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Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

1
KEPCO Research Institute, Korea Electric Power Corporation, 105 Munji-ro Yuseong-gu, Daejeon 34056, Korea
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Research Team for Health & Safety Convergence, Korea Research Institute of Standards and Science (KRISS), Daejeon 34113, Korea
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Department of KSB Convergence Research, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea
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Department of Rehabilitation Medicine, Chungnam National University College of Medicine, 266 Munhwa-ro Jung-gu, Daejeon 35015, Korea
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School of Creative Convergence, Andong National University, 1375 Gyeongdong-ro (Songcheon-dong), Andong, Gyeongsangbuk-do 36729, Korea
*
Authors to whom correspondence should be addressed.
Academic Editor: Paweł Pławiak
Sensors 2021, 21(13), 4269; https://doi.org/10.3390/s21134269
Received: 10 May 2021 / Revised: 15 June 2021 / Accepted: 16 June 2021 / Published: 22 June 2021
(This article belongs to the Section Sensing and Imaging)
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. View Full-Text
Keywords: electroencephalography (EEG); stroke prediction; stroke disease analysis; deep learning; long short-term memory (LSTM); convolutional neural network (CNN); bidirectional; ensemble electroencephalography (EEG); stroke prediction; stroke disease analysis; deep learning; long short-term memory (LSTM); convolutional neural network (CNN); bidirectional; ensemble
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MDPI and ACS Style

Choi, Y.-A.; Park, S.-J.; Jun, J.-A.; Pyo, C.-S.; Cho, K.-H.; Lee, H.-S.; Yu, J.-H. Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals. Sensors 2021, 21, 4269. https://doi.org/10.3390/s21134269

AMA Style

Choi Y-A, Park S-J, Jun J-A, Pyo C-S, Cho K-H, Lee H-S, Yu J-H. Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals. Sensors. 2021; 21(13):4269. https://doi.org/10.3390/s21134269

Chicago/Turabian Style

Choi, Yoon-A, Se-Jin Park, Jong-Arm Jun, Cheol-Sig Pyo, Kang-Hee Cho, Han-Sung Lee, and Jae-Hak Yu. 2021. "Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals" Sensors 21, no. 13: 4269. https://doi.org/10.3390/s21134269

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