Qualification: 
MBBS

Biographical Sketch

Dr. Ekanath Rangan received his MBBS from Amrita School of Medicine, a top-5 ranked University in India, winning Gold Medals for highest scores in general medicine and surgery. He has remarkable level of initiative and innovation in the synergistic intersection of medicine, wearable sensors, and artificial intelligence. He has co-authored numerous papers in reputed international journals and conferences and holds two US patents which propose novel systems for IoT based remote monitoring, smart and connected m-health, and techniques for data to decisions so as to deliver the 3P's of modern medicine: precision, personalization, and prevention. Particularly noteworthy, are his deep learning LSTM techniques for non-invasive single sensor based sleep apnea diagnosis.

In addition to architecting a COVID remote patient monitoring system for risk stratification and severity prediction, he is also a co-PI on Indian Government funded Indo-US project for discovery of early warning biomarkers of COVID-19.

Dr. Ekanath is a recipient of US NSF fellowship (2015) and excellence award for a talk titled "Rapid Health Alerts Using Multiple Sensors” delivered at University of California-San Francisco Bioengineering symposium (2016). At Amrita, he organized the first of its kind Research Synergy Meet, bringing together more than 50 researchers in medicine, engineering, and computer science from five different campuses, to deliberate on clinical problems and digital solutions.

Awards

2020

  • Ranked First with Distinction (only medical student in present graduating class to secure distinction), awarded the Gold Medal for highest scores in both General Medicine and General Surgery
  • Institute Medal for excellence in Research activities
  • Award Citation: “Best Outgoing Student - Graduating class of 2020”: The award citation reads as follows: “This very special award is being bestowed upon Ekanath for his extraordinary accomplishments in academics, research, and service, all as a graduate student in medicine here at Amrita. He is the co-inventor of the Digital Health at Every Doorstep system that architected novel Artificial Intelligence based smart and connected wearable IoMTs (Internet of Medical Things) for data-driven precision medicine, achieving breakthrough outcomes for medical conditions such as sleep apnea and AHE. He has written over 22 papers in international conferences and journals, and two american patents, all under the direct guidance of world renowned humanitarian leader Sri Mata Amritanandamayi Devi”

2019

  • First Prize in Research Paper Presentation, National Medical Summit, Cochin, India

2018

  • Award for excellent presentation, “Internet of Things Based Smart Edge for Global Health: Remote Monitoring with Severity Detection and Alerts Transmission” at 2nd Inter-Amrita Research Synergy Day, May 4th 2018, Amrita Institute of Medical Sciences.

2016

Award for excellent presentation, "Rapid Health Alerts Using Multiple Sensors" at University of California at San Francisco Bioengineering Symposium, June 14, 2016, Only international student speaker invited to present in an all University of California Symposium

2015

Summer Undergraduate Research Fellowship funded by the NSF at the University of California San Diego.

International Experience:

  • Joint Research Proposals: Co-Principal Investigator
    Collaborating Institution Title of the Research Project
    Indo-US Biomolecular Knowledge Network for COVID-19 – Genome and Exposome

    Also, Member of the Steering committee for the Amrita-UCSD (School of Medicine and Qualcomm Institute) collaborative research initiative on COVID-19 and the Human Plasma Exposome
    Re-Engineering Health – Research Synergy Meet
    Pandemic Intervention & Monitoring System (Indo-Australia)
  • International Internship: April-May 2019: Australia (Melbourne, Sydney, Brisbane and Sunshine Coast); Served as the General physician and provided primary care to pediatric, adult, and geriatric patients from diverse areas of Australia, Europe, UK, USA, Japan, Singapore, and India
  • Program Coordinator, Amrita Research Synergy (Engineering in Medicine & Digital Health) Conference, Amrita Institute of Medical Sciences and Research, May 4, 2018.

Publications

Publication Type: Patent

Year of Publication Title

2020

Dr. Maneesha V. Ramesh, Rahul K Pathinarupothi, and Ekanath Srihari Rangan, “Systems, methods, and devices for remote health monitoring and management”, 2020.[Abstract]


A remote health monitoring system, method and device is disclosed. The systems utilize one or more sensors, data aggregation and transmission units, mobile computing devices, processing, analytics and storage (PAS) units, and a framework based on a novel location- and power-aware communication systems and analytics to notify and manage patient health. Methods to transmit data to a PAS unit through the patients' smart phone that is connected to internet, abnormality detection in the data, advanced analytical diagnostics and communication system between the health service provider (HSP) and patient are also provided. The health monitoring systems, methods and devices allows for continuous monitoring of the patient without disrupting their normal lives, provides access even in sparsely connected and remote regions which lack good healthcare facilities, allows intervention by specialized practitioners, and sharing of resource or information in the existing healthcare facilities.

