Simultaneous multiple features tracking of beats: A representation learning approach to reduce false alarm rates in ICUs.

The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the medical staff. Several rule-based and machine learning-based techniques have been developed to address this problem. However, the majority of these methods rely on the availability of different physiological signals such as different electrocardiogram (ECG) leads, arterial blood pressure (ABP), and photoplethysmogram (PPG), where each signal is analyzed by an independent processing unit and the results are fed to an algorithm to determine an alarm.

An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs.

The high rate of false alarms in intensive care units (ICUs) is one of the top challenges of using medical technology in hospitals. These false alarms are often caused by patients' movements, detachment of monitoring sensors, or different sources of noise and interference that impact the collected signals from different monitoring devices. In this paper, we propose a novel set of high-level features based on unsupervised feature learning technique in order to effectively capture the characteristics of different arrhythmia in electrocardiogram (ECG) signal and differentiate them from irregularity in signals due to different sources of signal disturbances.

Inverse Feature Learning: Feature Learning Based on Representation Learning of Error.

This paper proposes (IFL) as a novel supervised feature learning technique that learns a set of high-level features for classification based on an approach. The key contribution of this method is to learn the representation of error as high-level features, while current representation learning methods interpret error by loss functions which are obtained as a function of differences between the true labels and the predicted ones. One advantage of this error representation is that the learned features for each class can be obtained independently of learned features for other classes; therefore, IFL can learn simultaneously meaning that it can learn new classes' features without retraining.

NPHS2 mutations in children with steroid-resistant nephrotic syndrome.

Introduction: Congenital nephrotic syndrome may be caused by mutations in NPHS1 and NPHS2, which encode nephrin and podocin, respectively. Since the identification of the NPHS2 gene, various investigators have demonstrated that its mutation is an important cause of steroid-resistant nephrotic syndrome. We aimed to evaluate frequency and spectrum of podocin mutations in the Iranian children with steroid-resistant nephritic syndrome.