This work attempts to reduce the number of false alarms generated by bedside monitors in the intensive care unit (ICU), as a majority of current alarms are false. In this study, we applied methods that can be categorized into three stages: signal processing, feature extraction, and optimized machine learning. At the stage of signal processing, we ensured that the heartbeats were properly annotated. During feature extraction, besides extracting features that are relevant to the arrhythmic alarms, we also extracted a set of signal quality indices (SQIs), which we used to distinguish noise/artifact from normal physiological signals. When applying a machine learning algorithm (Random Forest), we performed feature selection in order to reduce the complexity of the models and improve the efficiency of the algorithm. The dataset used is from Reducing False Arrhythmia Alarms in the ICU: the PhysioNet/Computing in Cardiology Challenge 2015. Using the performance metric “score” from the Challenge, we achieved a score of 83.08 in the real-time category on the hidden test set, which is the highest in all published work.

Cardio-respiratory monitoring is one of the most demanding areas in the rapidly growing, mobile-device, based health care delivery. We developed a 12-lead smartphone-based electrocardiogram (ECG) acquisition and monitoring system (called “cvrPhone”), and an application to assess underlying ischemia, and estimate the respiration rate (RR) and tidal volume (TV) from analysis of electrocardiographic (ECG) signals only. During in-vivo swine studies (n = 6), 12-lead ECG signals were recorded at baseline and following coronary artery occlusion. Ischemic indices calculated from each lead showed statistically significant (p < 0.05) increase within 2 min of occlusion compared to baseline. Following myocardial infarction, spontaneous ventricular tachycardia episodes (n = 3) were preceded by significant (p < 0.05) increase of the ischemic index ~1–4 min prior to the onset of the tachy-arrhythmias. In order to assess the respiratory status during apnea, the mechanical ventilator was paused for up to 2 min during normal breathing. We observed that the RR and TV estimation algorithms detected apnea within 7.9 ± 1.1 sec and 5.5 ± 2.2 sec, respectively, while the estimated RR and TV values were 0 breaths/min and less than 100 ml, respectively. In conclusion, the cvrPhone can be used to detect myocardial ischemia and periods of respiratory apnea using a readily available mobile platform.

Chikungunya is a relatively benign disease, and a paucity of literature on severe manifestations in children exits. We describe a cohort of pediatric chikungunya fever patients in New Delhi, India, who had severe sepsis and septic shock, which can develop during the acute phase of illness.

No abstract

Arterial stiffness (AS) has been shown to be an important marker for risk assessment of cardiovascular events. Local arterial stiffness (LAS) is conventionally measured by evaluating arterial distensibility at particular arterial sites through ultrasound imaging systems. Regional arterial stiffness (RAS) is generally obtained by evaluating carotid to femoral pulse wave velocity (cfPWV) through tonometric devices. RAS has a better prognostic value than LAS and cfPWV is considered as the gold standard of AS. Over the past few years our group has been developing ARTerial Stiffness Evaluation for Non-Invasive Screening (ARTSENS), an inexpensive and portable device to measure the LAS. It uses a single element ultrasound transducer to obtain A-Mode frames from the desired artery and is fully automated to enable a non-expert to perform measurements. In this work, we report an extension of ARTSENS to enable measurement of cfPWV that now makes it the only fully automatic device that can measure both LAS and RAS. In this paper, we provide a general review of the ARTSENS and compare it with other state-of-the-art AS measurement systems. cfPWV measurement using ARTSENS was cross-validated against SphygmoCor by successive measurements with both devices on 41 human subjects and excellent agreement between both devices was demonstrated (Coefficient of determination and, limits of agreement m/s). The inter-device correlation between ARTSENS and SphygmoCor was found to be better than other similar studies reported in the literature.