Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke

Abstract

The paper presents a novel method and system for personalised (individualised) modelling of spatio/spectro-temporal data (SSTD) and prediction of events. A novel evolving spiking neural network reservoir system (eSNNr) is proposed for the purpose. The system consists of: spike-time encoding module of continuous value input information into spike trains; a recurrent 3D SNNr; eSNN as an evolving output classifier. Such system is generated for every new individual, using existing data of similar individuals. Subject to proper training and parameter optimisation, the system is capable of accurate spatio- temporal pattern recognition (STPR) and of early prediction of individual events. The method and the system are generic, applicable to various SSTD and classification and prediction problems. As a case study, the method is applied for early prediction of occurrence of stroke on an individual basis. Preliminary experiments demonstrated a significant improvement in accuracy and time of event prediction when using the proposed method when compared with standard machine learning methods, such as MLR, SVM, MLP. Future development and applications are discussed

Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis

“…Five features were considered for obtaining the disease prediction using the three methods RS-RMC, PPM-VA and PE-SSTD respectively. From the figure, the value of disease prediction rate achieved using the proposed RS-RMC framework is higher when compared to two other existing techniques namely, PPM-VA [1] and PE-SSTD [2]. Besides we can also observe that by increasing the number of patients who provide their disease features, the disease prediction rate is increased using all the methods.…”

“…The RS-RMC framework is simulated using MATLAB. The experimental work is compared against the existing Prevention and Potential Management of Ventricular Arrhythmias (PPM-VA) [1] and Prediction of Events using Spatio Spectro Temporal Data (PE-SSTD) [2] to identify the effectiveness of RS-RMC framework. The performance of the RS-RMC framework is measured in terms of disease prediction rate, execution time and false positive rate on effective disease diagnosis and class accuracy.…”

“…Prediction of Events using Spatio Spectro Temporal Data (PE-SSTD) [2] provided with a case study on stroke resulting in the accuracy of the disease being diagnosed. However, both the above methods lack the class accuracy of disease diagnosis with the increase in the feature.…”

A Survey on Trend, Opportunities and Challenges of mHealth Apps

“…Thus, mHealth technology is an alternative for cost effective health support. The review of this study also discover that mHealth researchers and publishers have focused more on individual prevention diseases (Table 1) such as selfmanagement for diabetes [14,15], gout [16], chronic obstructive pulmonary disease [17], bipolar disorders [18] and bowel disease [19], stroke [20,21] heart disease [22] and flu [23]. Article [23] presented HHeal, an app which integrates flu risk information and flu preventive behaviors, which provide a personal flu risk bar that arises when a user is near someone with flu-like symptoms and drops when the user finishes one of the suggested flu-preventive behaviors.…”

Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks

“…The learning of ESNN has many good advantages as it can be applied incrementally, is adaptive and theoretically 'lifelong'. Therefore, the system can learn any new pattern via creating new output neurons, connecting them to the input neurons and merging with the similar ones [25][26][27]. This model stands on two principles: possibility of establishment of new classes and the merging of the similarities.…”