Qualification: 
M.Tech
durgap@am.students.amrita.edu

Durga P. currently serves as a Research Scholar at the Amrita Center for Wireless Networks & Applications (Amrita WNA), Amritapuri Campus.

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2018

Conference Paper

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

Conference Paper

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

Conference Paper

Durga P, Narayanan, G., Gayathri, B., Dr. Maneesha V. Ramesh, and Divya, P., “Modelling a Smart Agriculture System for Multiple Cropping Using Wireless Sensor Networks”, in 2017 IEEE Global Humanitarian Technology Conference (GHTC), 2017.[Abstract]


The field of wireless sensor networks is progressing at a very rapid pace with one of its major application in the area of agriculture. Several research problems have been addressed and solutions have been proposed. Most of these works are based on single crop scenario. Research done in the multiple-cropping scenario, where two or more crops are sown in a single field in the same year, are very few. Here, a unique solution for multiple cropping scenarios, in a system design perspective is proposed. The system forms a closed loop by including MAC protocol, data aggregation, routing and localization developed specifically for this scenario.

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2016

Conference Paper

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

Conference Paper

F. C. Robert, Ramanathan, U., ,, Durga P, and Mohan, R., “When academia meets rural India: Lessons learnt from a MicroGrid implementation”, in GHTC 2016 - IEEE Global Humanitarian Technology Conference: Technology for the Benefit of Humanity, Conference Proceedings, 2016, pp. 156-163.[Abstract]


Access to energy has been a lynchpin for the progress of modern civilization in the last century. However, a large fraction of the world's population still remains without electricity. Recent improvements in affordable, renewable energy generation technology, offer us the unique opportunity to realize the dream of global electrification. In this paper, we discuss how academia can translate into successfully meeting the needs of rural development via the Live-in-Labs™ program - an experiential learning initiative in rural India. Through the program, 35 post graduate university students and 15 staff and faculty designed, developed, and deployed a solar microgrid for the electrification of a tribal village in South India. In order to encourage academia to become involved more easily in such rural development programs, we describe our approach from pre-study to post deployment analysis and monitoring. We present practical tips and advice for project organisation, technical design, as well the implementation phase, accompanied with feedback from students. This project introduced tribal villagers to a new world of technology, promoting education and kindling long lasting enthusiasm in the village and students were offered a unique hands-on experience, changing their outlook on life. © 2016 IEEE.

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