{"title":"基于 CNN 的神经退行性疾病分类(使用 QR 表示的步态数据)。","authors":"Çağatay Berke Erdaş, Emre Sümer","doi":"10.1002/brb3.70100","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Purpose</h3>\n \n <p>The primary aim of this study is to develop an effective and reliable diagnostic system for neurodegenerative diseases by utilizing gait data transformed into QR codes and classified using convolutional neural networks (CNNs). The objective of this method is to enhance the precision of diagnosing neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD), through the introduction of a novel approach to analyze gait patterns.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The research evaluates the CNN-based classification approach using QR-represented gait data to address the diagnostic challenges associated with neurodegenerative diseases. The gait data of subjects were converted into QR codes, which were then classified using a CNN deep learning model. The dataset includes recordings from patients with Parkinson's disease (<i>n</i> = 15), Huntington's disease (<i>n</i> = 20), and amyotrophic lateral sclerosis (<i>n</i> = 13), and from 16 healthy controls.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The accuracy rates obtained through 10-fold cross-validation were as follows: 94.86% for NDD versus control, 95.81% for PD versus control, 93.56% for HD versus control, 97.65% for ALS versus control, and 84.65% for PD versus HD versus ALS versus control. These results demonstrate the potential of the proposed system in distinguishing between different neurodegenerative diseases and control groups.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>The results indicate that the designed system may serve as a complementary tool for the diagnosis of neurodegenerative diseases, particularly in individuals who already present with varying degrees of motor impairment. Further validation and research are needed to establish its wider applicability.</p>\n </section>\n </div>","PeriodicalId":9081,"journal":{"name":"Brain and Behavior","volume":"14 10","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brb3.70100","citationCount":"0","resultStr":"{\"title\":\"CNN-Based Neurodegenerative Disease Classification Using QR-Represented Gait Data\",\"authors\":\"Çağatay Berke Erdaş, Emre Sümer\",\"doi\":\"10.1002/brb3.70100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>The primary aim of this study is to develop an effective and reliable diagnostic system for neurodegenerative diseases by utilizing gait data transformed into QR codes and classified using convolutional neural networks (CNNs). The objective of this method is to enhance the precision of diagnosing neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD), through the introduction of a novel approach to analyze gait patterns.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>The research evaluates the CNN-based classification approach using QR-represented gait data to address the diagnostic challenges associated with neurodegenerative diseases. The gait data of subjects were converted into QR codes, which were then classified using a CNN deep learning model. 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引用次数: 0
摘要
目的:本研究的主要目的是利用转化为 QR 码的步态数据,并使用卷积神经网络 (CNN) 进行分类,从而开发一种有效、可靠的神经退行性疾病诊断系统。该方法的目的是通过引入一种新的步态模式分析方法,提高神经退行性疾病(包括肌萎缩性脊髓侧索硬化症(ALS)、帕金森病(PD)和亨廷顿病(HD))的诊断精度:研究评估了基于 CNN 的分类方法,该方法利用 QR 表示的步态数据来解决与神经退行性疾病相关的诊断难题。受试者的步态数据被转换成 QR 码,然后使用 CNN 深度学习模型对其进行分类。数据集包括帕金森病(15 人)、亨廷顿病(20 人)和肌萎缩侧索硬化症(13 人)患者以及 16 名健康对照者的记录:通过 10 倍交叉验证获得的准确率如下:NDD与对照组的准确率为94.86%,PD与对照组的准确率为95.81%,HD与对照组的准确率为93.56%,ALS与对照组的准确率为97.65%,PD与HD、ALS与对照组的准确率为84.65%。这些结果表明,该系统具有区分不同神经退行性疾病和对照组的潜力:结果表明,所设计的系统可作为神经退行性疾病诊断的辅助工具,尤其适用于已出现不同程度运动障碍的个体。要确定其更广泛的适用性,还需要进一步的验证和研究。
CNN-Based Neurodegenerative Disease Classification Using QR-Represented Gait Data
Purpose
The primary aim of this study is to develop an effective and reliable diagnostic system for neurodegenerative diseases by utilizing gait data transformed into QR codes and classified using convolutional neural networks (CNNs). The objective of this method is to enhance the precision of diagnosing neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD), through the introduction of a novel approach to analyze gait patterns.
Methods
The research evaluates the CNN-based classification approach using QR-represented gait data to address the diagnostic challenges associated with neurodegenerative diseases. The gait data of subjects were converted into QR codes, which were then classified using a CNN deep learning model. The dataset includes recordings from patients with Parkinson's disease (n = 15), Huntington's disease (n = 20), and amyotrophic lateral sclerosis (n = 13), and from 16 healthy controls.
Results
The accuracy rates obtained through 10-fold cross-validation were as follows: 94.86% for NDD versus control, 95.81% for PD versus control, 93.56% for HD versus control, 97.65% for ALS versus control, and 84.65% for PD versus HD versus ALS versus control. These results demonstrate the potential of the proposed system in distinguishing between different neurodegenerative diseases and control groups.
Conclusion
The results indicate that the designed system may serve as a complementary tool for the diagnosis of neurodegenerative diseases, particularly in individuals who already present with varying degrees of motor impairment. Further validation and research are needed to establish its wider applicability.
期刊介绍:
Brain and Behavior is supported by other journals published by Wiley, including a number of society-owned journals. The journals listed below support Brain and Behavior and participate in the Manuscript Transfer Program by referring articles of suitable quality and offering authors the option to have their paper, with any peer review reports, automatically transferred to Brain and Behavior.
* [Acta Psychiatrica Scandinavica](https://publons.com/journal/1366/acta-psychiatrica-scandinavica)
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* [GLIA](https://publons.com/journal/1287/glia)
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* [Journal of Comparative Neurology](https://publons.com/journal/1306/journal-of-comparative-neurology)
* [Journal of Neuroimaging](https://publons.com/journal/6379/journal-of-neuroimaging)
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* [Journal of Organizational Behavior](https://publons.com/journal/1123/journal-of-organizational-behavior)
* [Journal of the Peripheral Nervous System](https://publons.com/journal/3929/journal-of-the-peripheral-nervous-system)
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