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

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 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: 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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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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