10 research outputs found
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals
Parkinson's disease (PD), a severe and progressive neurological illness,
affects millions of individuals worldwide. For effective treatment and
management of PD, an accurate and early diagnosis is crucial. This study
presents a deep learning-based model for the diagnosis of PD using resting
state electroencephalogram (EEG) signal. The objective of the study is to
develop an automated model that can extract complex hidden nonlinear features
from EEG and demonstrate its generalizability on unseen data. The model is
designed using a hybrid model, consists of convolutional neural network (CNN),
bidirectional gated recurrent unit (Bi-GRU), and attention mechanism. The
proposed method is evaluated on three public datasets (Uc San Diego Dataset,
PRED-CT, and University of Iowa (UI) dataset), with one dataset used for
training and the other two for evaluation. The results show that the proposed
model can accurately diagnose PD with high performance on both the training and
hold-out datasets. The model also performs well even when some part of the
input information is missing. The results of this work have significant
implications for patient treatment and for ongoing investigations into the
early detection of Parkinson's disease. The suggested model holds promise as a
non-invasive and reliable technique for PD early detection utilizing resting
state EEG
Analysis of speech and other sounds
This thesis comprises a study of various types of signal processing techniques, applied to the tasks of extracting information from speech, cough, and dolphin sounds.
Established approaches to analysing speech sounds for the purposes of low data rate speech encoding, and more generally to determine the characteristics of the speech signal, are reviewed. Two new speech processing techniques, shift-and-add and CLEAN (which have previously been applied in the field of astronomical image processing), are developed and described in detail. Shift-and-add is shown to produce a representation of the long-term "average" characteristics of the speech signal. Under certain simplifying assumptions, this can be equated to the average glottal excitation. The iterative deconvolution technique called CLEAN is employed to deconvolve the shift-and-add signal from the speech signal. Because the resulting "CLEAN" signal has relatively few non-zero samples, it can be directly encoded at a low data rate. The performance of a low data rate speech encoding scheme that takes advantage of this attribute of CLEAN is examined in detail. Comparison with the multi-pulse LP C approach to speech coding shows that the new method provides similar levels of performance at medium data rates of about 16kbit/s.
The changes that occur in the character of a person's cough sounds when that person is afflicted with asthma are outlined. The development and implementation of a micro-computer-based cough sound analysis system, designed to facilitate the ongoing study of these sounds, is described. The system performs spectrographic analysis on the cough sounds. A graphical user interface allows the sound waveforms and spectra to be displayed and examined in detail. Preliminary results are presented, which indicate that the spectral content of cough sounds are changed by asthma.
An automated digital approach to studying the characteristics of Hector's dolphin vocalisations is described. This scheme characterises the sounds by extracting descriptive parameters from their time and frequency domain envelopes. The set of parameters so obtained from a sample of click sequences collected from free-ranging dolphins is analysed by principal component analysis. Results are presented which indicate that Hector's dolphins produce only a small number of different vocal sounds. In addition to the statistical analysis, several of the clicks, which are assumed to be used for echo-location, are analysed in terms of their range-velocity ambiguity functions.
The results suggest that Hector's dolphins can distinguish targets separated in range by about 2cm, but are unable to separate targets that differ only in their velocity
A right hemisphere advantage for processing blurred faces
No description supplie
Proceedings of the 9th international conference on disability, virtual reality and associated technologies (ICDVRAT 2012)
The proceedings of the conferenc