Improving Data quality for the detection of Obstructive Sleep Apnea(OSA), using adversarial training for deep neural architectures
Abstract: In many real-world situations, biological data collected from body sensors have low data quality due to various reasons (misplacement, bad sensor quality, movement of the patient etc). This can negatively impact the potential application of the training procedures of machine learning/ deep learning models, and thus decrease their class decision performance. Real world OSA recordings are a prime example of this type of data . Many signal processing techniques exist that preprocess the data before the classification procedure in order to make them more suitable for training. Adversarial training is an idea mostly used in the context of deep learning, where an optimization algorithm tries to find and exploit weaknesses in the deep learning model by slightly altering the data. However with this procedure the deep model also learns the manipulation and thus becomes better at handling it. We are interested in employing this idea to low quality real world OSA recordings, where the manipulation of the adversary will be related to a mapping of the low quality behavior.
-Basic knowledge of Python
-Basic calculus and probability theory background
-Interest in machine learning