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--- |
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license: mit |
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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/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 designed to examine human airway's internal physiological conditions. AWARE/PTEase probes the airway with acoustic pulses through mouth, and collect the airway's reflections for analysis. Please refer to our paper[1,2] for more details. |
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This dataset includes the raw and pre-processed acoustic data, arranged in 3 parts: |
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- "aware_raw": Raw .wav files |
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- Subfolders are named by the subjects' ID (from 1 to 382) |
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- 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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- "aware_segmented": Segmented and aligned acoustic pulses |
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- "aware_segmented.csv" and "headers.txt" |
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- Column 1-46 : Basic subject information, including demographics, clinical measurements and labels. See "headers.txt". |
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- Column 1 : Subject ID |
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- Column 2-10 : Demographics |
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- Column 11 : Disease label |
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- Column 12-22 : Impulse Oscillometry Measurements |
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- Column 23-31 : Spirometry measurements |
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- Column 32-45 : Spirometry measurements in post-bronchodilator test |
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- Column 46 : Test number |
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- Column 47-1012846 : 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 last for 0.1 sec (4800 sample points). |
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- Column 47-57646 : Phase "Calibration #1". Flattend from [12,4800] by row-major order. |
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- Column 57647-115246 : Phase "Calibration #2". Flattend from [12,4800] by row-major order. |
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- Column 115247-172846 : Phase "Calibration #3". Flattend from [12,4800] by row-major order. |
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- Column 172847-292846 : Phase "Nasal breathing". Flattend from [25,4800] by row-major order. |
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- Column 292847-412846 : Phase "Inhale #1". Flattend from [25,4800] by row-major order. |
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- Column 412847-532846 : Phase "Inhale #2". Flattend from [25,4800] by row-major order. |
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- Column 532847-652846 : Phase "Inhale #3". Flattend from [25,4800] by row-major order. |
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- Column 652847-772846 : Phase "Exhale #1". Flattend from [25,4800] by row-major order. |
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- Column 772847-892846 : Phase "Exhale #2". Flattend from [25,4800] by row-major order. |
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- Column 892847-1012846 : Phase "Exhale #3". Flattend from [25,4800] by row-major order. |
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- "aware_segmented.pkl" |
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- Python pandas.DataFrame saved as pickle file. Same contents as "aware_segmented.csv", but without flattening. |
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- To load the data, use the following commends: |
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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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- "aware_csa": Processed airway cross-sectional area (CSA) curves infered from the acoustic signals |
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- NOTE: Not all raw samples are used for airway CSA reconsturctions. Low-quality data is discarded, thus the sample size of this part is smaller than above |
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- "aware_csa.csv" and "headers.txt" |
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Column 1-46 : Basic subject information. Same as "aware_segmented.csv". See "headers.txt". |
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Column 47-130: Reconstructed airway cross-sectional area at different depth from mouth. |
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For more information about this dataset please contact: eric.yin@pitt.edu |
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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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@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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``` |
