US 12,440,150 B2
Speech-based pulmonary assessment
Korosh Vatanparvar, Santa Clara, CA (US); Viswam Nathan, Fresno, CA (US); Ebrahim Nematihosseinabadi, Santa Clara, CA (US); Md Mahbubur Rahman, San Jose, CA (US); Tousif Ahmed, San Jose, CA (US); Jilong Kuang, San Jose, CA (US); and Jun Gao, Menlo Park, CA (US)
Assigned to SAMSUNG ELECTRONICS CO., LTD., Gyeonggi-Do (KR)
Filed by SAMSUNG ELECTRONICS CO., LTD, Gyeonggi-Do (KR)
Filed on Feb. 4, 2022, as Appl. No. 17/592,777.
Claims priority of provisional application 63/148,276, filed on Feb. 11, 2021.
Prior Publication US 2022/0257175 A1, Aug. 18, 2022
Int. Cl. A61B 5/00 (2006.01); A61B 5/024 (2006.01); A61B 5/08 (2006.01); G10L 25/66 (2013.01)
CPC A61B 5/4803 (2013.01) [A61B 5/0022 (2013.01); A61B 5/024 (2013.01); A61B 5/0816 (2013.01); G10L 25/66 (2013.01); A61B 2562/0204 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-based method, comprising:
extracting from a user's speech, using computer hardware, audio features including Mel-frequency cepstral coefficients (MFCCs) specifying speech patterns of the user's speech;
calculating, by the computer hardware, a metric specifying cognitive burden associated with the user's speech based on at least one of complexity of words or grammatical structure of the user's speech; and
determining, by the computer hardware, a pulmonary condition of the user by processing the audio features and the metric of cognitive burden through a predictive model including one or more first convolutional neural network layers trained to extract spatiotemporal features pertaining to airway anomalies and one or more second long short-term memory layers trained to correlate the spatiotemporal features with lung functions.