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PRIYANKA MAYAPUR

News Anchor,Writer,Researcher

An ECE Graduate from Agnel Institute of Technology and Design who believes in living the Passion with Determination. Enthusiastic about the field of Media, Radio, Communications and Writing. I am also a Research Oriented person and Love to get connected to ideas of Biology, Biomedical, Physics and...
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experience

News Anchor, Researcher, Writer  
Prime TV, All India Radio, March 2018 to Present, Panjim India
(Media and Research)
Prime Media Goa is a television channel providing news and interesting feature programmes entertaing and educating the viewers about issues on social concern. Simultaneously the channel also provides a platform to showcase the talent of young and budding enthusiastic people. All India Radio, officially known since 1956 as Ākāsha Vānī, is the national public radio broadcaster of India and is a division of Prasar Bharati. It was established in 1930. It is the sister service of Prasar Bharati's Doordarshan, an Indian television broadcaster.

education

AGNEL INSTITUTE OF TECHNOLOGY AND DESIGN  
Bachelor of Engineering, B.E., Jun, 2013 to Dec, 2013
Awarded as the Girl Topper and the Second Topper in the Branch of Electronic and Communications Engineering by Goa University & AITD in the presence of Hon'ble President, Shri Ram Nath Kovind

St. Xavier's Higher Secondary School  
Other, Science Graduate, Senior High, Jun, 2011 to Jun, 2013
Awarded the All Rounder Award at the hands of ExGovernor of Goa, Bharat Vir Wanchoo.

projects

ANALYSIS OF LEAD-II ECG FOR CLASSIFICATION AND DETECTION OF HEART DEFECTS  by  Priyanka Mayapur, 7 Eminent Cardiologists, Ex-NIT Guide, Co-Guide (Biomed)    

Goal: Heart Defects are immensely threatful to human beings. Improvements in diagnosis and treatment tools are welcome by the medical community; one of the most useful diagnostic tools for heart patients is the Electrocardiogram (ECG), which operates by measuring the electrical signals emitted by the heart through electrodes. An Electrocardiogram (ECG) is a test that records the electrical activity of the heart. Automatic ECG classification is an emerging tool for the cardiologists in medical diagnosis for effective treatment. Traditional technique of visual analysis of ECG is complicated for doctors, time consuming and requires expertise. Hence, Computer based classification of diseases can be immensely useful in diagnostics. This project has been inspired by the need to find an efficient method for ECG Signal Analysis which is simple and has good accuracy and less computation time. It deals with the study and analysis of ECG signal processing by means of MATLAB tool effectively for classification and detection of heart defects using Lead-II Configuration. Study of ECG signal includes reading and plotting of the ECG signal, acquisition of real time ECG data, ECG signal filtering and processing, feature extraction using Pan Tompkins Algorithm including R peak detection, decoding, detection of any abnormalities in ECG, comparison between normal and abnormal ECG signal and classifying along with calculating the heart rate, heart rate variability, amplitudes and intervals of the required features. This enormous amount of information can be stored in the memory for further correspondence. In this thesis/project, we first find out the characteristics to classify a Normal ECG and then pass any random signal to check whether the features or the values determined fall within the specified range with the ones characterized to be a normal ECG. If it does, then we classify it as Normal ECG else we classify it as an Abnormal ECG. The main goal of this project is to provide local clinics with a computer-aided electrocardiogram (ECG) diagnostic tool in an attempt to reduce time and work demands for busy physicians especially in areas where only one general medicine doctor is employed and a bulk of cases are to be diagnosed. Another advantage of such a design is that it can be expanded and can easily be connected to a recording device or a PC to collect and analyze the data for over a period of time. Most of these use frequency or time domain representation of ECG signals.

publications

Processing Different Format Waveforms Across Various Databases Using Matlab     
Published by (International Journal of Engineering Research in Electronics and Communication Engineering)
Authors: Priyanka Mayapur.  Published December 22, 2018

Signal Processing could be stated as a science that enhances our ability to communicate, understand and share the data, thus helping us extract this information for effective studies. Not being able to access a comprehensible format data could turn out to be the breaking point for a key factor or a principal research. This following paper proposes specific algorithms or rather techniques on how to convert such incomprehensive data into a comprehensive fact using four different formats with an accuracy of 99.99%. One such signal chosen for reference here is Electrocardiogram (ECG) and the plotting and processing have been implemented using MATLAB. We have specifically chosen Lead-II ECG Configuration for our research work. This technique is applicable for any real-time general waveform that exists in these specific formats as discussed in the paper.

