Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Wavelet-based birdsong recognition for conservation : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand

Listed in 2017 Dean's List of Exceptional ThesesAccording to the International Union for the Conservation of Nature Red Data List nearly a quarter of the world's bird species are either threatened or at risk of extinction. To be able to protect endangered species, we need accurate survey methods that reliably estimate numbers and hence population trends. Acoustic monitoring is the most commonly-used method to survey birds, particularly cryptic and nocturnal species, not least because it is non-invasive, unbiased, and relatively time-effective. Unfortunately, the resulting data still have to be analysed manually. The current practice, manual spectrogram reading, is tedious, prone to bias due to observer variations, and not reproducible. While there is a large literature on automatic recognition of targeted recordings of small numbers of species, automatic analysis of long field recordings has not been well studied to date. This thesis considers this problem in detail, presenting experiments demonstrating the true efficacy of recorders in natural environments under different conditions, and then working to reduce the noise present in the recording, as well as to segment and recognise a range of New Zealand native bird species. The primary issues with field recordings are that the birds are at variable distances from the recorder, that the recordings are corrupted by many different forms of noise, that the environment affects the quality of the recorded sound, and that birdsong is often relatively rare within a recording. Thus, methods of dealing with faint calls, denoising, and effective segmentation are all needed before individual species can be recognised reliably. Experiments presented in this thesis demonstrate clearly the effects of distance and environment on recorded calls. Some of these results are unsurprising, for example an inverse square relationship with distance is largely true. Perhaps more surprising is that the height from which a call is transmitted has a signifcant effect on the recorded sound. Statistical analyses of the experiments, which demonstrate many significant environmental and sound factors, are presented. Regardless of these factors, the recordings have noise present, and removing this noise is helpful for reliable recognition. A method for denoising based on the wavelet packet decomposition is presented and demonstrated to significantly improve the quality of recordings. Following this, wavelets were also used to implement a call detection algorithm that identifies regions of the recording with calls from a target bird species. This algorithm is validated using four New Zealand native species namely Australasian bittern (Botaurus poiciloptilus), brown kiwi (Apteryx mantelli ), morepork (Ninox novaeseelandiae), and kakapo (Strigops habroptilus), but could be used for any species. The results demonstrate high recall rates and tolerate false positives when compared to human experts

Multiple bottlenecks sorting criterion at initial sequence in solving permutation flow shop scheduling problem

This paper proposes a heuristic that introduces the application of bottleneck-based concept at the beginning of an initial sequence determination with the objective of makespan minimization. Earlier studies found that the scheduling activity become complicated when dealing with machine, m greater than 2, known as non-deterministic polynomial-time hardness (NP-hard). To date, the Nawaz-Enscore-Ham (NEH) algorithm is still recognized as the best heuristic in solving makespan problem in scheduling environment. Thus, this study treated the NEH heuristic as the highest ranking and most suitable heuristic for evaluation purpose since it is the best performing heuristic in makespan minimization. This study used the bottleneck-based approach to identify the critical processing machine which led to high completion time. In this study, an experiment involving machines (m =4) and n-job (n = 6, 10, 15, 20) was simulated in Microsoft Excel Simple Programming to solve the permutation flowshop scheduling problem. The overall computational results demonstrated that the bottleneck machine M4 performed the best in minimizing the makespan for all data set of problems

Wavelet-Based Kernel Construction for Heart Disease Classification

© 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERINGHeart disease classification plays an important role in clinical diagnoses. The performance improvement of an Electrocardiogram classifier is therefore of great relevance, but it is a challenging task too. This paper proposes a novel classification algorithm using the kernel method. A kernel is constructed based on wavelet coefficients of heartbeat signals for a classifier with high performance. In particular, a wavelet packet decomposition algorithm is applied to heartbeat signals to obtain the Approximation and Detail coefficients, which are used to calculate the parameters of the kernel. A principal component analysis algorithm with the wavelet-based kernel is employed to choose the main features of the heartbeat signals for the input of the classifier. In addition, a neural network with three hidden layers in the classifier is utilized for classifying five types of heart disease. The electrocardiogram signals in nine patients obtained from the MIT-BIH database are used to test the proposed classifier. In order to evaluate the performance of the classifier, a multi-class confusion matrix is applied to produce the performance indexes, including the Accuracy, Recall, Precision, and F1 score. The experimental results show that the proposed method gives good results for the classification of the five mentioned types of heart disease.Peer reviewedFinal Published versio