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2020

Dr. Maneesha V. Ramesh, Rahul K Pathinarupothi, and Ekanath Srihari Rangan, “Systems and Methods for Remote Health Monitoring and Management”, U.S. Patent US Patent 10,542,8892020.

2019

Dr. Maneesha V. Ramesh, Rahul K Pathinarupothi, Ekanath Srihari Rangan, Durga P, and P Rangan, V., “Systems, methods, and devices for remote health monitoring and management using internet of things sensors”, U.S. Patent US16/117,6892019.[Abstract]


A health-monitoring system has IoT-vitals sensing nodes joined to a patient's body, sensing vital characteristics, employing wireless transmission circuitry transmitting sensed data by a short-range network, and a local gateway having wireless circuitry receiving transmitted data from the IoT-vitals sensors, software (SW) executing on a processor from a non-transitory medium, the SW processing the transmitted data received, and transmission circuitry transmitting processed data over a long-range network.

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Publication Type: Conference Paper

Year of Publication Title

2018

R. Krishnan Pathinarupothi, Soublet, A., Ekanath Srihari Rangan, V, K. E., Durga P, and Menon, K. A. Unnikris, “Internet-of-Things Based Respiratory Rate Monitoring for Early Detection of Cardiovascular and Pulmonary Diseases”, in HealthyIoT 2018 - 5th EAI International Conference on IoT Technologies for HealthCare, 2018.

2018

Durga P, Rahul K Pathinarupothi, Ekanath Srihari Rangan, and Prakash Ishwar, “When Less is Better: A Summarization Technique that Enhances Clinical Effectiveness of Data”, in 8th ACM International Digital Health Conference (DH 2018), Lyon, France, 2018.[Abstract]


The increasing number of wearable sensors for monitoring of various vital parameters such as blood pressure (BP), blood glucose, heart rate (HR), etc., has opened up an unprecedented opportunity for personalized real-time monitoring and prediction of critical health conditions of patients. This, however, also poses the dual challenges of identifying clinically relevant information from vast volumes of sensor time series data and of storing and communicating it to health-care providers especially in the context of rural areas of developing regions where communication bandwidth may be limited. One approach to address these challenges is data summarization, but the danger of losing clinically useful information makes it less appealing to medical practitioners. To overcome this, we develop a data summarization technique called RASPRO (Rapid Active Summarization for effective PROgnosis), which transforms raw sensor time series data into a series of low bandwidth, medically interpretable symbols, called “motifs”, which measure criticality and preserve clinical effectiveness benefits for patients. We evaluate the predictive power and bandwidth requirements of RASPRO on more than 16,000 minutes of patient monitoring data from a widely used open source challenge dataset. We find that RASPRO motifs have much higher clinical efficacy and efficiency (20 − 90% improvement in F1 score over bandwidths ranging from 0.2–0.75 bits/unit-time) in predicting an acute hypotensive episode (AHE) compared to Symbolic Aggregate approXimation (SAX) which is a state-of-the-art data reduction and symbolic representation method. Furthermore, the RASPRO motifs typically perform as well or much better than the original raw data time series, but with up to 15-fold reduction in transmission/storage bandwidth thereby suggesting that less is better.

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2018

Rahul K Pathinarupothi, Ekanath Srihari Rangan, and Durga P, “Deriving High Performance Alerts from Reduced Sensor Data for Timely Intervention in Acute Hypotensive Episodes”, in 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 18) , Honolulu, Hawaii, 2018.[Abstract]


Alerting critical health conditions ahead of time leads to reduced mortality rates. Recently wirelessly enabled medical sensors have become pervasive in both hospital and ambulatory settings. These sensors pour out voluminous data that are generally not amenable to direct interpretation. For this data to be practically useful for patients, they must be translatable into alerts that enable doctors to intervene in a timely fashion. In this paper we present a novel three-step technique to derive high performance alerts from voluminous sensor data: A data reduction algorithm that takes into account the medical condition at personalized patient level and thereby converts raw multi-sensor data to patient and disease specific severity representation, which we call as the Personalized Health Motifs (PHM). The PHMs are then modulated by criticality factors derived from interventional time and severity frequency to yield a Criticality Measure Index (CMI). In the final step we generate alerts whenever the CMI crosses patient-disease-specific thresholds. We consider one medical condition called Acute Hypotensive Episode (AHE). We evaluate the performance of our CMI derived alerts using 7,200 minutes of data from the MIMIC II database. We show that the CMI generates valid alerts up to 180 minutes prior to onset of AHE with F1 score, precision and recall of 0.8, 1.0 and 0.67 respectively, outperforming alerts from raw data.

