US 12,340,906 B2
Noninvasive methods for detection of pulmonary hypertension
Tyler Wagner, Boston, MA (US); Samir Awasthi, Boston, MA (US); Venkataramanan Soundararajan, Andover, MA (US); Murali Aravamudan, Andover, MA (US); Corinne Carpenter, Cambridge, MA (US); Katherine Carlson, Cambridge, MA (US); Itzhak Zachi Attia, Rochester, MA (US); Paul A. Friedman, Rochester, MN (US); Samuel J. Asirvatham, Rochester, MN (US); Suraj Kapa, Rochester, MN (US); Francisco Lopez-Jimenez, Rochester, MN (US); and Hilary M. Dubrock, Rochester, MN (US)
Assigned to Anumana, Inc., Cambridge, MA (US)
Filed by Anumana, Inc., Cambridge, MA (US)
Filed on Oct. 13, 2021, as Appl. No. 17/500,287.
Claims priority of provisional application 63/091,715, filed on Oct. 14, 2020.
Prior Publication US 2022/0189634 A1, Jun. 16, 2022
Int. Cl. G16H 50/00 (2018.01); A61B 5/341 (2021.01); A61B 5/349 (2021.01); G16H 10/60 (2018.01); G16H 50/20 (2018.01)
CPC G16H 50/20 (2018.01) [A61B 5/341 (2021.01); A61B 5/349 (2021.01); G16H 10/60 (2018.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
receiving voltage-time data of a subject, the voltage-time data comprising an electrocardiogram (ECG) waveform of a plurality of leads of an electrocardiograph;
generating a plurality of feature vectors from the voltage-time data, wherein each of the feature vectors comprises a portion of the ECG waveform;
providing the plurality of the feature vectors to a pretrained learning system, wherein the pretrained learning system comprises:
a machine-learning model configured to:
generate a plurality of fixed size encodings from the plurality of feature vectors respectively; and
generate predictions based on the plurality of fixed size encodings;
receiving from the pretrained learning system an indication of a presence or absence of pulmonary hypertension in the subject based on the predictions; and
providing the indication to a computing node for display to a user.