A Gender Recognition System from Facial Image

Epilepsy is a severe neurological disorder characterized by recurrent seizures, which increases the risk of death three times more than normal. Currently Electroencephalography (EEG) has emerged as a highly promising technique for the diagnosis of epilepsy. The majority of current EEG-based epilepsy detection research have employed a variety of deep learning-based models, but most of the approaches suffer from poor generalizability, optimal design and performance rates. To address these issues, this study aims to develop an efficient framework based on deep spatio-temporal neural network called convolutional long short-term memory (ConvLSTM) for epilepsy detection from EEG signals. In the proposed model, firstly standard 19 channel EEG data are selected and resampled at 256Hz, and then those signals are segmented into three-second time frames. Afterward, the segmented data are fed as input to the ConvLSTM model for identifying epileptic patients from normal subjects. To generalize the proposed model, we have tested it on two different datasets with varying population sizes. We have used the five-fold cross validation and leave-one-out cross validation schemes to eliminate the experiment's biases. To further validate the proposed framework, we have carried out various ablation studies. The experimental results demonstrate that the proposed model outperforms the current state-of-theart results for the studied datasets, making it suitable for use as an automated system for the diagnosis of epilepsy.

show abstract

A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG

Tawhid

Siuly

Wang

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.

show abstract

A Gender Recognition System from Facial Image

Cited by 4 publications

References 24 publications

Performance Enhancement For Gender Recognition Using Trainable Bank of Gabor Filters and NCA

Performance Enhancement For Gender Recognition Using Trainable Bank of Gabor Filters and NCA

A Convolutional Long Short-Term Memory-Based Neural Network for Epilepsy Detection From EEG

A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG

Contact Info

Product

Resources

About