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2017

Ekanath Srihari Rangan and Rahul K Pathinarupothi, “Multi-Sensor Architecture and Algorithms for Digital Health at Every Doorstep”, in IEEE International Conference on Electrical, Computer and Communication Technologies, Coimbatore, India, 2017.[Abstract]


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 multidisciplinary 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.

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2017

Rahul K Pathinarupothi and Ekanath Srihari Rangan, “Consensus Motifs as Adaptive and Efficient Predictors for Acute Hypotensive Episodes”, in IEEE EMBC , 39th Annual International Conference ofthe IEEE Engineering in Medicine and Biology Society, Jeju Island, Korea, 2017.[Abstract]


Acute hypotensive episodes (AHE) are characterized by continuously low blood pressure for prolonged time, and could be potentially fatal. We present a novel AHE detection system, by first quantizing the blood pressure data into clinically accepted severity ranges and then identifying most frequently occurring blood pressure pattern among these which we call consensus motifs. We apply machine learning techniques (support vector machine) on these consensus motifs. The results show that the use of consensus motifs instead of raw time series data extends the predictability by 45 minutes beyond the 2 hours that is possible using only the raw data, yielding a significant improvement without compromising the clinical accuracy. The system has been implemented as part of a new framework called RASPRO (Rapid Summarization for Effective Prognosis) that we have developed for Wireless Remote Health Monitoring.

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2017

Rahul K Pathinarupothi, Dhara Prathap J, Ekanath Srihari Rangan, Gopalakrishnan E A, Vinaykumar R, and Dr. Soman K. P., “Single Sensor Techniques for Sleep Apnea Diagnosis Using Deep Learning”, in IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, USA, 2017.[Abstract]


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.

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2017

Ramkumar Narayanan, Uma Gopalakrishnan, and Ekanath Srihari Rangan, “Gaze Alignment Techniques for Multipoint Mobile Telemedicine for Ophthalmological Consultations”, in 7th EAI International Conference on Wireless Mobile Communication and Health care (MobiHealth 2017), Vienna, Austria, 2017.[Abstract]


Telemedical consultation systems are emerging as a viable medium for patient-doctor interaction in a number of medical specialties. Such systems are already prevalent in fields like cardio diagnosis and it is still very nascent in the field of ophthalmology. But with the emergence of affordable and high quality remote-control cameras, a host of new possibilities have opened up. In this paper, we have developed innovative gaze alignment techniques for ensuring Mutual Gaze, Gaze Awareness and Gaze following. The system is shown to work effectively even for interactions that are as complex as involving multiparty consultations involving remotely located patients through the use of a mobile telemedicine network and general physician/physician-assistant and specialist ophthalmologist.

Gaze Alignment Techniques for Multipoint... (PDF Download Available). Available from: https://www.researchgate.net/publication/320979757_Gaze_Alignment_Techni... [accessed Apr 23 2018].

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2017

Rahul K Pathinarupothi, Vinaykumar R, Ekanath Srihari Rangan, Dr. E. A. Gopalakrishnan, and Dr. Soman K. P., “Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning”, in IEEE International Conference on Biomedical and Health Informatics, Orlando, Florida, 2017, pp. 293-296.[Abstract]


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.

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2017

Rahul K Pathinarupothi, Bithin Alangot, and Ekanath Srihari Rangan, “Severity Summarization and Just in Time Alert Computation in mHealth Monitoring”, in EFMI International Conference on Informatics for Health, Manchester, UK , 2017, vol. 235, pp. 48-52.[Abstract]


Mobile health is fast evolving into a practical solution to remotely monitor high-risk patients and deliver timely intervention in case of emergencies. Building upon our previous work on a fast and power efficient summarization framework for remote health monitoring applications, called RASPRO (Rapid Alerts Summarization for Effective Prognosis), we have developed a real-time criticality detection technique, which ensures meeting physician defined interventional time. We also present the results from initial testing of this technique.

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2016

Rahul K Pathinarupothi and Ekanath Srihari Rangan, “Effective Prognosis Using Wireless Multi-sensors for Remote Healthcare Service”, in Healthwear 2016: International Conference on Wearables in Healthcare in Budapest, Hungary, 2016.[Abstract]


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.

