Author Correction: Deep learning for automated sleep staging using instantaneous heart rate

SLEEPNET: Automated Sleep Staging System via Deep Learning

Sleep disorders, such as sleep apnea, parasomnias, and hypersomnia, affect 50-70 million adults in the United States (Hillman et al., 2006). Overnight polysomnography (PSG), including brain monitoring using electroencephalography (EEG), is a central component of the diagnostic evaluation for sleep disorders. While PSG is conventionally performed by trained technologists, the recent rise of powe...

Personalizing deep learning models for automatic sleep staging

Despite continued advancement in machine learning algorithms and increasing availability of large data sets, there is still no universally acceptable solution for automatic sleep staging of human sleep recordings. One reason is that a skilled neurophysiologist scoring brain recordings of a sleeping person implicitly adapts his/her staging to the individual characteristics present in the brain r...

Automated Selection of Optimal ECG Lead Using Heart Instantaneous Frequency During Sleep

Towards Automated Tuberculosis detection using Deep Learning

Tuberculosis(TB) in India is the world’s largest TB epidemic [1]. TB leads to 480,000 deaths every year [2]. Between the years 2006 and 2014, Indian economy lost US$340 Billion due to TB. This combined with the emergence of drug resistant bacteria in India makes the problem worse [3]. The government of India has hence come up with a new strategy which requires a high-sensitivity microscopy base...

Towards Automated ICD Coding Using Deep Learning

International Classification of Diseases(ICD) is an authoritative health care classification system of different diseases and conditions for clinical and management purposes. Considering the complicated and dedicated process to assign correct codes to each patient admission based on overall diagnosis, we propose a hierarchical deep learning model with attention mechanism which can automatically...