Classification of Arrhythmia from ECG Signals using MATLAB     
Published by (International Journal of Engineering and Management Research)
Authors: Priyanka Mayapur.  Published December 23, 2018

An Electrocardiogram (ECG) is defined as a test that is performed on the heart to detect any abnormalities in the cardiac cycle. Automatic classification of ECG has evolved as an emerging tool in medical diagnosis for effective treatments. The work proposed in this paper has been implemented using MATLAB. In this paper, we have proposed an efficient method to classify the ECG into normal and abnormal as well as classify the various abnormalities. To brief it, after the collection and filtering the ECG signal, morphological and dynamic features from the signal were obtained which was followed by two step classification method based on the traits and characteristic evaluation. ECG signals in this work are collected from MIT-BIH, AHA, ESC, UCI databases. In addition to this, this paper also provides a comparative study of various methods proposed via different techniques. The proposed technique used helped us process, analyze and classify the ECG signals with an accuracy of 97% and with good convenience.

Detection and Classification of Heart Defects     
Published by (International Journal of Science and Healthcare Research)
Authors: Priyanka Mayapur .  Published November 28, 2018

Heart Defects are immensely threatful to human beings and can cause death. Improvements in diagnosis and treatment tools are welcome by the medical community and have proven to be one of the most useful diagnostic tools for heart patients, one of it to be mentioned would be the Electrocardiogram. Traditional technique of visual analysis of ECG is complicated for doctors, time consuming and requires expertise. Hence, computer based classification and detection of diseases can be immensely useful in diagnostics. This project has been inspired by the need to find an efficient method for ECG signal analysis and classification which is simple yet has good accuracy and less computation time. It deals with the study and analysis of ECG signal processing by means of MATLAB tool effectively for classification and detection of heart defects using Lead-II Configuration. Study of ECG signal includes reading and plotting of the ECG signal, acquisition of real time ECG data, ECG signal filtering and processing, feature extraction and detection of certain parameters, decoding, comparison, classification of the required features. In this thesis, we first find out the characteristics to classify a normal ECG and then pass any random signal to check whether the features or the values determined fall within the specified range with the ones characterized to be a normal ECG. If it does, then we classify it as normal ECG else we classify it as an abnormal ECG using Lead-II Configuration.

Feature Extraction of the T Peak and Its Analysis     
Published by (International Journal of Innovative Research in Computer and Communication Engineering)
Authors: Priyanka Mayapur.  Published November 29, 2018

An Electrocardiogram (ECG) is a graphical representation of the electrical signals generated during the heart activity. Analysis of ECG by identifying the various features and traits could help us detect the various cardiac peculiarities, thus providing valuable information about the activity of the human heart. Automatic classification of ECG has evolved as an emerging tool in medical diagnosis for effective treatments. A basic algorithm has been defined in this paper in order to detect the T peaks and its related features. Also, the work proposed in this paper reviews and summarizes the various techniques used by researchers in order to detect and delineate T waves (peaks). ECG signals in this work are collected from MIT-BIH database and it has been implemented using MATLAB routine. Lead-II ECG signals were used in processing of data.

A Survey on the Cardiology Ontology and Its Analytical Procedures     
Published by (International Journal of Advanced Research in Electrical,Electronics and Instrumentation Engineering)
Authors: Priyanka Mayapur.  Published December 26, 2018

Heart is an organ whose electrical activity is responsible in generating a trace that contains important information about the heart. This general article focuses on the various details and basics related of the heart and the techniques used to analyse an ECG.

