KR102463764B1 - 부정맥 검출 방법, 장치, 전자장치 및 컴퓨터 기억 매체 - Google Patents
부정맥 검출 방법, 장치, 전자장치 및 컴퓨터 기억 매체 Download PDFInfo
- Publication number
- KR102463764B1 KR102463764B1 KR1020207035714A KR20207035714A KR102463764B1 KR 102463764 B1 KR102463764 B1 KR 102463764B1 KR 1020207035714 A KR1020207035714 A KR 1020207035714A KR 20207035714 A KR20207035714 A KR 20207035714A KR 102463764 B1 KR102463764 B1 KR 102463764B1
- Authority
- KR
- South Korea
- Prior art keywords
- signal
- arrhythmia
- arrhythmia detection
- neural network
- processing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
- A61B5/361—Detecting fibrillation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
- A61B5/363—Detecting tachycardia or bradycardia
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
-
- G06N3/0427—
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Cardiology (AREA)
- Public Health (AREA)
- Medical Informatics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Pathology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Heart & Thoracic Surgery (AREA)
- Veterinary Medicine (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Physiology (AREA)
- Psychiatry (AREA)
- Fuzzy Systems (AREA)
- Databases & Information Systems (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2018/104002 WO2020047750A1 (zh) | 2018-09-04 | 2018-09-04 | 心律失常的检测方法、装置、电子设备及计算机存储介质 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| KR20210037614A KR20210037614A (ko) | 2021-04-06 |
| KR102463764B1 true KR102463764B1 (ko) | 2022-11-03 |
Family
ID=69721489
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020207035714A Active KR102463764B1 (ko) | 2018-09-04 | 2018-09-04 | 부정맥 검출 방법, 장치, 전자장치 및 컴퓨터 기억 매체 |
Country Status (5)
| Country | Link |
|---|---|
| EP (1) | EP3847958A4 (https=) |
| JP (1) | JP7304901B2 (https=) |
| KR (1) | KR102463764B1 (https=) |
| CN (1) | CN111163690B (https=) |
| WO (1) | WO2020047750A1 (https=) |
Families Citing this family (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020166239A1 (ja) * | 2019-02-13 | 2020-08-20 | 国立大学法人京都大学 | 睡眠時無呼吸症候群判定装置、睡眠時無呼吸症候群判定方法、及び、睡眠時無呼吸症候群判定プログラム |
| CN111588349B (zh) * | 2020-05-28 | 2023-12-01 | 京东方科技集团股份有限公司 | 一种健康分析装置及电子设备 |
| CN112022142B (zh) * | 2020-08-07 | 2023-10-17 | 上海联影智能医疗科技有限公司 | 心电信号类型识别方法、装置及介质 |
| CN112001482B (zh) * | 2020-08-14 | 2024-05-24 | 佳都科技集团股份有限公司 | 振动预测及模型训练方法、装置、计算机设备和存储介质 |
| CN112070067B (zh) * | 2020-10-12 | 2023-11-21 | 乐普(北京)医疗器械股份有限公司 | 一种光体积描计信号的散点图分类方法和装置 |
| CN112464721A (zh) * | 2020-10-28 | 2021-03-09 | 中国石油天然气集团有限公司 | 微地震事件自动识别方法及装置 |
| CN112450942B (zh) * | 2020-11-26 | 2023-01-24 | 中国人民解放军南部战区总医院 | 心电信号的监测方法、系统、装置及介质 |
| CN114692667B (zh) * | 2020-12-30 | 2025-06-10 | 华为技术有限公司 | 一种模型训练方法及相关装置 |
