Abstract
With the prevalence of smartphones and smartwatches during the last decade, ambulatory audio-based monitoring of lung function has become more common. A symptom that provides significant information for determining the pulmonary patients’ state is cough. This work proposes a novel algorithm to detect the cough features such as frequency, type, and intensity, in a privacy preserving manner using readily-available mobile devices for daily monitoring. The cough classification algorithm consists of three main modules. Audio events will be detected in the first layer after pre-processing of raw audio data. Then, a random forest algorithm is applied to classify the event as cough, speech, or none for each of the time frames. Lastly, a majority voting scheme is applied on the output of the second layer for each time window (which consists of multiple time frames) to determine the final label for that window. The implemented sound event detector has detection error of less than 3% in an indoor situation and cough/speech/none classification reaches 96% accuracy using random forest algorithm. The system benefits from a privacy preservation algorithm (proposed in our previous work) to make the speech unintelligible while keeping the important cough features recognizable. The privacy preservation algorithm reduces the accuracy of classification by 11% (8% for cough and speech labels) which is not significant comparing to the benefit it delivers.
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References
American Lung Association. How serious is COPD. http://www.lung.org/lung-health-and-diseases/lung-disease-lookup/copd/learn-about-copd/how-serious-is-copd.html. Accessed 29 May 2018
Environmental Protection Agency. Asthma Facts. https://www.epa.gov/sites/production/files/2018-05/documents/asthma_fact_sheet_0.pdf. Accessed 29 May 2018
Nemati, E., Liaqat, D., Rahman, M.M., Kuang, J.: A novel algorithm for activity state recognition using smartwatch data. In Healthcare Innovations and Point of Care Technologies (HI-POCT), 2017 IEEE (pp. 18–21). IEEE, 2017
Nemati, E., Suh, Y.S., Motamed, B. and Sarrafzadeh, M.: Gait velocity estimation for a smartwatch platform using kalman filter peak recovery. In Wearable and Implantable Body Sensor Networks (BSN), 2016 IEEE 13th International Conference on (pp. 230–235). IEEE, 2016
Tehrany, R. M.: Speech breathing patterns in health and chronic respiratory disease. 2015
Miravitlles, M.: Cough and sputum production as risk factors for poor outcomes in patients with copd. Respir. Med. 105(8), 1118–1128 (2011)
Burgel, P.-R., Nesme-Meyer, P., Chanez, P., Caillaud, D., Carre, P., Perez, T., Roche, N.: Cough and sputum production are associated with frequent exacerbations and hospitalizations in copd subjects. Chest. 135(4), 975–982 (2009)
Dicpinigaitis, P.V.: Chronic cough due to asthma: ACCP evidence-based clinical practice guidelines. Chest. 129(1), 75S–79S (2006)
Shi, Y., Liu, H., Wang, Y., Cai, M., Xu, W.: Theory and application of audio-based assessment of cough. Hindawi J Sensors, 2018, 9845321, 10 pages, (2018)
Matos, S., Birring, S.S., Pavord, I.D., Evans, D.H.: An automated system for 24-h monitoring of cough frequency: the Leicester cough monitor. IEEE Trans. on Biomed. Eng. 54, 1472–1479 (2007)
McGuinness, K., Kelsall, A., Lowe, J., Woodcock, A., Smith, J. A.: Automated cough detection: a novel approach. J Resp Crit Care Med (2007)
Amoh, J., Odame, K.: Deep neural networks for identifying cough sounds. IEEE TCAS (2016)
Birring, S., Fleming, T., Matos, S., et al.: The Leicester cough monitor: Preliminary validation of an automated cough detection system in chronic cough. Eur. Respir. (2008)
Barry, S., Dane, A., Morice, A., Walmsley, A.: The automatic recognition and counting of cough. Cough J. 2, 8, (2006)
Swarnkar, V., Abeyratne, U.R., Amrulloh, Y., Hukins, C., Triasih, R., Setyati, A.: Neural network based algorithm for automatic identification of cough sounds. IEEE EMBC. 2013, 1764–1767 (2013)
Sun, X., et al.: SymDetector: detecting sound-related respiratory symptoms using smartphones. In: Proceedings of ACM International Joint Conference on PerComp and UbiComp (2015)
Lane, N.D., Georgiev, P., Qendro, L.: DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 283–294). ACM (2015)
Korpas, J., Sadlonova, J., Vrabec, M.: Analysis of the cough sound: an overview. Pulm. Pharmacol. 261–268 (1996)
Zephyr HomePage. http://www.zephyranywhere.com/. Accessed 17 May 2018
Sheikh, Naveed A., and Debra, A.: Titone. “Sensorimotor and linguistic information attenuate emotional word processing benefits: An eye-movement study.” Emotion (2013)
Eyben, F., Weninger, F., Gross, B.: Schuller Recent developments in openSMILE the munich open-source multimedia feature extractor ACM Press pp. 835–838 (2013)
Liaqat, D., Nemati, E., Rahman, M., Kuang, J.: A method for preserving privacy during audio recordings by filtering speech. In Life Sciences Conference (LSC), 2017 IEEE (pp. 79–82). IEEE, 2017
Rahman, M., Nemati, E., Nathan, V. and Kuang, J., Instant, R.R.: Instantaneous Respiratory Rate Estimation on Context-aware Mobile Devices. In EAI BodyNets, (2018)
Nemati, E., Sideris, K., Kalantarian, H., Sarrafzadeh, M.: A dynamic data source selection system for smartwatch platform. In Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the (pp. 5993–5996). IEEE, 2016
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Nemati, E., Rahman, M.M., Nathan, V., Kuang, J. (2020). Private Audio-Based Cough Sensing for In-Home Pulmonary Assessment Using Mobile Devices. In: Sugimoto, C., Farhadi, H., Hämäläinen, M. (eds) 13th EAI International Conference on Body Area Networks . BODYNETS 2018. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-29897-5_18
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