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DK3100675T3 - Fremgangsmåde og system til genkendelse af fysiologiske lyde - Google Patents

Fremgangsmåde og system til genkendelse af fysiologiske lyde Download PDF

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Publication number
DK3100675T3
DK3100675T3 DK16166727.4T DK16166727T DK3100675T3 DK 3100675 T3 DK3100675 T3 DK 3100675T3 DK 16166727 T DK16166727 T DK 16166727T DK 3100675 T3 DK3100675 T3 DK 3100675T3
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Denmark
Prior art keywords
module
heart
sounds
lung
sound
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DK16166727.4T
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English (en)
Inventor
Kun-Hsi Tsai
Yu Tsao
Shih-Hsuan Ku
Tzu-Chen Liang
Yun-Fan Chang
Shih-I Yang
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Imedi Plus Inc
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Publication of DK3100675T3 publication Critical patent/DK3100675T3/da

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Claims (15)

1. System til genkendelse af hjertelyde eller lungelyde, omfattende: et modtagelsesmodul (110) konfigureret til at modtage hjertelydene eller lungelydene; et kendetegnekstraheringsmodul (120) konfigureret til at ekstrahere mindst ét kendetegn fra hjertelydene eller lungelydene; og en klassifikator (130) konfigureret til at klassificere mindst ét kendetegn for at identificere mindst én kategori; hvor kendetegnekstraheringsmodulet (120) omfatter et stemmeaktivitetsdetektor-(VAD)-modul (121), et Mel-frekvenscepstrumkoefficient-(MFCC)-modul (122), og et K-means-algoritmemodul (123), hvor VAD-modulet (121) er konfigureret til at detektere mindst ét segment fra hjertelydene eller lungelydene, MFCC-modulet (122) er konfigureret til overføre det mindst ene segment til mindst én MFCC-kendetegnsvektor, og K-means-algoritmemodulet (123) er konfigureret til at finde mindst ét repræsentativt datapunkt fra den mindst ene MFCC-kendetegnsvektor for at identificere støj og hjertelydene eller lungelydene.
2. Systemet til genkendelse af hjertelyde eller lungelyde ifølge krav 1, hvor modtagelsesmodulet (110) er en fysiologisk optageindretning der konverterer et analogt signal af hjertelydene eller lungelydene til et digitalt signal af hjertelydene eller lungelydene.
3. Systemet til genkendelse af hjertelyde eller lungelyde ifølge krav 2, hvor den fysiologiske optageindretning er et elektronisk stetoskop.
4. Systemet til genkendelse af hjertelyde eller lungelyde ifølge krav 1, hvor klassifikatoren (130) omfatter et dybt neuralt netværks-(DNN)-modul (134).
5. Systemet til genkendelse af hjertelyde eller lungelyde ifølge krav 4, hvor DNN-modulet (134) omfatter tre skjulte lag, og hvert af de tre skjulte lag omfatter 100 neuroner.
6. Systemet til genkendelse af hjertelyde eller lungelyde ifølge krav 1, hvor VAD-modulet (121) er konfigureret til at detektere det mindst ene segment fra hjertelydene eller lungelydene baseret på lydenergiforskelle.
7. Systemet til genkendelse af hjertelyde eller lungelyde ifølge krav 1, hvor klassifikatoren (130) inkludereret K-nærmeste-nabo-(KNN)-modul (131), et Gaussisk blandingsmodel-(GMM)-modul (132), eller et støttevektormaskine-(SVM)-modul (133).
8. Systemet til genkendelse af hjertelyde eller lungelyde ifølge krav 1, hvor hjertelydene omfatter første hjertelyd (SI), anden hjertelyd (S2) eller en kombination deraf.
9. Systemet til genkendelse af hjertelyde eller lungelyde ifølge krav 1, hvor systemet yderligere omfatter en automatiseret ekstern defibrillator, en Holter-monitor, en kardio-pulmonal genoplivnings-(CPR)-maskine, en pacemaker, en implanterbar kardioverter-defibrillator (ICD), et elektrokardiogram (EKG), eller en ultralydsbølgeindretning.
