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- 01J8FDECB0NT0KVH7V609M0MYQ classification A1.
- 01J8FDECB0NT0KVH7V609M0MYQ date "2024".
- 01J8FDECB0NT0KVH7V609M0MYQ language "eng".
- 01J8FDECB0NT0KVH7V609M0MYQ type journalArticle.
- 01J8FDECB0NT0KVH7V609M0MYQ hasPart 01J8FDNATP2665ZF02P1MN1M4V.pdf.
- 01J8FDECB0NT0KVH7V609M0MYQ subject "Technology and Engineering".
- 01J8FDECB0NT0KVH7V609M0MYQ doi "10.3390/s24175544".
- 01J8FDECB0NT0KVH7V609M0MYQ issn "1424-8220".
- 01J8FDECB0NT0KVH7V609M0MYQ issue "17".
- 01J8FDECB0NT0KVH7V609M0MYQ volume "24".
- 01J8FDECB0NT0KVH7V609M0MYQ abstract "Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient eta using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast eta values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter eta, achieving an R2 of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast eta with an R2 of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring.".
- 01J8FDECB0NT0KVH7V609M0MYQ author 30a972b2-9784-11ed-bac3-dd73abd88931.
- 01J8FDECB0NT0KVH7V609M0MYQ author 88292577-c5ac-11ec-a948-846bba855e46.
- 01J8FDECB0NT0KVH7V609M0MYQ author 9347753b-3249-11ee-a7ea-df2862ac6552.
- 01J8FDECB0NT0KVH7V609M0MYQ author 9C5754C4-530B-11E8-AC68-92A811A95AF2.
- 01J8FDECB0NT0KVH7V609M0MYQ author C10E6192-1B01-11E2-A477-54B510BDE39D.
- 01J8FDECB0NT0KVH7V609M0MYQ author F6F803D2-F0ED-11E1-A9DE-61C894A0A6B4.
- 01J8FDECB0NT0KVH7V609M0MYQ author b13acc98-cb2c-11ec-887a-cc1b0132e2ed.
- 01J8FDECB0NT0KVH7V609M0MYQ author urn:uuid:ba5bdb11-7f8e-4a52-95af-6d2628f91eaa.
- 01J8FDECB0NT0KVH7V609M0MYQ author urn:uuid:c3723ac7-bf2c-4e9c-91da-d6fd73a659c8.
- 01J8FDECB0NT0KVH7V609M0MYQ author urn:uuid:e414cfe4-4835-4d1b-814e-1ef7304ddd59.
- 01J8FDECB0NT0KVH7V609M0MYQ dateCreated "2024-09-23T12:24:32Z".
- 01J8FDECB0NT0KVH7V609M0MYQ dateModified "2024-12-12T20:59:56Z".
- 01J8FDECB0NT0KVH7V609M0MYQ name "Artificial intelligence-driven prognosis of respiratory mechanics : forecasting tissue hysteresivity using long short-term memory and continuous sensor data".
- 01J8FDECB0NT0KVH7V609M0MYQ pagination urn:uuid:cf20b53c-b566-491f-b6a1-4c902b6dd92d.
- 01J8FDECB0NT0KVH7V609M0MYQ sameAs LU-01J8FDECB0NT0KVH7V609M0MYQ.
- 01J8FDECB0NT0KVH7V609M0MYQ sourceOrganization urn:uuid:80c989a0-3379-4362-b413-4ea429a31032.
- 01J8FDECB0NT0KVH7V609M0MYQ type A1.