DOI: 10.5061/dryad.v41ns1s8g
title: Data from: COPD screening using time–frequency features of self-recorded respiratory sounds

description: Chronic obstructive pulmonary disease (COPD) is the third leading cause of
 death worldwide, with up to 70% of cases remaining undiagnosed. This paper
 proposes a COPD screening tool based on time-frequency representation
 features of self-recorded respiratory sounds. Respiratory sound samples
 (breath and cough sounds) were extracted from COPD and asymptomatic
 non-COPD volunteers using a large, scientific-purpose database. We
 analysed 39 time-frequency representation features of breath and cough
 sounds, combined with age, sex, and smoking status, using Autoencoder
 neural networks and random forest algorithms. We compared the performance
 of different breath and cough random forest models built to detect COPD:
 based exclusively on sound features, based exclusively on sociodemographic
 characteristics, and based on sound features and sociodemographic
 characteristics. Models including breathing features outperformed models
 exclusively based on sociodemographic characteristics. Specifically, the
 model combining sociodemographic characteristics and breathing features
 achieved an AUC, accuracy, sensitivity, and specificity of 0.901, 0.836,
 0.871, and 0.761, respectively, in the test set, representing a
 substantial increase in AUC when compared to the model based exclusively
 on sociodemographic characteristics (0.901 vs. 0.818). Our results suggest
 that a lightweight collection of the time-frequency representation
 features of self-recorded breathing sounds could effectively improve the
 predictive performance of COPD screening or case-finding questionnaires.
 COPD screening through self-recorded breathing sounds could be easily
 integrated as a low-cost first step in case-finding programs, potentially
 contributing to mitigate COPD underdiagnosis.
description: # Data from: COPD screening using time–frequency features of self-recorded
 respiratory sounds Dataset DOI:
 [10.5061/dryad.v41ns1s8g](10.5061/dryad.v41ns1s8g) ## Description of the
 data and file structure This dataset, **record_data.csv**, is a new
 collection of time-frequency features derived from cough and respiration
 sound samples. The original sound samples were obtained from the
 proprietary **Covid-19 Sounds database**, a resource developed by
 Professor Cecilia Mascolo and her team in the Department of Computer
 Science and Technology at Cambridge University. We were granted permission
 to compute these features and share the resulting dataset. While the
 original sound data and associated sociodemographic information cannot be
 shared here due to proprietary rights held by Cambridge University, the
 data included in this repository can be used freely. For additional
 information about the original Covid-19 Sounds database or to request
 access to the raw sound data, please refer to the provided reference [1]
 and contact the University of Cambridge directly. The dataset is provided
 as a comma-separated values (CSV) file. Please note that any cells with a
 "null" value indicate missing data and were excluded from our
 analysis. \[1] Tong Xia^, Dimitris Spathis^, Chloë Brown*, Jagmohan
 Chauhan*, Andreas Grammenos*, Jing Han*, Apinan Hasthanasombat*, Erika
 Bondareva*, Ting Dang*, Andres Floto, Pietro Cicuta, Cecilia Mascolo.
 "COVID-19 Sounds: A Large-Scale Audio Dataset for Digital Respiratory
 Screening." Proceedings of the 35th Thirty-fifth Conference on Neural
 Information Processing Systems Datasets and Benchmarks Track (NeurIPS
 2021) **[\[PDF\]](https://openreview.net/pdf?id=9KArJb4r5ZQ)**.\ ^joint
 first authors, *equal contribution, alphabetical order ### Files and
 variables #### File: record_data.csv **Description:**  ##### Variables *
 f_Cres1: average of the Instantaneous Frequency Peak, calculated in the
 0-80Hz frequency band. * f_Cres2: average of the Instantaneous Frequency
 Peak, calculated in the 80-250Hz frequency band. * f_Cres3: average of the
 Instantaneous Frequency Peak, calculated in the 250-550Hz frequency band.
