US 12,453,527 B2
Lung sound analysis system
Masao Higuchi, Tokyo (JP); Mitsuru Noma, Tokyo (JP); Reishi Kondo, Tokyo (JP); and Yumi Arai, Tokyo (JP)
Assigned to NEC CORPORATION, Tokyo (JP)
Appl. No. 18/020,362
Filed by NEC Corporation, Tokyo (JP)
PCT Filed Aug. 25, 2020, PCT No. PCT/JP2020/032054
§ 371(c)(1), (2) Date Feb. 8, 2023,
PCT Pub. No. WO2022/044126, PCT Pub. Date Mar. 3, 2022.
Prior Publication US 2023/0293136 A1, Sep. 21, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. A61B 7/00 (2006.01); A61B 5/00 (2006.01)
CPC A61B 7/003 (2013.01) [A61B 5/7203 (2013.01)] 13 Claims
OG exemplary drawing
 
1. A lung sound analysis device comprising:
a memory containing program instructions; and
a processor coupled to the memory, wherein the processor is configured to execute the program instructions to:
acquire time-series acoustic signals including lung sounds of a subject who is a heart failure patient;
determine a pause phase of breathing of the subject;
divide the time-series acoustic signals into time-series acoustic signals in a period of the pause phase of breathing of the subject and time-series acoustic signals in a period other than the pause phase, according to a result of the determination; and
determine abnormality in the lung sounds from the time-series acoustic signals in the period other than the pause phase after the division, wherein
the processor is further configured to execute the instructions to:
in the determining abnormality,
input the lung sounds of the subject into a first normal model trained using lung sounds data with lung sounds abnormalities of the subject;
acquire a probability that the lung sounds of the subject are abnormal lung sounds from the first normal model;
determine that the lung sounds of the subject are a same type of lung sounds abnormalities as the lung sounds data used for training the first normal model based on the probability exceeding a first threshold; and
determine that the lung sounds of the subject are either a different type of lung sounds abnormalities from the lung sound data used for training the first normal model or normal lung sounds based on the probability being equal to or less than the first threshold.