| CN112818773A (zh) * | 2021-01-19 | 2021-05-18 | 青岛歌尔智能传感器有限公司 | 心率检测方法、设备及存储介质 |
| CN112597986B (zh) * | 2021-03-05 | 2021-06-08 | 腾讯科技(深圳)有限公司 | 生理电信号分类处理方法、装置、计算机设备和存储介质 |
| CN115316996B (zh) * | 2021-05-10 | 2024-10-18 | 广州视源电子科技股份有限公司 | 心律异常识别模型训练方法、装置、设备及存储介质 |
| KR102573059B1 (ko) * | 2021-05-13 | 2023-08-31 | 경북대학교 산학협력단 | 부정맥 판단 방법 및 장치, 그리고 이를 구현하기 위한 프로그램이 기록된 기록매체 |
| WO2022244291A1 (ja) * | 2021-05-21 | 2022-11-24 | 株式会社カルディオインテリジェンス | プログラム、出力装置及びデータ処理方法 |
| CN113349753A (zh) * | 2021-07-19 | 2021-09-07 | 成都芯跳医疗科技有限责任公司 | 一种基于便携式动态心电监护仪的心律失常检测方法 |
| CN113768514B (zh) * | 2021-08-09 | 2024-03-22 | 西安理工大学 | 基于卷积神经网络与门控循环单元的心律失常分类方法 |
| CN114359625B (zh) * | 2021-12-13 | 2025-03-18 | 重庆邮电大学 | 一种基于二维图像的深度学习心率失常分类方法 |
| CN114343665B (zh) * | 2021-12-31 | 2022-11-25 | 贵州省人民医院 | 一种基于图卷积空时特征融合选择的心律失常识别方法 |
| CN114587375B (zh) * | 2022-03-28 | 2024-12-20 | 联通(广东)产业互联网有限公司 | 心电图关键波段提取方法、设备和介质 |
| CN116942175B (zh) * | 2022-04-12 | 2026-01-30 | 广州视源电子科技股份有限公司 | 用于心电信号的特征波检测方法、装置、设备及存储介质 |
| EP4542573A4 (en) * | 2022-07-22 | 2025-05-28 | Medical AI Co., Ltd. | METHOD, PROGRAM AND APPARATUS FOR PREDICTING HEALTH STATE USING ELECTROCARDIOGRAM |
| KR102549010B1 (ko) * | 2022-08-30 | 2023-06-28 | 주식회사 휴이노 | 복합 인공 신경망을 이용하여 부정맥을 추정하기 위한 방법, 시스템 및 비일시성의 컴퓨터 판독 가능한 기록 매체 |
| CN115429284B (zh) * | 2022-09-16 | 2024-05-03 | 山东科技大学 | 心电信号分类方法、系统、计算机设备以及可读存储介质 |
| CN115708684A (zh) * | 2022-10-24 | 2023-02-24 | 卫软(江苏)科技有限公司 | 一种基于心电信息激活的心电监测方法及装置 |
| CN115624322B (zh) * | 2022-11-17 | 2023-04-25 | 北京科技大学 | 一种基于高效时空建模的非接触生理信号检测方法及系统 |
| CN117257324B (zh) * | 2023-11-22 | 2024-01-30 | 齐鲁工业大学(山东省科学院) | 基于卷积神经网络和ecg信号的房颤检测方法 |
| CN118845033A (zh) * | 2024-07-12 | 2024-10-29 | 重庆理工大学 | 基于改进的lstm增强心电图(ecg)分类方法 |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108039203A (zh) * | 2017-12-04 | 2018-05-15 | 北京医拍智能科技有限公司 | 基于深度神经网络的心律失常的检测系统 |
Family Cites Families (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08221378A (ja) * | 1995-02-10 | 1996-08-30 | Ricoh Co Ltd | 学習機械 |
| US20160189730A1 (en) * | 2014-12-30 | 2016-06-30 | Iflytek Co., Ltd. | Speech separation method and system |
| JP6678930B2 (ja) * | 2015-08-31 | 2020-04-15 | インターナショナル・ビジネス・マシーンズ・コーポレーションInternational Business Machines Corporation | 分類モデルを学習する方法、コンピュータ・システムおよびコンピュータ・プログラム |
| WO2017072250A1 (en) * | 2015-10-27 | 2017-05-04 | CardioLogs Technologies | An automatic method to delineate or categorize an electrocardiogram |
| JP6897673B2 (ja) * | 2016-04-06 | 2021-07-07 | ソニーグループ株式会社 | 情報処理装置、情報処理方法および情報提供方法 |
| JP2018005773A (ja) * | 2016-07-07 | 2018-01-11 | 株式会社リコー | 異常判定装置及び異常判定方法 |
| JP6945987B2 (ja) * | 2016-10-28 | 2021-10-06 | キヤノン株式会社 | 演算回路、その制御方法及びプログラム |
| CN106725426A (zh) * | 2016-12-14 | 2017-05-31 | 深圳先进技术研究院 | 一种心电信号分类的方法及系统 |
