Transient ischemic attack analysis through non-contact approaches

Abstract

IntroductionTransient ischemic attack (TIA) is a transient neurological disorder caused by focal ischemia of the brain or retina without acute infarction. The clinical symptoms usually last less than 1 hour and the neurological function can return to normal after the onset [1]. TIA is characterized by sudden onset, short duration and high frequency of attack. Currently, the causes of TIA are generally recognized by the medical community as follows :(1) Embolus in arterial blood flows into the brain, resulting in blockage and poor circulation of blood. (2) When blood pressure fluctuates, especially when blood pressure drops, the blood flow in the distal part of the brain's smaller blood vessels decreases. (3) Changes in blood composition cause blood clots to form in blood vessels, which can block blood vessels in the brain [2] [3]. Relevant clinical experimental data show that TIA has an early warning effect on stroke. After TIA, the incidence of AbstractThe transient ischemic attack (TIA) is a kind of sudden disease, which has the characteristics of short duration and high frequency. Since most patients can return to normal after the onset of the disease, it is often neglected. Medical research has proved that patients are prone to stroke in a relatively short time after the transient ischemic attacks. Therefore, it is extremely important to effectively monitor transient ischemic attack, especially for elderly people living alone. At present, video monitoring and wearing sensors are generally used to monitor transient ischemic attacks, but these methods have certain disadvantages. In order to more conveniently and accurately monitor transient ischemic attack in the indoor environment and improve risk management of stroke, this paper uses a microwave sensing platform working in C-Band (4.0 GHz-8.0 GHz) to monitor in a non-contact way. The platform first collects data, then preprocesses the data, and finally uses principal component analysis to reduce the dimension of the data. Two machine learning algorithms support vector machine (SVM) and random forest (RF) are used to establish prediction models respectively. The experimental results show that the accuracy of SVM and RF approaches are 97.3% and 98.7%, respectively; indicating that the scheme described in this paper is feasible and reliable.

Applicable artificial intelligence for brain disease: A survey

“…suggested that their study was potential. In 2020, Zhang, et al [112] proposed a contactless approach to detect TIA in the indoor environment. They employed a microwave sensing platform to collect monitoring data and then performed the support vector machine (SVM) and the random forest (RF) to build the recognition model.…”

The Prediction of Sinter Drums Strength Using Hybrid Machine Learning Algorithms

“…It is concluded that the prediction of ANN model is better than that of linear regression model. In order to monitor transient Islamic attack in the interior environment and improve risk management of stroke, two machine learning algorithms support vector machine (SVM) and random forest (RF) are used to establish prediction models respectively [ 37 ]. The accuracy rate has reached more than 97%.…”