Electroencephalography (EEG) is a complex bioelectrical signal. Analysis of which can provide researchers with useful physiological information. In order to recognize and classify EEG signals, a pattern recognition method for optimizing the support vector machine (SVM) by using improved squirrel search algorithm (ISSA) is proposed. The EEG signal is preprocessed, with its time domain features being extracted and directed to the SVM as feature vectors for classification and identification. In this paper, the method of good point set is used to initialize the population position, chaos and reverse learning mechanism are introduced into the algorithm. The performance test of the improved squirrel algorithm (ISSA) is carried out by using the benchmark function. As can be seen from the statistical analysis of the results, the exploration ability and convergence speed of the algorithm are improved. This is then used to optimize SVM parameters. ISSA-SVM model is established and built for classification of EEG signals, compared with other common SVM parameter optimization models. For data sets, the average classification accuracy of this method is 85.9%. This result is an improvement of 2–5% over the comparison method.
Wireless Body Area Network (WBAN) has gained considerable significance in medical fields like implantable cardiac defibrillators (ICDs), neuro-stimulators etc. The body area networks information with in the implantable medical devices (IMDs) must be secure and their privacy must be protected. The absence of protection at the interface makes it easy for the attackers to take control of the IMDs. Thus, protection of wireless interface has become mandatory in IMDs during key agreement schemes. To secure the key agreement scheme, the most practical light weight bio-cryptosystem schemes popularly known as fuzzy vault (FV) is implemented. The most computationally intensive task in the FV scheme is the chaff point generation process, used for hiding the secret key and valid point inside the vault. Thus, a Raspberry Pi implemented with MATLAB simulation and communication of physiological signal based fast chaff point generation (RPSC) algorithm for WBAN. RPSC algorithm reduced the number of candidate chaff points in the chaff point generation and reduced the overall execution time. The RPSC algorithm has an algorithm complexity of O(n2), which is a significant over the existing O(n3) complexity. The RPSC algorithm has a speedup performance of 206 times over Clancy’s, 130 times over Khalil’s and 93 times than Nguyen algorithms for the generation of 504 chaff points, within smaller computation duration of 0.7 s. Raspberry Pi pro 3 (RPi3) hardware modules are considered as IMD and programmer devices, are used for implementation of chaff point generation and real-time communication module for proposed WBAN.
Tracking and localizing objects is a central problem in computer-assisted surgery. Optical coherence tomography (OCT) can be employed as an optical tracking system, due to its high spatial and temporal resolution. Recently, 3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single volumetric OCT images. While this approach relied on spatial information only, OCT allows for a temporal stream of OCT image volumes capturing the motion of an object at high volumes rates. In this work, we systematically extend 3D CNNs to 4D spatio-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking. Across various architectures, our results demonstrate that using a stream of OCT volumes and employing 4D spatio-temporal convolutions leads to a 30% lower mean absolute error compared to single volume processing with 3D CNNs.