Deep learning-based classification of dementia using image representation of subcortical signals.

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Shivani Ranjan, Ayush Tripathi, Harshal Shende, Robin Badal, Amit Kumar, Pramod Yadav, Deepak Joshi, Lalan Kumar
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引用次数: 0

Abstract

Background: Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. Early and accurate diagnosis of dementia cases (AD and FTD) is crucial for effective medical care, as both conditions have similar early-symptoms. EEG, a non-invasive tool for recording brain activity, has shown potential in distinguishing AD from FTD and mild cognitive impairment (MCI).

Methods: This study aims to develop a deep learning-based classification system for dementia by analyzing EEG derived scout time-series signals from deep brain regions, specifically the hippocampus, amygdala, and thalamus. Scout time series extracted via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique are utilized. The time series is converted to image representations using continuous wavelet transform (CWT) and fed as input to deep learning models. Two high-density EEG datasets are utilized to validate the efficacy of the proposed method: the online BrainLat dataset (128 channels, comprising 16 AD, 13 FTD, and 19 healthy controls (HC)) and the in-house IITD-AIIA dataset (64 channels, including subjects with 10 AD, 9 MCI, and 8 HC). Different classification strategies and classifier combinations have been utilized for the accurate mapping of classes in both data sets.

Results: The best results were achieved using a product of probabilities from classifiers for left and right subcortical regions in conjunction with the DenseNet model architecture. It yield accuracies of 94.17 % and 77.72 % on the BrainLat and IITD-AIIA datasets, respectively.

Conclusions: The results highlight that the image representation-based deep learning approach has the potential to differentiate various stages of dementia. It pave the way for more accurate and early diagnosis, which is crucial for the effective treatment and management of debilitating conditions.

基于皮质下信号图像表示的深度学习痴呆分类。
背景:痴呆症是一种以认知能力下降为特征的神经系统综合征。阿尔茨海默病(AD)和额颞叶痴呆(FTD)是痴呆的常见形式,每一种都有不同的进展模式。痴呆病例(AD和FTD)的早期准确诊断对于有效的医疗护理至关重要,因为这两种疾病具有相似的早期症状。EEG是一种记录大脑活动的非侵入性工具,在区分AD与FTD和轻度认知障碍(MCI)方面显示出潜力。方法:本研究旨在通过分析脑深部区,特别是海马、杏仁核和丘脑的EEG童子军时间序列信号,开发基于深度学习的痴呆分类系统。利用标准化低分辨率脑电磁断层扫描(sLORETA)技术提取的Scout时间序列。使用连续小波变换(CWT)将时间序列转换为图像表示,并将其作为深度学习模型的输入。使用两个高密度脑电数据集来验证所提出方法的有效性:在线BrainLat数据集(128个通道,包括16个AD, 13个FTD和19个健康对照(HC))和内部IITD-AIIA数据集(64个通道,包括10个AD, 9个MCI和8个HC)。不同的分类策略和分类器组合被用于精确映射两个数据集中的类。结果:使用左侧和右侧皮层下区域分类器的概率乘积与DenseNet模型架构相结合,获得了最佳结果。它在BrainLat和IITD-AIIA数据集上的准确率分别为94.17%和77.72%。结论:研究结果表明,基于图像表示的深度学习方法具有区分不同阶段痴呆的潜力。它为更准确和早期诊断铺平了道路,这对于有效治疗和管理衰弱性疾病至关重要。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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