Add to ORKGThis project is about developing novel deep learning methods for detecting abnormalities in time series. Specifically, we will consider the problem of detecting spikes in the EEG of patients of epilepsy as well as recurrent neural networks. We will also analyze the EEG of healthy subjects, as a baseline. This project was concluded on April 2017 and it was found that long short term memory networks work decently well in the spike detection of epileptic spikes. Tuned parameters were also presented in this report.Bachelor of Engineerin
Objective: Visual assessment of the EEG still outperforms current computer algorithms in detecting e...
<p>Electroencephalography (EEG) is a widely used and significant technique for aiding in epile...
Electrophysiological observation plays a major role in epilepsy evaluation. However, human interpret...
Spike like waveforms, which are different from normal background waveforms, are usually discovered i...
Epilepsy is one of the most serious neurological disorders that affects people of all ages. In Canad...
In this paper, an analysis of artificial neural network (ANN) effectivenes, when used as a tool to a...
Interictal Epileptiform Discharge (IED) detection in EEG signals is widely used in the diagnosis of ...
Epilepsy is the most common chronic neurological disorder. Clinical neurologists use Electroencephal...
Objective: Automating detection of Interictal Epileptiform Discharges (IEDs) in electroencephalogram...
Diagnosis of epilepsy can be expensive, time-consuming, and often inaccurate. The gold standard diag...
Diagnosis of epilepsy can be expensive, time-consuming, and often inaccurate. The gold standard diag...
Deep learning is a recently emerged field within machine learning which is gaining more and more att...
The volume, variability and high level of noise in electroencephalographic (EEG) recordings of the e...
Nowadays, machine and deep learning techniques are widely used in different areas, ranging from econ...
Recent advancements in machine learning and deep learning models find them helpful in designing effe...
Objective: Visual assessment of the EEG still outperforms current computer algorithms in detecting e...
<p>Electroencephalography (EEG) is a widely used and significant technique for aiding in epile...
Electrophysiological observation plays a major role in epilepsy evaluation. However, human interpret...
Spike like waveforms, which are different from normal background waveforms, are usually discovered i...
Epilepsy is one of the most serious neurological disorders that affects people of all ages. In Canad...
In this paper, an analysis of artificial neural network (ANN) effectivenes, when used as a tool to a...
Interictal Epileptiform Discharge (IED) detection in EEG signals is widely used in the diagnosis of ...
Epilepsy is the most common chronic neurological disorder. Clinical neurologists use Electroencephal...
Objective: Automating detection of Interictal Epileptiform Discharges (IEDs) in electroencephalogram...
Diagnosis of epilepsy can be expensive, time-consuming, and often inaccurate. The gold standard diag...
Diagnosis of epilepsy can be expensive, time-consuming, and often inaccurate. The gold standard diag...
Deep learning is a recently emerged field within machine learning which is gaining more and more att...
The volume, variability and high level of noise in electroencephalographic (EEG) recordings of the e...
Nowadays, machine and deep learning techniques are widely used in different areas, ranging from econ...
Recent advancements in machine learning and deep learning models find them helpful in designing effe...
Objective: Visual assessment of the EEG still outperforms current computer algorithms in detecting e...
<p>Electroencephalography (EEG) is a widely used and significant technique for aiding in epile...
Electrophysiological observation plays a major role in epilepsy evaluation. However, human interpret...