| EP3558101B1 (en) * | 2016-12-21 | 2022-06-08 | Emory University | Methods and systems for determining abnormal cardiac activity |
| JP6813033B2 (ja) * | 2017-01-19 | 2021-01-13 | 株式会社島津製作所 | 分析データ解析方法および分析データ解析装置 |
| CN106901723A (zh) * | 2017-04-20 | 2017-06-30 | 济南浪潮高新科技投资发展有限公司 | 一种心电图异常自动诊断方法 |
| CN107341452B (zh) * | 2017-06-20 | 2020-07-14 | 东北电力大学 | 基于四元数时空卷积神经网络的人体行为识别方法 |
| CN107562784A (zh) * | 2017-07-25 | 2018-01-09 | 同济大学 | 基于ResLCNN模型的短文本分类方法 |
| CN107516075B (zh) * | 2017-08-03 | 2020-10-09 | 安徽华米智能科技有限公司 | 心电信号的检测方法、装置及电子设备 |
| CN107943525A (zh) * | 2017-11-17 | 2018-04-20 | 魏茨怡 | 一种基于循环神经网络的手机app交互方式 |
| CN108095716B (zh) * | 2017-11-21 | 2020-11-06 | 河南工业大学 | 一种基于置信规则库和深度神经网络的心电信号检测方法 |
| CN107958044A (zh) * | 2017-11-24 | 2018-04-24 | 清华大学 | 基于深度时空记忆网络的高维序列数据预测方法和系统 |
| CN108030488A (zh) * | 2017-11-30 | 2018-05-15 | 北京医拍智能科技有限公司 | 基于卷积神经网络的心律失常的检测系统 |
| GB201720059D0 (en) * | 2017-12-01 | 2018-01-17 | Ucb Biopharma Sprl | Three-dimensional medical image analysis method and system for identification of vertebral fractures |
| CN107870306A (zh) * | 2017-12-11 | 2018-04-03 | 重庆邮电大学 | 一种基于深度神经网络下的锂电池荷电状态预测算法 |
| CN108073704B (zh) * | 2017-12-18 | 2020-07-14 | 清华大学 | 一种liwc词表扩展方法 |
| CN108108768B (zh) * | 2017-12-29 | 2020-09-25 | 清华大学 | 基于卷积神经网络的光伏玻璃缺陷分类方法及装置 |
| CN107961007A (zh) * | 2018-01-05 | 2018-04-27 | 重庆邮电大学 | 一种结合卷积神经网络和长短时记忆网络的脑电识别方法 |
| CN108418792B (zh) * | 2018-01-29 | 2020-12-22 | 华北电力大学 | 基于深度循环神经网络的网络逃避行为检测方法 |
| CN108255656B (zh) * | 2018-02-28 | 2020-12-22 | 湖州师范学院 | 一种应用于间歇过程的故障检测方法 |
-
2018
- 2018-09-04 WO PCT/CN2018/104002 patent/WO2020047750A1/zh not_active Ceased
- 2018-09-04 EP EP18932602.8A patent/EP3847958A4/en active Pending
- 2018-09-04 CN CN201880001770.4A patent/CN111163690B/zh active Active
- 2018-09-04 KR KR1020207035714A patent/KR102463764B1/ko active Active
- 2018-09-04 JP JP2020568775A patent/JP7304901B2/ja active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108039203A (zh) * | 2017-12-04 | 2018-05-15 | 北京医拍智能科技有限公司 | 基于深度神经网络的心律失常的检测系统 |
Also Published As
| Publication number | Publication date |
|---|---|
| JP7304901B2 (ja) | 2023-07-07 |
| KR20210037614A (ko) | 2021-04-06 |
| WO2020047750A1 (zh) | 2020-03-12 |
| CN111163690A (zh) | 2020-05-15 |
| CN111163690B (zh) | 2023-05-23 |
| EP3847958A1 (en) | 2021-07-14 |
| EP3847958A4 (en) | 2021-09-08 |
| JP2021526063A (ja) | 2021-09-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102463764B1 (ko) | 부정맥 검출 방법, 장치, 전자장치 및 컴퓨터 기억 매체 | |
| US12293287B2 (en) | Systems and methods of identity analysis of electrocardiograms | |
| CN111772619B (zh) | 一种基于深度学习的心搏识别方法、终端设备及存储介质 | |
| CN109948647B (zh) | 一种基于深度残差网络的心电图分类方法及系统 | |
