{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T20:18:09Z","timestamp":1792009089479,"version":"4.4.0"},"reference-count":64,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T00:00:00Z","timestamp":1708560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Georgia Research Alliance (GRA)","award":["R03 EB029099"],"award-info":[{"award-number":["R03 EB029099"]}]},{"name":"Georgia Research Alliance (GRA)","award":["R03 EB029099"],"award-info":[{"award-number":["R03 EB029099"]}]},{"name":"National Institute of Health (NIH)","award":["R03 EB029099"],"award-info":[{"award-number":["R03 EB029099"]}]},{"name":"National Institute of Health (NIH)","award":["R03 EB029099"],"award-info":[{"award-number":["R03 EB029099"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient\u2019s chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time\u2013frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set\u2014outperforming the deterministic time\u2013frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously.<\/jats:p>","DOI":"10.3390\/bios14030118","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T08:34:35Z","timestamp":1708590875000},"page":"118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9054-8958","authenticated-orcid":false,"given":"Brian","family":"Sang","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Wen","sequence":"additional","affiliation":[{"name":"StethX Microsystems Inc., Atlanta, GA 30308, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Junek","sequence":"additional","affiliation":[{"name":"StethX Microsystems Inc., Atlanta, GA 30308, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wendy","family":"Neveu","sequence":"additional","affiliation":[{"name":"Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenzo","family":"Di Francesco","sequence":"additional","affiliation":[{"name":"Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farrokh","family":"Ayazi","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA"},{"name":"StethX Microsystems Inc., Atlanta, GA 30308, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s00281-020-00785-1","article-title":"Asthma epidemiology and risk factors","volume":"42","author":"Stern","year":"2020","journal-title":"Semin. 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