Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram

作     者：Vinay Arora Karun Verma Rohan Singh Leekha Kyungroul Lee Chang Choi Takshi Gupta Kashish Bhatia 

作者机构：Department of Computer Science and EngineeringThapar Institute of Engineering and TechnologyPatialaPunjabIndia Associate ApplicationITConcentrixGurugramHaryanaIndia School of Computer SoftwareDaegu Catholic UniversityGyeongsanKorea Department of Computer EngineeringGachon UniversitySeongnam13120Korea Information Security EngineeringSoonchunhyang UniversityKorea Department of Computer EngineeringUniversity College of EngineeringPunjabi UniversityPatialaPunjabIndia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第12期

页      面：4151-4168页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 0809[工学-电子科学与技术（可授工学、理学学位）] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 0805[工学-材料科学与工程（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：This work was supported by the National Research Foundation of Korea(NRF)Grant Funded by the Korea government(Ministry of Science and ICT)(No.2017R1E1A1A01077913) by the Institute of Information&Communications Technology Planning&Evaluation(IITP)funded by the Korea Government(MSIT)(Development of Smart Signage Technology for Automatic Classification of Untact Examination and Patient Status Based on AI)under Grant 2020-0-01907 

主　　题：PCG signals transfer learning repeating pattern-based spectrogram biomedical signals internet of things(IoT) 

摘      要：The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension,irregular cardiac functioning,and heart ***-based learning of heart sound is an efficient technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac ***(PCG)and electrocardiogram(ECG)waveforms provide the much-needed information for the diagnosis of these *** this work,the researchers have converted the heart sound signal into its corresponding repeating pattern-based *** 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform *** existing models,***,Xception,Visual Geometry Group(VGG16),ResNet,DenseNet,and InceptionV3 of Transfer Learning have been used for classifying the heart sound signals as normal and *** PhysioNet 2016,DenseNet has outperformed its peer models with an accuracy of 89.04 percent,whereas for PASCAL 2011,VGG has outperformed its peer approaches with an accuracy of 92.96 percent.