 : Phase "Inhale #1". Flattened from [25,4800] by row-major order. |
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- Column 412846-532845 : Phase "Inhale #2". Flattened from [25,4800] by row-major order. |
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- Column 532846-652845 : Phase "Inhale #3". Flattened from [25,4800] by row-major order. |
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- Column 652846-772845 : Phase "Exhale #1". Flattened from [25,4800] by row-major order. |
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- Column 772846-892845 : Phase "Exhale #2". Flattened from [25,4800] by row-major order. |
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- Column 892846-1012845 : Phase "Exhale #3". Flattened from [25,4800] by row-major order. |
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- Processed airway cross-sectional area (CSA) curves inferred from the acoustic signals |
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- "aware_csa.csv" |
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- 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 |
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- Column 0-45 : Basic subject information. Same as "aware_segmented.csv". |
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- Column 46-129: Reconstructed airway cross-sectional area at different depths from mouth. |
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For more information about this dataset please contact: [email protected] |
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Citation |
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=================================================================================================== |
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Use of this dataset in publications must be acknowledged by referencing the following publication: |
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|
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``` |
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@inproceedings{yin2023ptease, |
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title={PTEase: Objective Airway Examination for Pulmonary Telemedicine using Commodity Smartphones}, |
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author={Yin, Xiangyu and Huang, Kai and Forno, Erick and Chen, Wei and Huang, Heng and Gao, Wei}, |
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booktitle={Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services}, |
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pages={110--123}, |
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year={2023}, |
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url = {https://doi.org/10.1145/3581791.3596854}, |
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doi = {10.1145/3581791.3596854} |
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} |
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``` |
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Other Related Publications: |
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=================================================================================================== |
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``` |
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@inproceedings{yin2022out, |
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title={Out-Clinic Pulmonary Disease Evaluation via Acoustic Sensing and Multi-Task Learning on Commodity Smartphones}, |
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author={Yin, Xiangyu and Huang, Kai and Forno, Erick and Chen, Wei and Huang, Heng and Gao, Wei}, |
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booktitle={Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems}, |
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pages={1182--1188}, |
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year={2022}, |
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url = {https://doi.org/10.1145/3560905.3568437}, |
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doi = {10.1145/3560905.3568437} |
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} |
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``` |
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License |
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=================================================================================================== |
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[![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa] |
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|
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This work is licensed under a |
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[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. |
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[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] |
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[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png |
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[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg |
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