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机译:使用卷积神经网络自动识别可电击和不可电击的危及生命的室性心律失常
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic,Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences,Department of Biomedical Engineering, Faculty of Engineering, University of Malaya;
Iwate Prefectural University (IPU), Faculty of Software and Information Science;
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic;
Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal University;
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic;
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic;
Department of Surgery, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center;
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic;
Automated external defibrillator (AED); ECG signals; Non-shockable; Shockable; Ventricular arrhythmias;
机译:使用具有焦损的深度可分离的卷积神经网络自动性心律失常分类
机译:使用卷积神经网络的不同间隔的不同间隔自动检测心律失常的心律失常
机译:基于超声心动图的级联卷积神经网络自动分割左心室心肌
机译:结合三个神经网络识别室上和室性心律失常
机译:卷积神经网络的图像基础心血管建模的自动分割和不确定量化
机译:具有优化超参数的全卷积深度神经网络用于检测可电击和不可电击的节律
机译:完全卷积的深神经网络,具有优化的近似参数,用于检测可触扰和不可震动的节奏
机译:迈向自动空中加油:利用卷积神经网络进行自动视觉飞行识别。