Reliable Cardiovascular Disease (CVD) classification performed by a smart system can assist medical doctors in recognizing heart illnesses in patients more efficiently and effectively. Electrocardiogram (ECG) signals are an important diagnostic tool as they are already available early in the patients’ health diagnosis process and contain valuable indicators for various CVDs. Most ECG processing methods represent ECG data as a time series, often as a matrix with each row containing the measurements of a sensor lead; and/or the transforms of such time series like wavelet power spectrums. While methods processing such time-series data have been shown to work well in benchmarks, they are still highly dependent on factors like input noise and sequence length, and cannot always correlate lead data from different sensors well. In this paper, we propose to represent ECG signals incorporating all lead data plotted as a single image, an approach not yet explored by literature. We will show that such an image representation combined with our newly proposed convolutional neural network specifically designed for CVD classification can overcome the aforementioned shortcomings. The proposed (Convolutional Neural Network) CNN is designed to extract features representing both the proportional relationships of different leads to each other and the characteristics of each lead separately. Empirical validation on the publicly available PTB, MIT-BIH, and St.-Petersburg benchmark databases shows that the proposed method outperforms time seriesbased state-of-the-art approaches, yielding classification accuracy of 97.91%, 99.62%, and 98.70%, respectively.
{"title":"ImECGnet: Cardiovascular Disease Classification from Image-Based ECG Data Using a Multibranch Convolutional Neural Network","authors":"Amir Ghahremani, C. Lofi","doi":"10.18178/joig.11.1.9-14","DOIUrl":"https://doi.org/10.18178/joig.11.1.9-14","url":null,"abstract":"Reliable Cardiovascular Disease (CVD) classification performed by a smart system can assist medical doctors in recognizing heart illnesses in patients more efficiently and effectively. Electrocardiogram (ECG) signals are an important diagnostic tool as they are already available early in the patients’ health diagnosis process and contain valuable indicators for various CVDs. Most ECG processing methods represent ECG data as a time series, often as a matrix with each row containing the measurements of a sensor lead; and/or the transforms of such time series like wavelet power spectrums. While methods processing such time-series data have been shown to work well in benchmarks, they are still highly dependent on factors like input noise and sequence length, and cannot always correlate lead data from different sensors well. In this paper, we propose to represent ECG signals incorporating all lead data plotted as a single image, an approach not yet explored by literature. We will show that such an image representation combined with our newly proposed convolutional neural network specifically designed for CVD classification can overcome the aforementioned shortcomings. The proposed (Convolutional Neural Network) CNN is designed to extract features representing both the proportional relationships of different leads to each other and the characteristics of each lead separately. Empirical validation on the publicly available PTB, MIT-BIH, and St.-Petersburg benchmark databases shows that the proposed method outperforms time seriesbased state-of-the-art approaches, yielding classification accuracy of 97.91%, 99.62%, and 98.70%, respectively.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87574480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lin Jiang, Yifeng Zheng, Chen Che, Li Guohe, Wenjie Zhang
： Deep learning technique has been developing intensively in big data era. However ， its capability is still chal⁃ lenged for the design of network structure and parameter setting. Therefore ， it is essential to improve the performance of the model and optimize the complexity of the model. Machine learning can be segmented into five categories in terms of learn⁃ ing methods ： 1 ） supervised learning ， 2 ） unsupervised learning ， 3 ） semi - supervised learning ， 4 ） deep learning ， and 5 ） reinforcement learning. These machine learning techniques are required to be incorporated in. To improve its fitting and
{"title":"Review of optimization methods for supervised deep learning","authors":"Lin Jiang, Yifeng Zheng, Chen Che, Li Guohe, Wenjie Zhang","doi":"10.11834/jig.211139","DOIUrl":"https://doi.org/10.11834/jig.211139","url":null,"abstract":"： Deep learning technique has been developing intensively in big data era. However ， its capability is still chal⁃ lenged for the design of network structure and parameter setting. Therefore ， it is essential to improve the performance of the model and optimize the complexity of the model. Machine learning can be segmented into five categories in terms of learn⁃ ing methods ： 1 ） supervised learning ， 2 ） unsupervised learning ， 3 ） semi - supervised learning ， 4 ） deep learning ， and 5 ） reinforcement learning. These machine learning techniques are required to be incorporated in. To improve its fitting and","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80080636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Temporal series features extraction based on Bi-ConvLSTM of Alzheimer’s disease pridiction CTISS model","authors":"Hong Xin, Kaifeng Huang, Chenhui Yang","doi":"10.11834/jig.211186","DOIUrl":"https://doi.org/10.11834/jig.211186","url":null,"abstract":"","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81878764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xu Jiangtao, Wang Xinyang, Wang Tingdong, Chen Xin, Song Zongxi, Lei Hao, Liu Gang, Wen Desheng
{"title":"Review on optical visual sensor technology","authors":"Xu Jiangtao, Wang Xinyang, Wang Tingdong, Chen Xin, Song Zongxi, Lei Hao, Liu Gang, Wen Desheng","doi":"10.11834/jig.230039","DOIUrl":"https://doi.org/10.11834/jig.230039","url":null,"abstract":"","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"162 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73306483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yunpeng Liu, Tielin Wu, Cai Wenli, Renfang Wang, Dechao Sun, Kaifeng Gan, Li Jin, Jin Ran, Qiu Hong, Huixia Xu
{"title":"Pre analysis of difficulty in renal tumor enucleation surgery based on deep learning and image automation evaluation","authors":"Yunpeng Liu, Tielin Wu, Cai Wenli, Renfang Wang, Dechao Sun, Kaifeng Gan, Li Jin, Jin Ran, Qiu Hong, Huixia Xu","doi":"10.11834/jig.220375","DOIUrl":"https://doi.org/10.11834/jig.220375","url":null,"abstract":"","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89224199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiafei Liang, Li Ting, Yang Jiaqi, Li Yanan, Zhiwen Fang, Yang Feng
： Objective Anomaly detection has been developing in video surveillance domain. Video anomaly detection is
客观异常检测是视频监控领域的发展方向。视频异常检测
{"title":"Video anomaly detection by fusing self-attention and autoencoder","authors":"Jiafei Liang, Li Ting, Yang Jiaqi, Li Yanan, Zhiwen Fang, Yang Feng","doi":"10.11834/jig.211147","DOIUrl":"https://doi.org/10.11834/jig.211147","url":null,"abstract":"： Objective Anomaly detection has been developing in video surveillance domain. Video anomaly detection is","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"136 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79602862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research on visual analysis methods of bird satellite tracking data： a case study analysis for Nipponia nippon","authors":"Xinyue Li, Jiang Xian, Weiqun Cao, Dongping Liu","doi":"10.11834/jig.220403","DOIUrl":"https://doi.org/10.11834/jig.220403","url":null,"abstract":"","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85350443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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