Ekanath Srihari Rangan
Amrita Vishwa Vidyapeetham
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Publication
Featured researches published by Ekanath Srihari Rangan.
2016 IEEE Wireless Health (WH) | 2016
Rahul Krishnan Pathinarupothi; Ekanath Srihari Rangan; Bithin Alangot; Maneesha Vinodini Ramesh
Consistent power and cost effective health monitoring has become the need of the hour especially for the unstable, chronically and critically ill. Here we present a novel architecture and algorithmic methodology combining the sensing subsystem, symptom summarization, and data transmission. Physiological parameters from multiple sensors feed into a severity quantizer and a subsequent multiplexer, the output of which is processed by the RASPRO engine to rapidly discover and alert any health criticalities. The architecture is optimized for communication and energy performance, and the algorithms result in lucid presentations to physicians. The whole system is the result of close collaboration between engineering and medical teams at one of the best known multidisciplinary universities, building on a multi-terabyte more than a million patient Amrita Hospital Information System (HIS) database, and is being readied for deployment on a large telemedicine network of more than 60 nodes in the Indian subcontinent and parts of Africa.
ubiquitous computing | 2016
Rahul Krishnan Pathinarupothi; Ekanath Srihari Rangan
Personalization of remote health monitoring and healthcare delivery is a challenging research problem faced by practitioners and researchers alike. In this paper we present techniques for trend analysis and data summarization using personalized severity based motif discovery in large time series medical data.
Healthwear 2016: International Conference on Wearables in Healthcare in Budapest | 2017
Rahul Krishnan Pathinarupothi; Ekanath Srihari Rangan
Remote healthcare delivery is one of the most promising solutions to tackle global trends in falling health care access and quality of service. A wireless network of sensors, IoT devices, and cloud is presented here. New innovative algorithms for effective prognosis are designed and developed based on motifs and profile matrices. The system consisting of the sensor network and algorithms together enable delivering remote healthcare services.
global humanitarian technology conference | 2016
Rahul Krishnan Pathinarupothi; Ekanath Srihari Rangan
Remote health monitoring and intervention systems including wearable sensors, smartphones and advanced communication technologies are slated to be a game changer in the delivery of quality healthcare services, especially in developing parts of the world. However, we are yet to see large scale adoption of remote health monitoring systems due to many factors such as: lack of reliable data network coverage, high power requirements for smartphone analytics, and unreliability in the timely delivery of critical data to remote doctors. In addition to these, the huge volume of sensor data and alerts from multiple remote patients are unmanageable for already overloaded doctors. In this paper, we attempt to address each of these issues. First, we propose a novel healthcare communication architecture that connects remotely stationed telemedicine nodes and village clinics with remote doctors in specialty hospitals. Second, we present the development of disease severity pattern discovery and summarization algorithms, the result of which is a Consensus Abnormality Motif (CAM) and an associated Alert Measure Index, which suggests the immediacy of the patient data for doctors consultative time. By frequently sending CAM as SMS in the absence of data network, we ensure timely delivery of critical data. Through a Detailed Data on Demand (DD-on-D) pull data mechanism doctors can further investigate complete data from the cloud. The CAM and DD-on-D mechanisms result in energy savings of up to 25%, while the data usage is reduced tremendously. Furthermore, we present a pilot deployment of the systems using a continuous cardiac monitoring device coupled with an intervention framework including more than 60 telemedicine nodes station in villages across India.
ieee embs international conference on biomedical and health informatics | 2017
Rahul Krishnan Pathinarupothi; R Vinaykumar; Ekanath Srihari Rangan; E. A. Gopalakrishnan; K. P. Soman
Automated sleep apnea detection and severity identification has largely focused on multivariate sensor data in the past two decades. Clinically too, sleep apnea is identified using a combination of markers including blood oxygen saturation, respiration rate etc. More recently, scientists have begun to investigate the use of instantaneous heart rates for detection and severity measurement of sleep apnea. However, the best-known techniques that use heart rate and its derivatives have been able to achieve less than 85% accuracy in classifying minute-to-minute apnea data. In our research reported in this paper, we apply a deep learning technique called LSTM-RNN (long short-term memory recurrent neural network) for identification of sleep apnea and its severity based only on instantaneous heart rates. We have tested this model on multiple sleep apnea datasets and obtained perfect accuracy. Furthermore, we have also tested its robustness on an arrhythmia dataset (that is highly probable in mimicking sleep apnea heart rate variability) and found that the model is highly accurate in distinguishing between the two.
