Review of medical data analysis based on spiking neural networks

Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions. However, the interpretation of medical data requires a lot of human cost and there may be misjudgments, so many scholars use neural networks and deep learning to classify and study medical data, which can improve the efficiency and accuracy of doctors and detect diseases early for early diagnosis, etc. Therefore, it has a wide range of application prospects. However, traditional neural networks have disadvantages such as high energy consumption and high latency (slow computation speed). This paper presents recent research on signal classification and disease diagnosis based on a third-generation neural network, the spiking neuron network, using medical data including EEG signals, ECG signals, EMG signals and MRI images. The advantages and disadvantages of pulsed neural networks compared with traditional networks are summarized and its development orientation in the future is prospected

Mapping temporal variables into the NeuCube for improved pattern recognition, predictive modelling, and understanding of stream data.

This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitrary stream data and to achieve a better accuracy of temporal pattern recognition, a better and earlier event prediction and a better understanding of complex temporal stream data through visualization of the NeuCube connectivity. The effect of the new mapping is demonstrated on three bench mark problems. The first one is early prediction of patient sleep stage event from temporal physiological data. The second one is pattern recognition of dynamic temporal patterns of traffic in the Bay Area of California and the last one is the Challenge 2012 contest data set. In all cases the use of the proposed mapping leads to an improved accuracy of pattern recognition and event prediction and a better understanding of the data when compared to traditional machine learning techniques or spiking neural network reservoirs with arbitrary mapping of the variables.Comment: Accepted by IEEE TNNL

Parameter optimization of evolving spiking neural network with dynamic population particle swarm optimization

Evolving Spiking Neural Network (ESNN) is widely used in classification problem. However, ESNN like any other neural networks is incapable to find its own parameter optimum values, which are crucial for classification accuracy. Thus, in this study, ESNN is integrated with an improved Particle Swarm Optimization (PSO) known as Dynamic Population Particle Swarm Optimization (DPPSO) to optimize the ESNN parameters: the modulation factor (Mod), similarity factor (Sim) and threshold factor (C). To find the optimum ESNN parameter value, DPPSO uses a dynamic population that removes the lowest particle value in every pre-defined iteration. The integration of ESNN-DPPSO facilitates the ESNN parameter optimization searching during the training stage. The performance analysis is measured by classification accuracy and is compared with the existing method. Five datasets gained from University of California Irvine (UCI) Machine Learning Repository are used for this study. The experimental result presents better accuracy compared to the existing technique and thus improves the ESNN method in optimising its parameter values

Parameter optimization of evolving spiking neural network with dynamic population particle swarm optimization

Evolving Spiking Neural Network (ESNN) is widely used in classification problem. However, ESNN like any other neural networks is incapable to find its own parameter optimum values, which are crucial for classification accuracy. Thus, in this study, ESNN is integrated with an improved Particle Swarm Optimization (PSO) known as Dynamic Population Particle Swarm Optimization (DPPSO) to optimize the ESNN parameters: the modulation factor (Mod), similarity factor (Sim) and threshold factor (C). To find the optimum ESNN parameter value, DPPSO uses a dynamic population that removes the lowest particle value in every pre-defined iteration. The integration of ESNN-DPPSO facilitates the ESNN parameter optimization searching during the training stage. The performance analysis is measured by classification accuracy and is compared with the existing method. Five datasets gained from University of California Irvine (UCI) Machine Learning Repository are used for this study. The experimental result presents better accuracy compared to the existing technique and thus improves the ESNN method in optimising its parameter values

Brain Disease Detection From EEGS: Comparing Spiking and Recurrent Neural Networks for Non-stationary Time Series Classification

Modeling non-stationary time series data is a difficult problem area in AI, due to the fact that the statistical properties of the data change as the time series progresses. This complicates the classification of non-stationary time series, which is a method used in the detection of brain diseases from EEGs. Various techniques have been developed in the field of deep learning for tackling this problem, with recurrent neural networks (RNN) approaches utilising Long short-term memory (LSTM) architectures achieving a high degree of success. This study implements a new, spiking neural network-based approach to time series classification for the purpose of detecting three brain diseases from EEG datasets - epilepsy, alcoholism, and schizophrenia. The performance and training time of the spiking neural network classifier is compared to those of both a baseline RNN-LSTM EEG classifier and the current state-of-the art RNN-LSTM EEG classifier architecture from the relevant literature. The SNN EEG classifier model developed in this study outperforms both the baseline and state of-the-art RNN models in terms of accuracy, and is able to detect all three brain diseases with an accuracy of 100%, while requiring a far smaller number of training data samples than recurrent neural network approaches. This represents the best performance present in the literature for the task of EEG classificatio

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

Neurophysiology of prospective memory in typical and atypical ageing

The ability to delay an intention is known as ‘prospective memory’ (PM) and underpins many day-to-day activities. The ubiquity of PM makes it essential for independent living in older adults. Research suggests that PM function declines as we age and may be further exacerbated with the development of mild cognitive impairment (MCI). To date, there has been no research examining the neurophysiology of PM in older adults with MCI. This thesis addresses a series of questions to help understand the neurophysiology of PM and how it may be affected by ageing and MCI: 1) Are there neurophysiological differences between highly salient PM cues and less salient PM cues? 2) Can the neurophysiological reorientation of attention be identified in PM tasks? 3) Are there behavioural and neurophysiological differences between young adults, older adults and older adults with MCI during PM tasks? 4) Are there behavioural and neurophysiological differences when maintaining a PM intention between young adults, older adults and older adults with MCI? 5) Can machine learning be used to understand spatiotemporal patterns of brain activity in response to PM between young adults, older adults and older adults with MCI? To answer these questions behavioural and time-locked electroencephalographic (EEG) responses were examined during PM tasks and were modelled with a machine learning method known as Spiking Neural Networks (SNN). Results suggest that: there are behavioural and neurophysiological differences between the PM cues and the neurophysiological reorientation of attention can be detected in PM tasks; older adults are not impaired in PM tasks possibly due to compensatory neural mechanisms; older adults with MCI may be impaired in some PM tasks, which may be due to deficits in attention and feelings of knowing; modelling PM with SNNs may offer useful ways of understanding spatiotemporal connectivity in PM and MCI