KEPCO Research Institute, Korea Electric Power Corporation, 105 Munji-ro Yuseong-gu, Daejeon 34056, Korea.
Research Team for Health & Safety Convergence, Korea Research Institute of Standards and Science (KRISS), Daejeon 34113, Korea.
Sensors (Basel). 2021 Jun 22;21(13):4269. doi: 10.3390/s21134269.
The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.
由于预期寿命的延长和出生率的下降，老龄化社会的出现是不可避免的。这些社会变化需要新的智能医疗保健服务来用于日常生活，而 COVID-19 的出现也导致了无接触的趋势，需要更多的非面对面的健康服务。由于医疗技术的改进，越来越多的研究试图提前预测和分析某些疾病。中风疾病的研究正在积极进行，特别是针对人口老龄化问题。中风对老年人来说是致命的，是一种需要持续医疗观察和监测的疾病，因为其复发率和死亡率非常高。迄今为止，大多数研究中风疾病的研究都使用 MRI 或 CT 图像进行简单的分类。这种临床方法（成像）昂贵且耗时，同时需要庞大的设备。最近，人们越来越感兴趣地使用非侵入性可测量的 EEG 来弥补这些不足。然而，预测算法和处理过程都很耗时，因为在获得特定属性之前，原始数据需要分离。因此，在本文中，我们提出了一种新的方法，允许在不使用 EEG 频率特性的情况下，将深度学习模型立即应用于原始 EEG 数据。这个基于深度学习的中风疾病预测模型是使用从实时 EEG 传感器收集的数据进行开发和训练的。我们实现并比较了不同的深度学习模型（LSTM、双向 LSTM、CNN-LSTM 和 CNN-Bidirectional LSTM），这些模型专门用于时间序列数据分类和预测。实验结果证实，当使用 CNN 双向 LSTM 模型处理原始 EEG 数据时，可以以 94.0%的准确率预测中风，同时具有低 FPR（6.0%）和低 FNR（5.7%），从而对我们的系统具有高度信心。这些实验结果表明，非侵入性方法具有可行性，可以单独轻松测量脑电波，从而可以在日常生活中实时预测和监测中风疾病。与其他测量技术相比，这些发现有望实现早期中风检测的显著改进，降低成本和不适感。
