Ten lectures on wavelets
Determining mental state from EEG signals using parallel implementations of neural networks
Scientific Programming - On applications analysis
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
The classification of human tremor signals using artificial neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Detecting clinically relevant EEG anomalies using discrete wavelet transforms
WAMUS'05 Proceedings of the 5th WSEAS International Conference on Wavelet Analysis and Multirate Systems
Using EEG spectral components to assess algorithms for detecting fatigue
Expert Systems with Applications: An International Journal
Early prostate cancer diagnosis by using artificial neural networks and support vector machines
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Classification of EEG signals using relative wavelet energy and artificial neural networks
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Epileptic seizure detection using dynamic wavelet network
Expert Systems with Applications: An International Journal
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Comparing combinations of EEG activity in train drivers during monotonous driving
Expert Systems with Applications: An International Journal
Feature extraction of forearm EMG signals for prosthetics
Expert Systems with Applications: An International Journal
Wavelet basis functions in biomedical signal processing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A machine learning approach to classify vigilance states in rats
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Diagnosis of breast cancer using hybrid magnetoacoustic method and artificial neural network
ACS'11 Proceedings of the 11th WSEAS international conference on Applied computer science
Journal of Medical Systems
Classification of EMG signals using combined features and soft computing techniques
Applied Soft Computing
A decision support system for EEG signals based on adaptive fuzzy inference neural networks
Journal of Computational Methods in Sciences and Engineering
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Estimating cognitive workload using wavelet entropy-based features during an arithmetic task
Computers in Biology and Medicine
International Journal of Mobile Learning and Organisation
Hi-index | 12.08 |
Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. This paper deals with a novel method of analysis of EEG signals using wavelet transform, and classification using ANN. EEG signals were decomposed into the frequency sub-bands using wavelet transform and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Then these statistical features were used as an input to an ANN with three discrete outputs: alert, drowsy and sleep. The error back-propagation neural network is selected as a classifier to discriminate the alertness level of a subject. EEG signals were obtained from 30 healthy subjects. The group consisted of 14 females and 16 males with ages ranging from 18 to 65 years and a mean age of 33.5 years, and a Body Mass Index (BMI) of 32.4+/-7.3kg/m^2. Alertness level and classification properties of ANN were tested using the data recorded in 12 healthy subjects, whereby the EEG recordings were not used to train the ANN. The statistics were used as a measure of potential applicability of the ANN. The accuracy of the ANN was 95+/-3% alert, 93+/-4% drowsy and 92+/-5% sleep.