Developing enhanced classification methods for ECG and EEG signals

Zarei, Roozbeh (2017) Developing enhanced classification methods for ECG and EEG signals. PhD thesis, Victoria University.


A huge amount of biomedical data such as Electrocardiography (ECG) and Electroencephalography (EEG) signals are recorded daily from human body to assess and monitor human performance and physiological condition. The analysis of these signals is important for research as well as for medical diagnosis and treatment. Although ECG and EEG signals provide useful information about the heart and brain, the classification of these signals has not been well developed. Even now these signals are often examined manually by physicians. Hence, there is a need for developing automatic classification techniques that evaluate and assess these signals. This thesis presents enhanced methods for the classification of ECG and EEG signals in three areas: the detection of premature ventricular contraction (PVC), the identification of epileptic seizure, and the recognition of motor imagery (MI) tasks in Brain-Computer Interface (BCI).

Item type Thesis (PhD thesis)
Subjects Historical > FOR Classification > 0903 Biomedical Engineering
Historical > FOR Classification > 1102 Cardiorespiratory Medicine and Haematology
Historical > FOR Classification > 1109 Neurosciences
Current > Division/Research > College of Science and Engineering
Keywords BCI, PVC beats, heartbeats, heart, brain, epilepsy, epileptic seizures, premature ventricular contractions, Douglas-Peucker Algorithms, PCA, principal component analysis
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login