IEEE Internet of Things Journal | 2018
Rahul Krishnan Pathinarupothi; P Durga; Ekanath Srihari Rangan
Global health which denotes equitable access to healthcare, particularly in remote-rural-developing regions, is characterized by unique challenges of affordability, accessibility, and availability for which one of the most promising technological interventions that is emerging is the Internet of Things (IoT)-based remote health monitoring. We present an IoT-based smart edge system for remote health monitoring, in which wearable vital sensors transmit data into two novel software engines, namely rapid active summarization for effective prognosis (RASPRO) and criticality measure index (CMI) alerts, both of which we have implemented in the IoT smart edge. RASPRO transforms voluminous sensor data into clinically meaningful summaries called personalized health motifs (PHMs). The CMI alerts engine computes an aggregate criticality score. Our IoT smart edge employs a risk-stratified protocol consisting of rapid guaranteed push of alerts and PHMs directly to the physicians, and best effort pull of detailed data-on-demand through the cloud. We have carried out both clinical validation and performance evaluation of our smart edge system. The clinical validation on 183 patients demonstrated that the IoT smart edge is highly effective in remote monitoring, advance warning and detection of cardiac conditions, as quantified by three measures, precision (0.87), recall (0.83), and F1-score (0.85). Furthermore, performance evaluation showed significant reductions in the bandwidth (98%) and energy (90%), thereby making it suitable for emerging narrow-band IoT networks. In the deployment of our system in the cardiology institute of our University hospital, we observed that our IoT smart edge helped to increase the availability of physicians by 59%. Hence, our IoT smart edge system is a significant step toward addressing the requirements for global health.
BMC Medical Informatics and Decision Making | 2018
Rahul Krishnan Pathinarupothi; P Durga; Ekanath Srihari Rangan
BackgroundWith connected medical devices fast becoming ubiquitous in healthcare monitoring there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge.MethodsTo address this challenge, we present a 3P approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis). The heart of RASPRO is Physician Assist Filters (PAF) that transform unwieldy multi-sensor time series data into summarized patient/disease specific trends in steps of progressive precision as demanded by the doctor for patient’s personalized condition at hand and help in identifying and subsequently predictively alerting the onset of critical conditions. The output of PAFs is a clinically useful, yet extremely succinct summary of a patient’s medical condition, represented as a motif, which could be sent to remote doctors even over SMS, reducing the need for data bandwidths. We evaluate the clinical validity of these techniques using SVM machine learning models measuring both the predictive power and its ability to classify disease condition. We used more than 16,000 min of patient data (N=70) from the openly available MIMIC II database for conducting these experiments. Furthermore, we also report the clinical utility of the system through doctor feedback from a large super-speciality hospital in India.ResultsThe results show that the RASPRO motifs perform as well as (and in many cases better than) raw time series data. In addition, we also see improvement in diagnostic performance using optimized sensor severity threshold ranges set using the personalization PAF severity quantizer.ConclusionThe RASPRO-PAF system and the associated techniques are found to be useful in many healthcare applications, especially in remote patient monitoring. The personalization, precision, and prevention PAFs presented in the paper successfully shows remarkable performance in satisfying the goals of 3Ps, thereby providing the advantages of three A’s: availability, affordability, and accessibility in the global health scenario.
ieee international conference on healthcare informatics | 2017
Rahul Krishnan Pathinarupothi; Dhara Prathap J; Ekanath Srihari Rangan; Gopalakrishnan E A; R Vinaykumar; K. P. Soman
A large number of obstructive sleep apnea (OSA) cases are under-diagnosed due unavailability, inconvenience or expense of sleep labs. Hence, an automated detection by applying computational techniques to multivariate signals has already become a well-researched subject. However, the best-known techniques that use various features have not achieved the gold standard of polysomnography (PSG) tests. In this paper, we substantiate the medical conjecture that OSA directly impacts body parameters such as Instantaneous Heart Rate (IHR) and blood oxygen saturation (SpO2). We then use a deep learning technique called LSTM-RNN (long short-term memory recurrent neural networks) to experimentally prove that OSA severity detection can be solely based on either IHR or SpO2 signals, which can be easily, obtained using off-the-shelf non-intrusive wearable single sensors. The results obtained from LSTM-RNN model shows an area under curve (AUC) of 0.98 associated with very high accuracy on a dataset of more than 16,000 apnea non-apnea minutes. These results have encouraged our collaborating doctors to further come up with a diagnostic protocol that is based on LSTM-RNN, SpO2, and IHR, thereby increasing the chances of larger adoption among medical community.
ieee international conference on electrical, computer and communication technologies | 2017
Ekanath Srihari Rangan; Rahul Krishnan Pathinarupothi
Consistent cost effective health monitoring has become the need of the hour especially for the unstable, chronically and critically ill. Here we present a novel architecture and algorithmic methodology combining the sensing subsystem and the analytics engines. Physiological parameters from multiple sensors feed into a severity quantizer and a subsequent multiplexer, the output of which is processed by successive physician assist filters to rapidly discover and alert any health criticalities. The architecture is optimized for communication and energy performance, and the algorithms result in lucid presentations to physicians. The whole system is the result of close collaboration between engineering and medical teams at our multi-disciplinary University, building on a multi-terabyte, more than a million patient Hospital Information System (HIS) database, and is being readied for deployment on a large telemedicine network of more than 60 nodes in the Indian subcontinent and parts of Africa.
international conference on wireless mobile communication and healthcare | 2016
Ekanath Srihari Rangan; Rahul Krishnan Pathinarupothi
We have developed a rapid remote health monitoring architecture called RASPRO using wearable sensors and smartphones. RASPRO’s novelty comes from its techniques to efficiently compute compact alerts from sensor data. The alerts are computationally fast to run on patients’ smartphones, are effective to accurately communicate patients’ severity to physicians, take into consideration inter-sensor dependencies, and are adaptive based on recently observed parametric trends. Preliminary implementation with practicing physicians and testing on patient data from our collaborating multi-specialty hospital has yielded encouraging results.