Stroke Classification in Simulated Electromagnetic Imaging Using Graph Approaches

Abstract

Identifying stroke subtypes from electromagnetic imaging systems is usually based on frequency domain using radar or tomography algorithms which is computationally expensive. This paper presents a novel graph degree mutual information (GDMI) approach to distinguish Intracranial Haemorrhage (ICH) from Ischemic Stroke (IS). A total of 50 ICH and 50 IS signals simulated using a 16-antenna electromagnetic head imaging system are analysed to evaluate GDMI. The data collected from each model consists of 256 reflected and received signal. Subsequently, noise is injected into the collected signals to generate three groups of signals with different signal-to-noise ratios (40 dB, 25 dB and 10 dB SNR), to emulate measurement noise and to test the algorithm's robustness. Each signal is converted into a graph to avoid the variable signal amplitudes. Then, the relationship between each pair of graph degrees is calculated by mutual information and forwarded to a support vector machine to identify stroke type. The results indicate that signals from ICH subjects exhibit a significantly higher GDMI compared to the IS group ( $p ). An accuracy of 88% is achieved in identifying ICH from IS without the need to use time- and resource-expensive brain image reconstruction algorithms even under 25 dB signal-to-noise levels. The execution time for graph feature extraction and classification is less than one minute on a PC. Such a short time is suitable for stroke emergency requirements.