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2016

Ekanath Srihari Rangan and Rahul K Pathinarupothi, “Rapid Healthcare Alerts using Multiple Sensors”, in 38th IEEE Annual International Conference of Engineering in Medicine and Biology, Orlando, FLorida, USA, 2016.[Abstract]


We combine a multi-sensor network with motif-based data summarization methodology leading to effective prognosis of disease conditions of patients via wide area wireless networks. 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 whole system is the result of close collaboration between engineering and medical teams at one of the best known multidisciplinary universities. We have developed a prototype of the system consisting of wearable wireless ECG and other healthcare IoT (Internet of Things) devices.

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2016

Ekanath Srihari Rangan and Rahul K Pathinarupothi, “Adaptive Motif-Based Alerts for Mobile Health Monitoring”, in Mobihealth 2016 (6th International Conference on Wireless Mobile Communication and Healthcare - Transforming healthcare through innovations in mobile and wireless technologies), , Milan, Italy, 2016.[Abstract]


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.

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2016

Durga P, Ekanath Srihari Rangan, and Rahul K Pathinarupothi, “Real-time identification of ischemic events in high risk cardiac patients”, in IEEE International Conference on Computational Intelligence and Computing Research, Chennai, India, 2016.[Abstract]


There is a worldwide trend of increase in cardiac related deaths. One of the major reasons is the condition of cardiac ischemia, which implies inadequacy of blood supply to heart leading to myocardial infarction. One of the main techniques used for detection of ischemia is 12-lead ECG test. However, on most occasions the patient may not be attached to any such devices so as to provide immediate medical help. This emphasizes the need for real time detection of such events. With advances in the field of communication and smartphone-based computations, we are now able to use body attached sensors and smartphone based solutions for real-Time detection of diseases. In our work, we introduce a real-Time smartphone based ischemia detection system, which combines ECG signals from patients along with their activity for identification of ischemia. As an initial step, the impact of patient activity on ischemia is studied, with a comparison between severity threshold method and contextual severity threshold technique. We also present initial test results of this system. Initial results suggest that activities of patient needs to be considered in any ischemia detection system. © 2016 IEEE.

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2016

J. D. Prathap, Ekanath Srihari Rangan, and Pathinarupothi, R. K., “Real-time and offline techniques for identifying obstructive sleep apnea patients”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India, 2016.[Abstract]


Obstructive Sleep Apnea (OSA) is a common sleeping disorder in which persons temporarily stop breathing during their sleep. Untreated OSA may lead to several cardio vascular diseases, diabetes, stroke etc. Currently, overnight Polysomnography (PSG) is the widely used technique to detect sleep apnoea. However, a human expert has to monitor the patient overnight. In this paper, we use the technique of motif discovery to identify long term patterns in vital parameters obtained from a combination of smart phones and body attached sensors. We further extend this work to use hamming distance technique to identify similar patients for case based reasoning. Using this, we reduce the need for having expert intervention. As an initial implementation, we have tested our motif discovery technique on Physionet sleep apnea dataset of ECG and SpO2.

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2016

Rahul K Pathinarupothi and Ekanath Srihari Rangan, “Large scale remote health monitoring in sparsely connected rural regions”, in GHTC 2016 - IEEE Global Humanitarian Technology Conference: Technology for the Benefit of Humanity, Conference Proceedings, 2016, pp. 694-700.[Abstract]


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 doctor's 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. © 2016 IEEE.

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2016

Ekanath Srihari Rangan and Krishna Das, “A systematic methodology to transform campuses in the developing world into sustainable communities”, in GHTC 2016 - IEEE Global Humanitarian Technology Conference: Technology for the Benefit of Humanity, Conference Proceedings, USA, 2016, pp. 466-473.[Abstract]


In the relentless pursuit of human race to build a better world with technology, there lurk some blind spots in the developing world, and foremost among them is 'sustainable access to energy'. In this project, we develop and demonstrate a systematic methodology for institutional campuses to transform themselves into energy sustainable communities. The methodology starts with an investigation of the electricity consumption trend, followed by the determination of the seasonally variant solar generation capacity. We then compute the minimal geographical area to reach sustainability and present an economic viability model taking into account local energy costs. The methodology is experimentally piloted in a 400-acre campus community of 10000+ residents in rural monsoon dominated tropics of Southern India. Sustainability can be achieved through the deployment of 20 acres' solar panels together generating 13,369 MWh (annually). On the consumption side, smart control panels can help to limit usage to 4,338 MWh, allowing the possibility that a surplus of 9,031 MWh to be re-routed to humanitarian causes of lighting up adjoining low income village households (which otherwise would have been without power), powering campus vehicles and dining services, with potential to reduce the overall carbon footprint by 6452 tons. The initial investment gets paid back in eleven years' time, which is about half the panel lifespan, thereby proving economic viability. Our methodology provides a validated replicable roadmap for developing world communities aspiring to boldly transform into net zero carbon sustainability, thereby realizing the United Nations COP21 mandate. © 2016 IEEE.

