Lina Qiu;Jianping Li;Liangquan Zhong;Weisen Feng;Chengju Zhou;Jiahui Pan
{"title":"使用多尺度卷积原型网络的基于脑电图的新型帕金森病检测模型","authors":"Lina Qiu;Jianping Li;Liangquan Zhong;Weisen Feng;Chengju Zhou;Jiahui Pan","doi":"10.1109/TIM.2024.3351248","DOIUrl":null,"url":null,"abstract":"Objective and accurate detection of Parkinson’s disease (PD) is crucial for timely intervention and treatment. Electroencephalography (EEG) has been proven to characterize PD by measuring brain activity. In recent years, deep learning methods have gained great attention in automated PD detection, but their performance is limited by insufficient data samples. In this article, we propose a novel PD automated detection model named the multiscale convolutional prototype network (MCPNet), which integrates and improves upon multiscale convolutional neural networks (CNNs) and prototype learning. On the one hand, it employs multiscale CNNs to extract brain features from different scales, enhancing feature diversity and utilization. On the other hand, a prototype calibration strategy is introduced to mitigate the effect of data noise on prototype generation, improving the generalization performance of model. Multiple within-dataset and cross-dataset experiments on three different datasets demonstrate the effectiveness of our model in PD detection. The leave-one-subject-out (LOSO) results of within-dataset experiments show that MCPNet achieves an accuracy of 92.5%, a sensitivity of 93.1%, a specificity of 91.9%, and an AUC of 92.4% in cross-subject classification between PD patients and healthy controls. In the cross-dataset classification, the performance of MCPNet is somewhat weakened due to dataset variations. However, this weakening is partially compensated by introducing the prototype calibration strategy. With the introduction of the calibration strategy, the accuracy of cross-dataset classification increases to 90.2%, a 4.0% improvement compared to when it is not used. These results indicate that the proposed model may be a promising tool for automated PD diagnosis.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-14"},"PeriodicalIF":5.6000,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel EEG-Based Parkinson’s Disease Detection Model Using Multiscale Convolutional Prototype Networks\",\"authors\":\"Lina Qiu;Jianping Li;Liangquan Zhong;Weisen Feng;Chengju Zhou;Jiahui Pan\",\"doi\":\"10.1109/TIM.2024.3351248\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Objective and accurate detection of Parkinson’s disease (PD) is crucial for timely intervention and treatment. Electroencephalography (EEG) has been proven to characterize PD by measuring brain activity. In recent years, deep learning methods have gained great attention in automated PD detection, but their performance is limited by insufficient data samples. In this article, we propose a novel PD automated detection model named the multiscale convolutional prototype network (MCPNet), which integrates and improves upon multiscale convolutional neural networks (CNNs) and prototype learning. On the one hand, it employs multiscale CNNs to extract brain features from different scales, enhancing feature diversity and utilization. On the other hand, a prototype calibration strategy is introduced to mitigate the effect of data noise on prototype generation, improving the generalization performance of model. Multiple within-dataset and cross-dataset experiments on three different datasets demonstrate the effectiveness of our model in PD detection. The leave-one-subject-out (LOSO) results of within-dataset experiments show that MCPNet achieves an accuracy of 92.5%, a sensitivity of 93.1%, a specificity of 91.9%, and an AUC of 92.4% in cross-subject classification between PD patients and healthy controls. In the cross-dataset classification, the performance of MCPNet is somewhat weakened due to dataset variations. However, this weakening is partially compensated by introducing the prototype calibration strategy. With the introduction of the calibration strategy, the accuracy of cross-dataset classification increases to 90.2%, a 4.0% improvement compared to when it is not used. These results indicate that the proposed model may be a promising tool for automated PD diagnosis.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"73 \",\"pages\":\"1-14\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-01-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10385211/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10385211/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A Novel EEG-Based Parkinson’s Disease Detection Model Using Multiscale Convolutional Prototype Networks
Objective and accurate detection of Parkinson’s disease (PD) is crucial for timely intervention and treatment. Electroencephalography (EEG) has been proven to characterize PD by measuring brain activity. In recent years, deep learning methods have gained great attention in automated PD detection, but their performance is limited by insufficient data samples. In this article, we propose a novel PD automated detection model named the multiscale convolutional prototype network (MCPNet), which integrates and improves upon multiscale convolutional neural networks (CNNs) and prototype learning. On the one hand, it employs multiscale CNNs to extract brain features from different scales, enhancing feature diversity and utilization. On the other hand, a prototype calibration strategy is introduced to mitigate the effect of data noise on prototype generation, improving the generalization performance of model. Multiple within-dataset and cross-dataset experiments on three different datasets demonstrate the effectiveness of our model in PD detection. The leave-one-subject-out (LOSO) results of within-dataset experiments show that MCPNet achieves an accuracy of 92.5%, a sensitivity of 93.1%, a specificity of 91.9%, and an AUC of 92.4% in cross-subject classification between PD patients and healthy controls. In the cross-dataset classification, the performance of MCPNet is somewhat weakened due to dataset variations. However, this weakening is partially compensated by introducing the prototype calibration strategy. With the introduction of the calibration strategy, the accuracy of cross-dataset classification increases to 90.2%, a 4.0% improvement compared to when it is not used. These results indicate that the proposed model may be a promising tool for automated PD diagnosis.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.