10. Fremgangsmåde til genkendelse af hjertelyde eller lungelyde med systemet ifølge krav 1, omfattende: at modtage hjertelydene eller lungelydene med modtagelsesmodulet (110); at ekstrahere mindst ét kendetegn fra hjertelydene eller lungelydene med kendetegnekstraheringsmodulet (120); og at klassificere det mindst ene kendetegn for at identificere mindst én kategori med klassifikatoren (130); hvor ekstrahering af mindst ét kendetegn fra hjertelydene eller lungelydene med kendetegnekstraheringsmodulet (120) omfatter: at detektere mindst ét segment fra hjertelydene eller lungelydene med VAD-modulet (121); at overføre det mindst ene segment til mindst én MFCC-kendetegnsvektor med MFCC-modulet (122); at finde mindst ét repræsentativt datapunkt fra den mindst ene MFCC-kendetegnsvektor med K-means-algoritmemodulet (123) for at identificere støj og hjertelydene eller lungelydene.
11. Fremgangsmåden til genkendelse af hjertelyde eller lungelyde ifølge krav 10, hvor klassifikatoren (130) omfatter et dybt neuralt netværks-(DNN)-modul (134).
12. Fremgangsmåden til genkendelse af hjertelyde eller lungelyde ifølge krav 11, hvor DNN-modulet (134) omfatter tre skjulte lag, og hvert af de tre skjulte lag omfatter 100 neuroner.
13. Fremgangsmåden til genkendelse af hjertelyde eller lungelyde ifølge krav 10, hvor VAD-modulet (121) er konfigureret til at detektere det mindst ene segment fra hjertelydene eller lungelydene baseret på lydenergiforskelle.
14. Fremgangsmåden til genkendelse af hjertelyde eller lungelyde ifølge krav 10, hvor klassifikatoren (130) inkluderer et KNN-modul (131), et GMM-modul (132), eller et SVM-modul (133).
15. Fremgangsmåden til genkendelse af hjertelyde eller lungelyde ifølge krav 10, hvor hjertelydene omfatter første hjertelyd (SI), anden hjertelyd (S2) eller en kombination deraf.
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Families Citing this family (52)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TW201700063A (zh) * 2015-06-22 2017-01-01 Jon-Chao Hong 心肺音異常處理方法及系統
WO2018052446A1 (en) * 2016-09-19 2018-03-22 Siemens Aktiengesellschaft Critical infrastructure forensics
US10159459B2 (en) * 2016-11-11 2018-12-25 iMEDI PLUS Inc. Multi-mic sound collector and system and method for sound localization
CN108652658A (zh) * 2017-03-31 2018-10-16 京东方科技集团股份有限公司 爆裂音识别方法及系统
CN107280674A (zh) * 2017-06-02 2017-10-24 南京理工大学 基于音频录取设备的呼吸模式判决方法
CN107292286B (zh) * 2017-07-14 2021-10-22 中国科学院苏州生物医学工程技术研究所 基于机器学习的呼吸音鉴别方法及系统
WO2019023879A1 (zh) * 2017-07-31 2019-02-07 深圳和而泰智能家居科技有限公司 咳嗽声音识别方法、设备和存储介质
TWI622980B (zh) * 2017-09-05 2018-05-01 醫療財團法人徐元智先生醫藥基金會亞東紀念醫院 嗓音疾病偵測分類系統
CN110021435A (zh) * 2017-09-07 2019-07-16 医疗财团法人徐元智先生医药基金会亚东纪念医院 嗓音侦测分类系统
CN108053841A (zh) * 2017-10-23 2018-05-18 平安科技（深圳）有限公司 利用语音进行疾病预测的方法及应用服务器
WO2019079972A1 (zh) * 2017-10-24 2019-05-02 深圳和而泰智能控制股份有限公司 特定声音识别方法、设备和存储介质
CN108175426B (zh) * 2017-12-11 2020-06-02 东南大学 一种基于深度递归型条件受限玻尔兹曼机的测谎方法
CN107811649B (zh) * 2017-12-13 2020-12-22 四川大学 一种基于深度卷积神经网络的心音多分类方法
