Analysis of physiological signals using machine learning methods

Technological advances in data collection enable scientists to suggest novel approaches, such as Machine Learning algorithms, to process and make sense of this information. However, during this process of collection, data loss and damage can occur for reasons such as faulty device sensors or miscommunication. In the context of time-series data such as multi-channel bio-signals, there is a possibility of losing a whole channel. In such cases, existing research suggests imputing the missing parts when the majority of data is available. One way of understanding and classifying complex signals is by using deep neural networks. The hyper-parameters of such models have been optimised using the process of back propagation. Over time, improvements have been suggested to enhance this algorithm. However, an essential drawback of the back propagation can be the sensitivity to noisy data. This thesis proposes two novel approaches to address the missing data challenge and back propagation drawbacks: First, suggesting a gradient-free model in order to discover the optimal hyper-parameters of a deep neural network. The complexity of deep networks and high-dimensional optimisation parameters presents challenges to find a suitable network structure and hyper-parameter configuration. This thesis proposes the use of a minimalist swarm optimiser, Dispersive Flies Optimisation(DFO), to enable the selected model to achieve better results in comparison with the traditional back propagation algorithm in certain conditions such as limited number of training samples. The DFO algorithm offers a robust search process for finding and determining the hyper-parameter configurations. Second, imputing whole missing bio-signals within a multi-channel sample. This approach comprises two experiments, namely the two-signal and five-signal imputation models. The first experiment attempts to implement and evaluate the performance of a model mapping bio-signals from A toB and vice versa. Conceptually, this is an extension to transfer learning using CycleGenerative Adversarial Networks (CycleGANs). The second experiment attempts to suggest a mechanism imputing missing signals in instances where multiple data channels are available for each sample. The capability to map to a target signal through multiple source domains achieves a more accurate estimate for the target domain. The results of the experiments performed indicate that in certain circumstances, such as having a limited number of samples, finding the optimal hyper-parameters of a neural network using gradient-free algorithms outperforms traditional gradient-based algorithms, leading to more accurate classification results. In addition, Generative Adversarial Networks could be used to impute the missing data channels in multi-channel bio-signals, and the generated data used for further analysis and classification tasks

Deep Neuroevolution: Training Deep Neural Networks for False Alarm Detection in Intensive Care Units

We present a neuroevolution based-approach for training neural networks based on genetic algorithms, as applied to the problem of detecting false alarms in Intensive Care Units (ICU) based on physiological data. Typically, optimisation in neural networks is performed via backpropagation (BP) with stochastic gradient-based learning. Nevertheless, recent works have shown promising results in terms of utilising gradient-free, population-based genetic algorithms, suggesting that in certain cases gradient-based optimisation is not the best approach to follow. In this paper, we empirically show that utilising evolutionary and swarm intelligence algorithms can improve the performance of deep neural networks in problems such as the detection of false alarms in ICU. In more detail, we present results that improve the state-of-the-art accuracy on the corresponding Physionet challenge, while reducing the number of suppressed true alarms by deploying and adapting Dispersive Flies Optimisation (DFO)

Stimulating learning: A functional MRI and behavioral investigation of the effects of transcranial direct current stimulation on stochastic learning in schizophrenia

Transcranial direct current stimulation (tDCS) of the medial prefrontal cortex (mPFC) is under clinical investigation as a treatment for cognitive deficits. We investigate the effects of tDCS over the mPFC on performance SSLT in individuals with schizophrenia, and the underlying neurophysiological effect in regions associated with learning values and stimulus-outcome relationships. In this parallel-design double-blind pilot study, 49 individuals with schizophrenia, of whom 28 completed a fMRI, were randomized into active or sham tDCS stimulation groups. Subjects participated in 4 days of SSLT training (days 1, 2, 14, 56) with tDCS applied at day-1, and during a concurrent MRI scan at day-14. The SSLT demonstrated a significant mean difference in performance in the tDCS treatment group: at day-2 and at day-56. Active tDCS was associated with increased insular activity, and reduced amygdala activation. tDCS may offer an important novel approach to modulating brain networks to ameliorate cognitive deficits in schizophrenia, with this study being the first to show a longer-term effect on SSLT

Stimulating learning: A functional MRI and behavioral investigation of the effects of transcranial direct current stimulation on stochastic learning in schizophrenia

Transcranial direct current stimulation (tDCS) of the medial prefrontal cortex (mPFC) is under clinical investigation as a treatment for cognitive deficits. We investigate the effects of tDCS over the mPFC on performance SSLT in individuals with schizophrenia, and the underlying neurophysiological effect in regions associated with learning values and stimulus-outcome relationships. In this parallel-design double-blind pilot study, 49 individuals with schizophrenia, of whom 28 completed a fMRI, were randomized into active or sham tDCS stimulation groups. Subjects participated in 4 days of SSLT training (days 1, 2, 14, 56) with tDCS applied at day-1, and during a concurrent MRI scan at day-14. The SSLT demonstrated a significant mean difference in performance in the tDCS treatment group: at day-2 and at day-56. Active tDCS was associated with increased insular activity, and reduced amygdala activation. tDCS may offer an important novel approach to modulating brain networks to ameliorate cognitive deficits in schizophrenia, with this study being the first to show a longer-term effect on SSLT