 * f_Cres4: average of the Instantaneous Frequency Peak, calculated in the
 550-900Hz frequency band. * f_Cres5: average of the Instantaneous
 Frequency Peak, calculated in the 900-1500Hz frequency band. * f_Cres6:
 average of the Instantaneous Frequency Peak, calculated in the 1500-3000Hz
 frequency band. * f_Cres7: average of the Instantaneous Frequency Peak,
 calculated in the 3000-4000Hz frequency band. * Enr_Bn1: average of the
 instantaneous spectral energy of each sample, calculated in the 0-80Hz
 frequency band. * Enr_Bn2: average of the instantaneous spectral energy of
 each sample, calculated in the 80-250Hz frequency band. * Enr_Bn3: average
 of the instantaneous spectral energy of each sample, calculated in the
 250-550Hz frequency band. * Enr_Bn4: average of the instantaneous spectral
 energy of each sample, calculated in the 550-900Hz frequency band. *
 Enr_Bn5: average of the instantaneous spectral energy of each sample,
 calculated in the 900-1500Hz frequency band. * Enr_Bn6: average of the
 instantaneous spectral energy of each sample, calculated in the
 1500-3000Hz frequency band. * Enr_Bn7: average of the instantaneous
 spectral energy of each sample, calculated in the 3000-4000Hz frequency
 band. * f_Med1: average of the instantaneous frequency, calculated in the
 0-80Hz frequency band. * f_Med2: average of the instantaneous frequency,
 calculated in the 80-250Hz frequency band. * f_Med3: average of the
 instantaneous frequency, calculated in the 250-550Hz frequency band. *
 f_Med4: average of the instantaneous frequency, calculated in the
 550-900Hz frequency band. * f_Med5: average of the instantaneous
 frequency, calculated in the 900-1500Hz frequency band. * f_Med6: average
 of the instantaneous frequency, calculated in the 1500-3000Hz frequency
 band. * f_Med7: average of the instantaneous frequency, calculated in the
 3000-4000Hz frequency band. * IE_Bn1: average of the spectral information,
 calculated in the 0-80Hz frequency band. * IE_Bn2: average of the spectral
 information, calculated in the 80-250Hz frequency band. * IE_Bn3: average
 of the spectral information, calculated in the 250-550Hz frequency band. *
 IE_Bn4: average of the spectral information, calculated in the 550-900Hz
 frequency band. * IE_Bn5: average of the spectral information, calculated
 in the 900-1500Hz frequency band. * IE_Bn6: average of the spectral
 information, calculated in the 1500-3000Hz frequency band. * IE_Bn7:
 average of the spectral information, calculated in the 3000-4000Hz
 frequency band. * H_tf: joint Shannon entropy in a range of 0 to 20 bits.
 * H_t: instantaneous entropy. * H_f: spectral entropy. * fm: average of
 the instantaneous frequencies across the entire spectrum. * k: kurtosis. *
 MomC_11: joint time-frequency moments (n = 1 and m = 1) * MomC_77: joint
 time-frequency moments (n = 7 and m = 7) * MomC_1515: joint time-frequency
 moments (n = 15 and m = 15) * MomM_11: joint moments of the marginal
 signals of the instantaneous power and spectral density (n = 1 and m = 1)
 * MomM_77: joint moments of the marginal signals of the instantaneous
 power and spectral density (n = 7 and m = 7) * MomM_1515: joint moments of
 the marginal signals of the instantaneous power and spectral density (n =
 15 and m = 15) Further details of the time-frequency features in the
 dataset can be found in the Online Supplementary file of the associated
 publication titled "COPD Screening Using Time–Frequency Features of
 Self-Recorded Respiratory Sounds" published in JAMIA Open. ## Access
 information Data was derived from the following sources: * Tong Xia^,
 Dimitris Spathis^, Chloë Brown*, Jagmohan Chauhan*, Andreas Grammenos*,
 Jing Han*, Apinan Hasthanasombat*, Erika Bondareva*, Ting Dang*, Andres
 Floto, Pietro Cicuta, Cecilia Mascolo. "COVID-19 Sounds: A
 Large-Scale Audio Dataset for Digital Respiratory Screening."
 Proceedings of the 35th Thirty-fifth Conference on Neural Information
 Processing Systems Datasets and Benchmarks Track (NeurIPS 2021)
 **[\[PDF\]](https://openreview.net/pdf?id=9KArJb4r5ZQ)**.\ ^joint first
 authors, *equal contribution, alphabetical order ## Human subjects data
 All human subject data included in this dataset have been de-identified in
 accordance with relevant privacy regulations. Identifying information has
 been removed or masked. No data in this dataset can reasonably be used to
 identify individuals.