CA3083639C (en) * 2017-12-14 2023-04-04 Salcit Technologies Private Limited Method and system for analyzing risk associated with respiratory sounds
US10828009B2 (en) * 2017-12-20 2020-11-10 International Business Machines Corporation Monitoring body sounds and detecting health conditions
CN108143407A (zh) * 2017-12-25 2018-06-12 四川大学 一种自动提取心音包络特征的心音分段方法
CN108417225A (zh) * 2018-02-11 2018-08-17 广州市碳码科技有限责任公司 一种呼吸音监测方法、装置、终端及计算机可读存储介质
CN108420454B (zh) * 2018-02-26 2021-11-26 河南善仁医疗科技有限公司 基于多路心音的心音分裂识别方法
CN108550394A (zh) * 2018-03-12 2018-09-18 广州势必可赢网络科技有限公司 一种基于声纹识别的诊病方法及装置
CN108831486B (zh) * 2018-05-25 2023-06-02 南京邮电大学 基于dnn与gmm模型的说话人识别方法
CN108962389A (zh) * 2018-06-21 2018-12-07 上海掌门科技有限公司 用于风险提示的方法及系统
CN108937866B (zh) * 2018-06-29 2020-03-20 出门问问信息科技有限公司 睡眠状态的监测方法及装置
CN112399819B (zh) 2018-07-02 2024-08-23 3M创新有限公司 用于监测时间相关过程的传感系统和方法
CN108935188A (zh) * 2018-07-05 2018-12-07 平安科技（深圳）有限公司 猪只疾病识别方法、装置及电子设备
EP3608918B1 (en) * 2018-08-08 2024-05-22 Tata Consultancy Services Limited Parallel implementation of deep neural networks for classifying heart sound signals
CN109357749B (zh) * 2018-09-04 2020-12-04 南京理工大学 一种基于dnn算法的电力设备音频信号分析方法
WO2020077413A1 (en) * 2018-10-17 2020-04-23 The University Of Queensland A method and apparatus for diagnosis of maladies from patient sounds
CN109431517A (zh) * 2018-11-13 2019-03-08 四川长虹电器股份有限公司 一种基于心音的身份识别方法
TWI687206B (zh) * 2018-12-06 2020-03-11 財團法人工業技術研究院 異常肺音標記的方法及裝置
CN109893161A (zh) * 2019-03-12 2019-06-18 南京大学 一种基于改进梅尔非线性频段划分的心音信号特征提取方法
CN110179492B (zh) * 2019-05-08 2021-12-14 四川新源生物电子科技有限公司 基于自适应双阈值的肠鸣音智能识别算法
CN110432924B (zh) * 2019-08-06 2021-10-22 杭州智团信息技术有限公司 肠鸣音检测装置、方法和电子设备
CN110517783B (zh) * 2019-09-18 2023-02-03 东软集团股份有限公司 模型训练方法、装置以及心脏疾病诊断装置、设备、介质
CN111712874B (zh) * 2019-10-31 2023-07-14 支付宝(杭州)信息技术有限公司 用于确定声音特性的方法、系统、装置和存储介质
CN110970042B (zh) * 2019-12-13 2023-04-18 苏州美糯爱医疗科技有限公司 一种电子听诊器的肺部啰音人工智能实时分类方法、系统、装置及可读存储介质
CN112017695A (zh) * 2020-03-04 2020-12-01 上海交通大学医学院附属上海儿童医学中心 一种自动识别生理声音的系统及方法
BR112022024759A2 (pt) * 2020-06-04 2022-12-27 Entac Medical Inc Dispositivos e métodos para previsão in vivo de comprometimentos e eventos funcionais
US20220031256A1 (en) * 2020-07-31 2022-02-03 3M Innovative Properties Company Composite phonocardiogram visualization on an electronic stethoscope display
CN111956259B (zh) * 2020-08-13 2021-07-06 厦门大学 基于深度神经网络架构的肺音识别方法、装置和设备
CN112370072B (zh) * 2020-11-13 2022-09-16 深圳市凯沃尔电子有限公司 一种听诊信号处理系统
TWI749880B (zh) * 2020-11-19 2021-12-11 緯創資通股份有限公司 生理病徵識別方法及生理病徵感測系統
CN114680867A (zh) * 2020-12-30 2022-07-01 陈之硕 呼吸功能检测方法及装置
TWI848197B (zh) * 2020-12-30 2024-07-11 國立清華大學 呼吸功能檢測方法及裝置
KR102287081B1 (ko) * 2021-01-06 2021-08-06 김도형 휴대용 디지털 청진기를 활용한 의료 서비스 관리 및 제공 장치
RU2766751C1 (ru) * 2021-03-05 2022-03-15 Федеральное государственное бюджетное образовательное учреждение высшего образования «Юго-Западный государственный университет» (ЮЗГУ) (RU) Способ диагностики астматического бронхита в процессе аускультации легких взрослых людей
