Peripartum cardiomyopathy: Review of the literature

Peripartum cardiomyopathy (PPCM) is a rare but serious form of cardiac failure affecting women in the last months of pregnancy or early puerperium. Clinical presentation of PPCM is similar to that of systolic heart failure from any cause, and it can sometimes be complicated by a high incidence of thromboembolism. Prior to the availability of echocardiography, diagnosis was based only on clinical findings. Recently, inclusion of echocardiography has made diagnosis of PPCM easier and more accurate. Its etiopathogenesis is still poorly understood, but recent evidence supports inflammation, viral infection and autoimmunity as the leading causative hypotheses. Prompt recognition with institution of intensive treatment by a multidisciplinary team is a prerequisite for improved outcome. Conventional treatment consists of diuretics, β blockers, vasodilators, and sometimes digoxin and anticoagulants, usually in combination. In resistant cases, newer therapeutic modalities such as immunomodulation, immunoglobulin and immunosuppression may be considered. Cardiac transplantation may be necessary in patients not responding to conventional and newer therapeutic strategies. The role of the anesthesiologist is important in perioperative and intensive care management. Prognosis is highly related to reversal of ventricular dysfunction. Compared to historically higher mortality rates, recent reports describe better outcome, probably because of advances in medical care. Based on current information, future pregnancy is usually not recommended in patients who fail to recover heart function. This article aims to provide a comprehensive updated review of PPCM covering etiopathogeneses, clinical presentation and diagnosis, as well as pharmacological, perioperative and intensive care management and prognosis, while stressing areas that require further research

Do weak readers in rural India automatically read same language subtitles on Bollywood films? An eye gaze analysis

Same Language Subtitling (SLS) of audio-visual content on mainstream TV entertainment to improve mass reading literacy was first conceived and piloted in India. SLS is now being scaled up nationally to ensure that the reading skills of one billion TV viewers, including 600 million weak readers, remain on a lifelong pathway to practice, progress, and proficiency. Will weak readers ignore or try to read along with SLS? Our eye-tracking study investigates this question with 136 weak readers drawn from a remote village in Rajasthan state by showing them popular Hindi film clips of dialog and songs, with and without SLS. We developed an interactive web-based visual analytics tool for exploring eye-tracking data. Based on an analysis of fixations, saccades, and time spent in the subtitle and non-subtitle areas, our main finding is that 70 percent of weak readers engaged in unprompted reading while watching film clips with SLS. We observed that saccadic eye movement is a good indicator to quantify the amount of reading with SLS, and saccadic regression can further differentiate weak readers.  Eye-tracking studies of weak readers watching subtitles are rare, and ours may be the first with subjects from rural India

I2DNet - Design and real-time evaluation of an appearance-based gaze estimation system

Gaze estimation problem can be addressed using either model-based or appearance-based approaches. Model-based approaches rely on features extracted from eye images to fit a 3D eye-ball model to obtain gaze point estimate while appearance-based methods attempt to directly map captured eye images to gaze point without any handcrafted features. Recently, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. However, many appearance-based gaze estimation systems perform well in within-dataset validation but fail to provide the same degree of accuracy in cross-dataset evaluation. Hence, it is still unclear how well the current state-of-the-art approaches perform in real-time in an interactive setting on unseen users. This paper proposes I2DNet, a novel architecture aimed to improve subject-independent gaze estimation accuracy that achieved a state-of-the-art 4.3 and 8.4 degree mean angle error on the MPIIGaze and RT-Gene datasets respectively. We have evaluated the proposed system as a gaze-controlled interface in real-time for a 9-block pointing and selection task and compared it with Webgazer.js and OpenFace 2.0. We have conducted a user study with 16 participants, and our proposed system reduces selection time and the number of missed selections statistically significantly compared to other two systems

Cognitive load estimation in VR flight simulator

This paper discusses the design and development of a low-cost virtual reality (VR) based flight simulator with cognitive load estimation feature using ocular and EEG signals.  Focus is on exploring methods to evaluate pilot’s interactions with aircraft by means of quantifying pilot’s perceived cognitive load under different task scenarios. Realistic target tracking and context of the battlefield is designed in VR. Head mounted eye gaze tracker and EEG headset are used for acquiring pupil diameter, gaze fixation, gaze direction and EEG theta, alpha, and beta band power data in real time. We developed an AI agent model in VR and created scenarios of interactions with the piloted aircraft. To estimate the pilot’s cognitive load, we used low-frequency pupil diameter variations, fixation rate, gaze distribution pattern, EEG signal-based task load index and EEG task engagement index. We compared the physiological measures of workload with the standard user’s inceptor control-based workload metrics. Results of the piloted simulation study indicate that the metrics discussed in the paper have strong association with pilot’s perceived task difficulty