Sensors, Vol. 21, Pages 4269: Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

Sensors, Vol. 21, Pages 4269: Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals Sensors doi: 10.3390/s21134269 Authors: Yoon-A Choi Se-Jin Park Jong-Arm Jun Cheol-Sig Pyo Kang-Hee Cho Han-Sung Lee Jae-Hak Yu 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. ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research