Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
IT Services, Lidoma Sanat Mehregan Part Ltd., Shiraz 71581, Fars, Iran.
Sci Rep. 2024 Oct 29;14(1):26002. doi: 10.1038/s41598-024-77876-8.
The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer's disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in addressing this challenge. We propose the Convolutional Learning Attention-Bidirectional Time-Aware Long-Short-Term Memory (CL-ATBiLSTM) model, a deep learning approach designed to classify different AD phases through EEG data analysis. The model utilizes Discrete Wavelet Transform (DWT) to decompose EEG data into distinct frequency bands, allowing for targeted analysis of AD-related brain activity patterns. Additionally, the data is segmented into smaller windows to handle the dynamic nature of EEG signals, and these segments are transformed into spectrogram images, visually depicting brain activity distribution over time and frequency. The CL-ATBiLSTM model incorporates convolutional layers to capture spatial features, attention mechanisms to emphasize crucial data, and BiLSTM networks to explore temporal relationships within the sequences. To optimize the model's performance, Bayesian optimization is employed to fine-tune the hyperparameters of the ATBiLSTM network, enhancing its ability to generalize and accurately classify AD stages. Incorporating Bayesian learning ensures the most effective model configuration, improving sensitivity and specificity for identifying AD-related patterns. Our model extracts discriminative features from EEG data to differentiate between AD, Mild Cognitive Impairment (MCI), and healthy controls (CO), offering a more comprehensive approach than existing two-class detection algorithms. By including the MCI category, our method facilitates earlier identification and potentially more impactful therapy interventions. Achieving a 96.52% accuracy on Figshare datasets containing AD, MCI, and CO groups, our approach demonstrates strong potential for practical use, accelerating AD identification, enhancing patient care, and contributing to the development of targeted treatments for this debilitating condition.
物联网 (IoMT) 有望在未来的医疗支持系统中发挥关键作用，实现智能城市的普及健康监测。阿尔茨海默病 (AD) 影响着全球数百万人，本文探讨了脑电图 (EEG) 数据在应对这一挑战中的潜力。我们提出了卷积学习注意力双向时间感知长短时记忆 (CL-ATBiLSTM) 模型，这是一种深度学习方法，旨在通过 EEG 数据分析对不同的 AD 阶段进行分类。该模型利用离散小波变换 (DWT) 将 EEG 数据分解为不同的频带，从而可以针对 AD 相关的大脑活动模式进行有针对性的分析。此外，将数据分段为较小的窗口以处理 EEG 信号的动态性质，并且这些段被转换为频谱图图像，直观地描绘了大脑活动随时间和频率的分布。CL-ATBiLSTM 模型结合了卷积层来捕获空间特征，注意力机制来强调关键数据，以及双向长短时记忆网络 (BiLSTM) 来探索序列中的时间关系。为了优化模型的性能，采用贝叶斯优化来微调 ATBiLSTM 网络的超参数，提高其泛化能力和准确分类 AD 阶段的能力。引入贝叶斯学习确保了最有效的模型配置，提高了识别 AD 相关模式的敏感性和特异性。我们的模型从 EEG 数据中提取鉴别特征，以区分 AD、轻度认知障碍 (MCI) 和健康对照 (CO)，提供了比现有两分类检测算法更全面的方法。通过包括 MCI 类别，我们的方法可以更早地识别并可能进行更有影响力的治疗干预。在包含 AD、MCI 和 CO 组的 Figshare 数据集上实现了 96.52%的准确率，我们的方法展示了实际应用的强大潜力，加速了 AD 的识别，改善了患者护理，并为这种衰弱疾病的靶向治疗做出了贡献。
