	US 12,390,149 B2
	Explainable CNN-attention network (C-attention network) architecture for automated detection of Alzheimer's disease
Koduvayur P. Subbalakshmi, Holmdel, NJ (US); Mingxuan Chen, Jersey City, NJ (US); and Ning Wang, Jersey City, NJ (US)
Assigned to The Trustees of the Stevens Institute of Technology, Hoboken, NJ (US)
Appl. No. 18/022,981
Filed by THE TRUSTEES OF THE STEVENS INSTITUTE OF TECHNOLOGY, Hoboken, NJ (US)
PCT Filed Aug. 24, 2021, PCT No. PCT/US2021/047381 § 371(c)(1), (2) Date Feb. 23, 2023, PCT Pub. No. WO2022/046793, PCT Pub. Date Mar. 3, 2022.
Claims priority of provisional application 63/069,628, filed on Aug. 24, 2020.
Prior Publication US 2024/0023876 A1, Jan. 25, 2024
Int. Cl. A61B 5/00 (2006.01)

CPC A61B 5/4088 (2013.01) [A61B 5/4803 (2013.01); A61B 5/7264 (2013.01)]

27 Claims

1. A method of speech analysis for medical diagnostics, comprising the steps of:

a) obtaining a speech sample from a patient;

b) classifying said speech sample with a plurality of parts of speech tags;

c) applying a self-attention module to said plurality of parts of speech tags;

d) applying an attention layer to said plurality of parts of speech tags;

e) applying a convolution layer to said plurality of parts of speech tags;

f) applying a softmax layer to said plurality of parts of speech tags;

g) generating an intra-class explanation of said speech sample from data derived from the performance of steps c) through f); and

h) determining a diagnosis of a medical condition.