Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America.
Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America.
J Neural Eng. 2021 Aug 19;18(4). doi: 10.1088/1741-2552/ac1982.
Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andmTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.
轻度创伤性脑损伤（mTBI）是最常见的脑损伤类型。及时诊断 mTBI 对于做出“去/留”决策至关重要，以防止反复受伤，避免可能延长恢复的剧烈活动，并确保受试者具有高水平表现的能力。如果未被诊断，mTBI 可能导致各种短期和长期异常，包括但不限于认知功能受损、疲劳、抑郁、易怒和头痛。现有的用于检测急性和 mTBI 的筛查和诊断工具的灵敏度和特异性不足。这导致在临床决策方面对诊断和恢复活动或需要进一步治疗存在不确定性。因此，识别相关的生理生物标志物非常重要，这些标志物可以整合到一个互补的集合中，并提供数据模态的组合，以提高 mTBI 的现场诊断灵敏度。近年来，可穿戴式医疗设备的处理能力、信号保真度、记录通道和模态的数量都有了极大的提高，并产生了大量的数据。同期，机器学习工具和数据处理方法学也取得了令人难以置信的进步。这些成就使临床医生和工程师能够开发和实施用于 mTBI 的多参数高精度诊断工具。在这篇综述中，我们首先评估了急性 mTBI 诊断中的临床挑战，然后考虑了用于评估可能与 mTBI 相关的生理生物标志物的各种传感技术的记录模态和硬件实现。最后，我们讨论了基于机器学习的 mTBI 检测的最新技术，并考虑了更广泛的定量生理生物标志物特征列表如何通过为 mTBI 患者提供及时的诊断和治疗来改进当前基于数据的方法。
