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The need of data analytics in health informatics for better decision making is a challenging domain for the past decade. This stimulates more interest of researchers for the design of data driven models on the basis of machine learning such as deep learning models for health informatics. Deep learning is an emerging machine learning technique with various applications for health care monitoring such as medical imaging, bioinformatics, pervasive sensing and public health care, etc. There exist various deep learning approaches with their own pros and cons for health informatics to address multiple challenges in medical data processing such as high dimensional, heterogeneous, incomplete, unstructured biomedical, temporally dependent and irregular data, and so on. Deep learning techniques have their own added characteristics suited for health informatics such as enhanced performance, end-to-end learning …