JP7304901B2 - 不整脈検出方法、装置、電子装置およびコンピュータ記憶媒体 - Google Patents

不整脈検出方法、装置、電子装置およびコンピュータ記憶媒体 Download PDF

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JP7304901B2
JP7304901B2 JP2020568775A JP2020568775A JP7304901B2 JP 7304901 B2 JP7304901 B2 JP 7304901B2 JP 2020568775 A JP2020568775 A JP 2020568775A JP 2020568775 A JP2020568775 A JP 2020568775A JP 7304901 B2 JP7304901 B2 JP 7304901B2
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arrhythmia
arrhythmia detection
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electrocardiogram signal
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啓航 姚
▲いぇ▼ 李
小毛 樊
云鵬 蔡
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Shenzhen Institute of Advanced Technology of CAS
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* Cited by examiner, † Cited by third party
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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
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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
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2017049684A (ja) 2015-08-31 2017-03-09 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
WO2017175434A1 (ja) 2016-04-06 2017-10-12 ソニー株式会社 情報処理装置、情報処理方法および情報提供方法
CN107516075A (zh) 2017-08-03 2017-12-26 安徽华米信息科技有限公司 心电信号的检测方法、装置及电子设备
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
JP2018073103A (ja) 2016-10-28 2018-05-10 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
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
WO2018119316A1 (en) 2016-12-21 2018-06-28 Emory University Methods and systems for determining abnormal cardiac activity
WO2018134952A1 (ja) 2017-01-19 2018-07-26 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

Family Cites Families (18)

* Cited by examiner, † Cited by third party
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
WO2017072250A1 (en) * 2015-10-27 2017-05-04 CardioLogs Technologies An automatic method to delineate or categorize an electrocardiogram
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
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
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

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2017049684A (ja) 2015-08-31 2017-03-09 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
WO2017175434A1 (ja) 2016-04-06 2017-10-12 ソニー株式会社 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
JP2018073103A (ja) 2016-10-28 2018-05-10 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
WO2018119316A1 (en) 2016-12-21 2018-06-28 Emory University Methods and systems for determining abnormal cardiac activity
WO2018134952A1 (ja) 2017-01-19 2018-07-26 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
CN107516075A (zh) 2017-08-03 2017-12-26 安徽华米信息科技有限公司 心电信号的检测方法、装置及电子设备
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

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Qihang Yao et al.，Time-Incremental Convolutional Neural Network for Arrhythmia Detection in Varied-Length Electrocardiogram，2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)，2018年08月12日，pp.754-761，DOI: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00131
Rasmus S. Andersen et al.，A deep learning approach for real-time detection of atrial fibrillation，Expert Systems With Applications，2018年08月14日，Volume 115，pp.465-473，DOI: 10.1016/j.eswa.2018.08.011
Shu Lih Oh et al.，Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats，Computers in Biology and Medicine，2018年06月05日，volume 102，pp.278-287，DOI: 10.1016/j.compbiomed.2018.06.002

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EP3847958A1 (en) 2021-07-14
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