| US10869610B2 (en) | System and method for identifying cardiac arrhythmias with deep neural networks | |
| CN109620205B (zh) | 心电数据分类方法、装置、计算机设备和存储介质 | |
| JP2021526063A5 (https=) | ||
| US20200394526A1 (en) | Method and apparatus for performing anomaly detection using neural network | |
| CN109009084B (zh) | 多导联心电信号的qrs波群校验方法、装置及设备、介质 | |
| US20170032221A1 (en) | Method, electronic apparatus, and computer readable medium of constructing classifier for disease detection | |
| CN112270212B (zh) | 基于多导联心电信号生成心搏标签数据序列的方法和装置 | |
| CN110141220A (zh) | 基于多模态融合神经网络的心肌梗死自动检测方法 | |
| CN118648883A (zh) | 双模血压计算方法、装置、设备及存储介质 | |
| CN111161883A (zh) | 基于变分自编码器的疾病预测系统及其电子设备 | |
| Al Rahhal et al. | Automatic premature ventricular contractions detection for multi-lead electrocardiogram signal | |
| John et al. | MLFusion: Multilevel data fusion using CNNs for atrial fibrillation detection | |
| CN116712056A (zh) | 心电图数据的特征图像生成与识别方法、设备及存储介质 | |
| Sakib et al. | Anomaly detection of ECG time series signal using auto encoders neural network | |
| KR20230072154A (ko) | 심장 질환을 판단하는 방법, 장치 및 컴퓨터 프로그램 제품 | |
| CN116458895B (zh) | 一种应用于心电图的子波形识别方法及系统 | |
| Kumar et al. | Non-invasive blood glucose estimation using a novel white-box model: An interpretable machine learning approach | |
| Lyozina et al. | Applying the Kohonen neural network to solve the problem of determining diseases of the cardiovascular system based on the results of electrocardiography | |
| CN117668604A (zh) | 一种自适应特征融合的生理信号识别方法及相关设备 | |
| CN112587146B (zh) | 基于改进损失函数的神经网络的心律类型识别方法和装置 | |
| CN116327211A (zh) | 基于可持续学习浅层循环神经网络的心电信号分类装置 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PA0105 | International application |
Patent event date: 20201211 Patent event code: PA01051R01D Comment text: International Patent Application |
|
| PA0201 | Request for examination |
Patent event code: PA02012R01D Patent event date: 20201211 Comment text: Request for Examination of Application |
|
| PG1501 | Laying open of application | ||
| E902 | Notification of reason for refusal | ||
| PE0902 | Notice of grounds for rejection |
Comment text: Notification of reason for refusal Patent event date: 20220418 Patent event code: PE09021S01D |
|
| E701 | Decision to grant or registration of patent right | ||
| PE0701 | Decision of registration |
Patent event code: PE07011S01D Comment text: Decision to Grant Registration Patent event date: 20221027 |
|
| GRNT | Written decision to grant | ||
| PR0701 | Registration of establishment |
Comment text: Registration of Establishment Patent event date: 20221101 Patent event code: PR07011E01D |
|
| PR1002 | Payment of registration fee |
Payment date: 20221101 End annual number: 3 Start annual number: 1 |
|
| PG1601 | Publication of registration |