Mithun Manjnath Nayak

Remote health monitoring system for detecting cardiac disorders.

Remote health monitoring system with Clinical Decision Support System as a key component could potentially quicken the response of medical specialists to critical health emergencies experienced by their patients. A monitoring system, specifically designed for cardiac care with ECG signal analysis as the core diagnostic technique, could play a vital role in early detection of a wide range of cardiac ailments, from a simple arrhythmia to life threatening conditions such as Myocardial Infarction. The system, that we have developed consists of three major components viz., (a) Mobile Gateway, deployed on patients mobile device, that receives 12-Lead ECG signals from any ECG sensor (b) remote server component that hosts algorithms for accurate annotation and analysis of the ECG signal and (c) Point of Care Device of the doctor to receive a diagnostic report from the server based on the analysis of ECG signals. In the present work our focus has been towards developing a system capable of detecting critical cardiac events well in advance using an advanced remote monitoring system. A system of this kind is expected to have applications ranging from tracking wellness/fitness to detection of symptoms leading to fatal cardiac events.

Continuous monitoring of stress on smartphone using heart rate variability

Continuous monitoring of an individuals stress levels is essential to manage stress and mental state in an effective way. With increasing ubiquity of wearable heart rate monitors and their unobtrusiveness, HRV (Heart rate variability) derived from heart rate signals has emerged as one of the most relevant parameters for continuous monitoring of stress. In the present work, we have made an attempt to address the challenges about distinguishing between stressed and non-stressed state of a person based on just one minute of IBI (Inter Beat Interval) records with good accuracy. Such ultra-short term analysis of HRV is particularly advantageous towards capturing very short term fluctuations in mental stress levels and enhanced scope for frequent monitoring. We have analyzed various time domain, frequency domain and nonlinear HRV features to narrow down to a most influential set of features for accurate classification between stressed and non-stressed state. We have identified RMSSD (root mean square of successive differences) of IBI series to be the most direct indicator of stressed state. We also provide a continuous stress score which, when used in continuous monitoring scenario, provides the user with adequate details about his/her stress levels. This helps the user to understand stress patterns across a day in a better way and to take appropriate measures to manage stressful situations. We have developed and deployed a system, based on above concept, on smartphone as an android application for real-time stress monitoring.

Remote health monitoring system for detecting cardiac disorders

Remote health monitoring system with Clinical Decision Support System as a key component could potentially quicken the response of medical specialists to critical health emergencies experienced by their patients. A monitoring system, specifically designed for cardiac care with ECG signal analysis as the core diagnostic technique, could play a vital role in early detection of a wide range of cardiac ailments, from a simple arrhythmia to life threatening conditions such as Myocardial Infarction. The system, that we have developed consists of three major components viz., (a) Mobile Gateway, deployed on patients mobile device, that receives 12-Lead ECG signals from any ECG sensor (b) remote server component that hosts algorithms for accurate annotation and analysis of the ECG signal and (c) Point of Care Device of the doctor to receive a diagnostic report from the server based on the analysis of ECG signals. In the present work our focus has been towards developing a system capable of detecting critical cardiac events well in advance using an advanced remote monitoring system. A system of this kind is expected to have applications ranging from tracking wellness/fitness to detection of symptoms leading to fatal cardiac events.

Detecting critical cardiac events with 12 lead ECG data

Detection of cardiac abnormalities through annotation and modeling based on 3-Lead ECG data has been reported in literature very extensively. However, we realize that analysis based on 12-Lead, dimension resolved, ECG data is vital for accurate detection of critical cardiac events such as Myocardial Infarction (MI), Ischemia, Bundle Branch Blocks, Pericarditis etc. and understanding of underlying conditions leading to it. In this work we present an approach to first annotate 12-Lead ECG data and further analyze them with the objective of classifying them broadly into normal or abnormal (MI) conditions. The algorithm that we have developed enables the representation of all 12-Lead ECG data in form of a critical feature set which are then subjected to clinically established rule set to detect cardiac abnormality. With a preliminary implementation of this methodology, the classification results seem encouraging leaving immense scope for further enhancements towards developing a robust Cardiac Decision Support System.