US12482560B2 (en) 2021-04-23 2025-11-25 Lasarrus Clinic And Research Center Energy-efficient collection of wearable sensor data
CN113449636B (zh) * 2021-06-28 2024-03-12 苏州美糯爱医疗科技有限公司 一种基于人工智能的主动脉瓣狭窄严重程度自动分类方法
US12476006B2 (en) 2021-07-10 2025-11-18 Lassarrus Clinic And Research Center Inc. Smart multi-modal telehealth-IoT system for respiratory analysis
US12148525B2 (en) 2021-09-04 2024-11-19 Lasarrus Clinic And Research Center Inc. Wearable multi-modal system for remote monitoring of patients with chronic obstructive pulmonary disease
CN113948108B (zh) * 2021-10-09 2023-03-21 广州蓝仕威克软件开发有限公司 一种自动识别生理声音的方法及系统
CN114391807B (zh) * 2021-12-17 2023-12-19 珠海脉动时代健康科技有限公司 睡眠呼吸障碍分析方法、装置、设备及可读介质
CN116807404A (zh) * 2023-06-16 2023-09-29 清春智富(苏州)高新科技有限公司 基于巡诊机器人的听诊信号智能识别方法及设备

Family Cites Families (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5218969A (en) * 1988-02-04 1993-06-15 Blood Line Technology, Inc. Intelligent stethoscope
US6056703A (en) * 1996-04-03 2000-05-02 Rush Presbyterian-St Luke's Medical Center Method and apparatus for characterizing gastrointestinal sounds
EP1667579A4 (en) * 2003-09-12 2008-06-11 Bodymedia Inc METHOD AND DEVICE FOR MEASURING CARDIAC PARAMETERS
US20060167385A1 (en) * 2005-01-24 2006-07-27 3M Innovative Properties Company Analysis of auscultatory sounds using voice recognition
US8323189B2 (en) * 2006-05-12 2012-12-04 Bao Tran Health monitoring appliance
CN100418480C (zh) * 2006-05-16 2008-09-17 清华大学深圳研究生院 基于心音分析的心脏病自动分类系统及其心音分段方法
US7479115B2 (en) * 2006-08-25 2009-01-20 Savic Research, Llc Computer aided diagnosis of lung disease
US20100094152A1 (en) * 2006-09-22 2010-04-15 John Semmlow System and method for acoustic detection of coronary artery disease
WO2011062071A1 (ja) * 2009-11-19 2011-05-26 日本電気株式会社 音響画像区間分類装置および方法
KR101191667B1 (ko) * 2010-05-20 2012-10-17 충북대학교 산학협력단 시간 영역 특징을 이용한 심장 질환 진단 장치
CN101930734B (zh) * 2010-07-29 2012-05-23 重庆大学 一种心音信号分类识别方法及装置
EP2636369A4 (en) * 2010-11-04 2014-09-24 Panasonic Corp BIOMETRIC SOUND MEASURING DEVICE AND BIOMETRIC MEASUREMENT METHOD
EP2651291A2 (en) * 2010-12-15 2013-10-23 Cardiac Pacemakers, Inc. Cardiac decompensation detection using multiple sensors
US20120209131A1 (en) * 2011-02-11 2012-08-16 AventuSoft, LLC Method and System of a Cardio-acoustic Classification system for Screening, Diagnosis and Monitoring of Cardiovascular Conditions
US20130237872A1 (en) * 2012-03-12 2013-09-12 Xusheng Zhang Heart sound sensing to reduce inappropriate tachyarrhythmia therapy
US10098569B2 (en) * 2012-03-29 2018-10-16 The University Of Queensland Method and apparatus for processing patient sounds
CN102697520B (zh) * 2012-05-08 2014-10-29 天津沃康科技有限公司 基于智能识别功能的电子听诊器
CN102934989A (zh) * 2012-12-05 2013-02-20 隋聪 基于神经网络的心音识别装置及方法
CN105393252B (zh) * 2013-04-18 2019-04-19 数字标记公司 生理数据采集和分析
US10013652B2 (en) * 2015-04-29 2018-07-03 Nuance Communications, Inc. Fast deep neural network feature transformation via optimized memory bandwidth utilization

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