Signal Processing and Deep Learning Image Classification

от DeepNetMatlab
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In this project, EEG signals recorded from healthy subjects, seizure free epileptic subjects and seizure epileptic subjects were represented as image by using short time Fourier, wavelet and Hilbert-Huang transforms. Then, CNNs are trained with these images. Finally, unseen images classified with trained networks. With different configurations an accuracy of 100% is achived.

image of username DeepNetMatlab Flag of Turkey K?z?ltepe, Turkey

Обо мне

I'm an electircal engineer and PhD student. I am specialized in but not limited to: - Digital signal processing - Biomedical signal (EEG, ECG, etc) processing - Pre-processing signals for machine learning algorithm - Features extraction for machine learning algorithm - Classification with k nearest neighbor (kNN) - Classification, regression, modeling and prediction with Artificial neural network (ANN) - Classification, regression, modeling and prediction with Support vector machines (SVM) - Image/signal transformation and representation for deep learn. ing algorithms - Image to label classification with CNN - Image to image regression with CNN - Object detection with R-CNN, fast R-CNN, faster R-CNN and YOLO - Image denoising with DnCNN - Image generation with GAN and VAE and etc. - Time series prediction with LSTM - Sequence to label classification with LSTM - Sequence to sequence classification with LSTM

$20 USD/ч.

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