student profile: Miss Rim Haidar


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Thesis work

Thesis title: Data mining sleep data for sleep disorders classification and sleep/wake detection

Supervisors: Irena KOPRINSKA , Bryn JEFFRIES

Thesis abstract:

The research focus on mining data such as actigraphy, Polysomnography (PSG), and electroencephalography (EEG) related to human sleep and alertness. The aim of this research includes testing and examining the ability of neural networks (especially deep neural networks) and machine learning algorithms in detecting sleep disorders, sleep disorders events and patterns. Deep neural networks are a powerful set of algorithms known for their ability to automatically learn and extract features from the raw data without prior knowledge. Deep Neural Networks includes Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM). These networks have shown a great success in image classification and feature engineering (especially CNNs), our objective is to investigate the ability of such networks with time series data of sleep signals.


Selected publications

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Conferences

  • Haidar, R., McCloskey, S., Koprinska, I., Jeffries, B. (2018). Convolutional Neural Networks on Multiple Respiratory Channels to Detect Hypopnea and Obstructive Apnea Events. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • McCloskey, S., Haidar, R., Koprinska, I., Jeffries, B. (2018). Detecting hypopnea and obstructive apnea events using convolutional neural networks on wavelet spectrograms of nasal airflow. 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2018), Cham: Springer. [More Information]
  • Haidar, R., Koprinska, I., Jeffries, B. (2017). Sleep Apnea Event Detection from Nasal Airflow Using Convolutional Neural Networks. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]

2018

  • Haidar, R., McCloskey, S., Koprinska, I., Jeffries, B. (2018). Convolutional Neural Networks on Multiple Respiratory Channels to Detect Hypopnea and Obstructive Apnea Events. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • McCloskey, S., Haidar, R., Koprinska, I., Jeffries, B. (2018). Detecting hypopnea and obstructive apnea events using convolutional neural networks on wavelet spectrograms of nasal airflow. 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2018), Cham: Springer. [More Information]

2017

  • Haidar, R., Koprinska, I., Jeffries, B. (2017). Sleep Apnea Event Detection from Nasal Airflow Using Convolutional Neural Networks. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]

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