Role of deep learning in cognitive healthcare: Wearable signal analysis, algorithms, benefits, and challenges

IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS
Md. Sakib Bin Alam , Aiman Lameesa , Senzuti Sharmin , Shaila Afrin , Shams Forruque Ahmed , Mohammad Reza Nikoo , Amir H. Gandomi
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Abstract

Deep Learning (DL) offers promising solutions for analyzing wearable signals and gaining valuable insights into cognitive disorders. While previous review studies have explored various aspects of DL in cognitive healthcare, there remains a lack of comprehensive analysis that integrates wearable signals, data processing techniques, and the broader applications, benefits, and challenges of DL methods. Addressing this limitation, our study provides an extensive review of DL's role in cognitive healthcare, with a particular emphasis on wearables, data processing, and the inherent challenges in this field. This review also highlights the considerable promise of DL approaches in addressing a broad spectrum of cognitive issues. By enhancing the understanding and analysis of wearable signal modalities, DL models can achieve remarkable accuracy in cognitive healthcare. Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-term Memory (LSTM) networks have demonstrated improved performance and effectiveness in the early diagnosis and progression monitoring of neurological disorders. Beyond cognitive impairment detection, DL has been applied to emotion recognition, sleep analysis, stress monitoring, and neurofeedback. These applications lead to advanced diagnosis, personalized treatment, early intervention, assistive technologies, remote monitoring, and reduced healthcare costs. Nevertheless, the integration of DL and wearable technologies presents several challenges, such as data quality, privacy, interpretability, model generalizability, ethical concerns, and clinical adoption. These challenges emphasize the importance of conducting future research in areas such as multimodal signal analysis and explainable AI. The findings of this review aim to benefit clinicians, healthcare professionals, and society by facilitating better patient outcomes in cognitive healthcare.
深度学习在认知医疗中的作用:可穿戴信号分析、算法、好处和挑战
深度学习(DL)为分析可穿戴信号和获得认知障碍的宝贵见解提供了有前途的解决方案。虽然以前的综述研究已经探索了DL在认知医疗保健中的各个方面,但仍然缺乏综合分析,将可穿戴信号、数据处理技术以及DL方法的更广泛应用、好处和挑战结合起来。为了解决这一限制,我们的研究对深度学习在认知医疗保健中的作用进行了广泛的回顾,特别强调了可穿戴设备、数据处理以及该领域的固有挑战。这篇综述还强调了深度学习方法在解决广泛的认知问题方面的巨大前景。通过增强对可穿戴信号模式的理解和分析,深度学习模型可以在认知医疗中取得显著的准确性。卷积神经网络(CNN)、循环神经网络(RNN)和长短期记忆(LSTM)网络在神经系统疾病的早期诊断和进展监测方面表现出了更好的性能和有效性。除了认知障碍检测,深度学习还被应用于情绪识别、睡眠分析、压力监测和神经反馈。这些应用可实现高级诊断、个性化治疗、早期干预、辅助技术、远程监控和降低医疗成本。然而,深度学习和可穿戴技术的整合带来了一些挑战,如数据质量、隐私、可解释性、模型概括性、伦理问题和临床应用。这些挑战强调了在多模态信号分析和可解释人工智能等领域开展未来研究的重要性。本综述的研究结果旨在通过促进认知卫生保健患者更好的预后,使临床医生、卫生保健专业人员和社会受益。
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来源期刊
Digital Communications and Networks
Digital Communications and Networks Computer Science-Hardware and Architecture
CiteScore
12.80
自引率
5.10%
发文量
915
审稿时长
30 weeks
期刊介绍: Digital Communications and Networks is a prestigious journal that emphasizes on communication systems and networks. We publish only top-notch original articles and authoritative reviews, which undergo rigorous peer-review. We are proud to announce that all our articles are fully Open Access and can be accessed on ScienceDirect. Our journal is recognized and indexed by eminent databases such as the Science Citation Index Expanded (SCIE) and Scopus. In addition to regular articles, we may also consider exceptional conference papers that have been significantly expanded. Furthermore, we periodically release special issues that focus on specific aspects of the field. In conclusion, Digital Communications and Networks is a leading journal that guarantees exceptional quality and accessibility for researchers and scholars in the field of communication systems and networks.
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