Extraction and Analysis of the P Peak Morphology     
Published by (International Journal of Advanced Research in Electrical,Electronics and Instrumentation Engineering)
Authors: Priyanka Mayapur.  Published November 27, 2018

An Electrocardiogram (ECG) could be defined as a continuous recording of electrical signals of the heart against time. Analysis of ECG by identifying the various features and traits could help us detect the various cardiac peculiarities. Automatic classification of ECG has evolved as an emerging tool in medical diagnosis for effective treatments. The work proposed in this paper has been implemented using MATLAB that presents an algorithm to detect the various features and the possible abnormalities it could represent. ECG signals in this work are collected across various databases. The processing of the data was done on the Lead-II ECG signals. In addition to that, this paper also provides a comparative study of various methods proposed by researchers used to detect and evaluate P peaks thus helping us obtain the results accurately, thus enabling precise calculations of the waveforms.

A Literature Survey on Heart Rate Variability and its Various Processing and Analyzing Techniques     
Published by (International Research Journal of Engineering Technology)
Authors: Priyanka Mayapur.  Published December 29, 2018

Human heart being the electro-mechanical pump supplies blood via a cardiovascular network. Its rhythmic beating gives rise to a pattern which when recorded can be used to find out the functionality of a heart. The diagnostic tool is called as Electrocardiogram (ECG) and its tracing contains a lot of attributes whose proper analysis may detect any cardiac peculiarity. Among them, is an entity called as the beat-to-beat interval (R-R interval). The analysis of beat to beat fluctuations of heart rate is known as heart rate variability (HRV) which is a concise marker to study the health of the heart along with a lot of measures clinically. This paper talks about the importance of the HRV and the various processing yet analysis techniques used to calculate the HRV by researchers.

The QRS Complex Detection Approach     
Published by (International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences)
Authors: Priyanka Mayapur.  Published November 30, 2018

A graphical representation of the electrical signals generated during the heart activity could be termed as Electrocardiogram (ECG). Analysis of ECG by identifying the various features and traits could help us detect the normal and pathological physiology of the heart, thus providing valuable information about the activity of the human heart. Automatic classification of ECG has evolved as an emerging tool in medical diagnosis for effective treatments. In this paper, a real time algorithm for detection of QRS Complex and its duration has been developed. Also, the work proposed in this paper reviews and summarizes the various techniques used by researchers in order to detect and delineate QRS Complex. ECG signals in this work are collected from MIT-BIH database and it has been implemented using MATLAB routine consisting of four different databases formats. The processing of the data was done on the Lead-II ECG signals.

Detection and Processing of the R Peak     
Published by (InternationalJournalofInnovativeResearchinElectrical,Electronics,Instrumentation&ControlEngineering)
Authors: Priyanka Mayapur.  Published November 29, 2018

Electrocardiogram (ECG) is a commonly recorded bio-signal that captures the electrical activity of the heart. Identifying various features and traits could help us detect the normal and pathological physiology of the heart, thus providing valuable information about the activity of the human heart which is a very important step in ECG signal analysis. Computer processing of ECG has evolved as an emerging tool in medical diagnosis for effective treatments. The work proposed in this paper deals with the detection of the R peak and its feature evaluation and reviews and summarizes the various techniques used by researchers in order to detect the same. ECG signals in this work are collected from several places and it has been implemented using MATLAB routine consisting of four different databases formats. The processing of the data was done on the Lead-II ECG signals.

A Review on Detection and Performance Analysis on R-R Interval Methods for ECG     
Published by (The International Journal of Innovative Research in Science, Engineering and Technology)
Authors: Priyanka Mayapur.  Published November 25, 2018

Electrocardiogram (ECG)is a signal that records the electrical performance of the heart. A reliable, real time analysis of ECG and its considerate features is a necessary prerequisite for monitoring R-R Interval, hence HRV and cardiovascular control. The work proposed in this paper reviews the various methods used to detect the R-R interval and its related features that summarizes the various techniques used by researchers and also provides generic advantages about the various methods used. The processing of the data was done on the Lead-II ECG signals and on a tool called as Matlab.

Awards

Cherishing and Learning!