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2016

Rahul K Pathinarupothi, Ekanath Srihari Rangan, Bithin Alangot, and Dr. Maneesha V. Ramesh, “RASPRO: rapid summarization for effective prognosis in wireless remote health monitoring”, in 2016 IEEE Wireless Health (WH), 2016.[Abstract]


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.

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2016

Rahul K Pathinarupothi, Dr. Maneesha V. Ramesh, and Ekanath Srihari Rangan, “Multi-Layer Architectures for Remote Health Monitoring”, in 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), Munich, Germany, 2016.[Abstract]


Remote health monitoring and delivery through mobile devices and wireless networks offers unique challenges related to performance, reliability, data size, power management, and analytical complexity. We present a multi-layered architecture that matches communication performance to medical importance of data being monitored. The priority of vital data and the context of sensing are used to select the communication medium and the power management policies. Further smartness is introduced into data summarization by employing a severity level quantizer, followed by a consensus abnormality motif discovery and an alert mechanism that prioritizes doctors' consultative time. We also present our successful implementation of the above multi-layered architecture in a system developed to remotely monitor cardiac patients.

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PDF iconMulti-Layer-Architectures-for-Remote-Health-Monitoring.pdf

2016

Rahul K Pathinarupothi and Ekanath Srihari Rangan, “Discovering Vital Trends for Personalized Healthcare Delivery”, in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, New York, NY, USA, 2016.[Abstract]


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.

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Publication Type: Journal Article

Year of Publication Title

2018

Rahul K Pathinarupothi, Durga P, and Ekanath Srihari Rangan, “IoT Based Smart Edge for Global Health: Remote Monitoring with Severity Detection and Alerts Transmission”, IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2449-2462, 2018.[Abstract]


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 & PHMs directly to the physicians, and best effort pull of detailed data-on-demand (DD-on-D) 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 towards addressing the requirements for global health. IEEE

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2018

Rahul K Pathinarupothi, Durga P, and Ekanath Srihari Rangan, “Data to diagnosis in global health: A 3P approach”, BMC Medical Informatics and Decision Making, vol. 18, no. 1, Article number 78, 2018.[Abstract]


Background: With 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. Methods: To 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. Results: The 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. Conclusion: The 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. © 2018 The Author(s).

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2016

Ekanath Srihari Rangan and Krishna Das, “An Experimental Approach towards Energy Sustainability in University Communities”, International Journal of Applied Environmental Sciences, vol. 11, pp. 667-681, 2016.[Abstract]


Ever rising demand for energy has crossed the parameters and has reached to an irrepressible level where human activities couldn’t go any further unless they address this perilous situation. Most communities and institutions have recently realized that they’ve been very much un-sustainable in their day-today operations. Now their prime concern is not to compete among themselves but to become self-sustainable which they realize as the key to successful business. This paper aims to demonstrate how an institutional community can become self-sustainable in meeting their energy requirements. Our study targets efficient deployment of solar panels in large campus communities to effectively meet the energy sustainability. Sustainability is achieved through the deployment of 1.7 million solar panels in the campus community together generating 13,369 megawatt hours per year. On the consumption side, smart control panels help to limit usage to 4,338 megawatt hours per year, allowing 9,031 megawatt hours to be pushed back to the grid, generating an annual revenue of $0.6 million. This level of surplus helps to defray the initial investment in eleven years’ time, which is about half the lifespan of solar panels, thereby proving the economic viability of the deployment. The methodology is repeatable and replicable in any community aspiring to achieve self-sustainability

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Prizes (Cultural):

  • March 2020: Institute Medal for excellence in arts and music
  • October 2017: First Prize in Percussion, Dakshat Arts Fest 2017
  • March 2016: Gold Medal (First Prize), India's Philosophy and Culture competition

Prizes (Sports):

  • March 2020: Institute Medal for excellence in athletics
  • March 2017: First Prize in 800m and 400m Running – Chaturangam Sports Meet 2017

Prizes (Societal Service):

  • March 2020: Institute Medal for excellence in societal service activities
  • Commendable selfless service, Amritavarsham International Convention, Amritapuri, Kerala, India, September 2014-2016 (annual series)