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: Patent

Year of Publication Title

2019

M. Vinodini Ramesh, Pathinarupothi, R. Krishnan, 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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2019

M. Vinodini Ramesh, Pathinarupothi, R. Krishnan, Ekanath Srihari Rangan, Durga P, and Rangan, P. Venkat, “Systems, Methods, and Devices for Remote Health Monitoring and Measurement using Internet of Things Sensors”, U.S. Patent 16 / 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 Proceedings

Year of Publication Title

2018

R. Krishnan Pathinarupothi, Soublet, A., Rangan, E., 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”, HealthyIoT 2018 - 5th EAI International Conference on IoT Technologies for HealthCare. 2018.

Publication Type: Conference Paper

Year of Publication Title

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

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

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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