Qualification: 
M.Tech
m_ganesan1@cb.amrita.edu
Phone: 
+91 422 2685000 Ext. 5727

Ganesan M. currently serves as Assistant Professor at the department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore. His areas of research include Signal Processing. Presently, he is the Academic Coordinator of the ECE department. His areas of interest include Biomedical Signal Processing and Hardware Implementation of Signal Processing Algorithms. He is a member in IETE. Apart from teaching, few other roles played at Amrita includes: class counselor, class advisor, coordinator-Amritavarsham, volunteering in Amalabharatham, member of various committees during Institution day, convocation, Amrithotsavam, Gokulashtami celebrations, etc.

Ganesan got selected for a scholarship in Research Scholar category within the Erasmus Mundus External Cooperation Window, India4EU project at TKK, Helsinki University of Technology, Helsinki, Finland on the subject, "Signal Processing, Acoustics" The duration of the scholarship was 18 months from August 2010 to January 2012.

Qualification

  • 2007: M. Tech. in Communication Engineering
    Calicut University, National Institute of Technology
  • 2002: PGDMIT in Bio-Medical Instrumentation
    Bharathiyar University, Coimbatore Institute of Technology
  • 2001: B. E. in Electronics and Communication
    Madurai Kamaraj University, MepcoSchlenk Engg College

Professional Experience

Year Affiliation
July 2009 - Present Assistant Professor (Senior Grade) , Amrita Vishwa Vidyapeetham
Domain : Teaching, Research and Projects Dept Administration
June 5, 2007 – June 30, 2009 Assistant Professor, Amrita Vishwa Vidyapeetham
Domain : Teaching, Research and Projects
August 1, 2003 - May 31, 2005 Lecturer, SACS MAVMM Engg college, Madurai under Anna University
Domain : Teaching
August 1, 2002 – July 31, 2003 Lecturer, Sethaiammal Engg college, Madurai, under Anna University
Domain : Teaching

Academic Responsibilities

SNo Position Class / Batch Responsibility
1. Class Advisor 2018 - 22 Counseling, Bridging the gap between students and staff
2. Lab Coordinator 2016 onwards Purchase and Overall Coordination
3. Academic Coordinator 2012 -2017 Solving Academic related issues
4. Class Advisor (2 years 2008 - 2010) 2008 - 2012 Counseling, Bridging the gap between students and staff
5. NAAC Incharge for Teaching Learning and Evaluation Procedure 2008 Formulate methodologies and Maintain Records
6. Class Counselor (1 year - 2007) 2007 - 2011 Counseling, Bridging the gap between students and staff

Undergraduate Courses Handled

  1. Digital Signal Processing
  2. Signals and system
  3. Electronic Circuits
  4. Digital systems
  5. Telecommunication Management
  6. Principles of Management

Post-Graduate Courses Handled

  1. Wavelets and Application
  2. Bio-Medical Instrumentation
  3. Prototyping of BioMedicalequipments

Participation in Faculty Development / STTP / Workshops /Conferences

SNo Title Organization Period Outcome
1. workshop on “ The Art of Technical Writing and Professional Ethics” Amrita, Coimbatore October 16, 2014 Paper writing skills
2. Seminar on “ Emerging perspectives in Nanoelectronics R&D” Amrita, Coimbatore September 19, 2014 Knowledge on Nanoelectronics application
3. seminar on “ EngineeringFailure Analysis” Amrita, Coimbatore April 26, 2014 Understanding basic concepts
4. National Workshop on, “Signal and Image processing Application using Xilinx System Generator” Amrita, Coimbatore April 10 - 11, 2014 Application of Xilinx system generator

Organizing Faculty Development / STTP / Workshops /Conferences

SNo Title Organization Period Outcome
1. International Workshop on Rural healthcare in India Amrita VishwaVidyapeetham May 18 - 19, 2018 Ways to improve Rural healthcare in India
2. International Workshop on Creative Thinking and User Centered Design Amrita VishwaVidyapeetham May 15 - 16, 2018 Improvement in Creative thinking skills
3. Role of Universities in Empowering Indian Villages Amrita VishwaVidyapeetham September 21 - 23, 2016 Knowledge on empowerment of villages.
1. FDP o Fundaments of Digital System   July 11 - 12, 2016 Basic knowledge on of Digital
2. Workshop on Advanced Electronic Circuit Analysis Amrita VishwaVidyapeetham July 7 - 9, 2016 Application of Electronics
3. FDP on Fundamentals of Electronics Amrita VishwaVidyapeetham June 7 - 10, 2016 Basic knowledge on of electronics
4. National Seminar on Techniques and Applications of Hyperspectral Image Analysis Amrita VishwaVidyapeetham April 19 - 20, 2016 Application of hyperspectral imaging
5. Workshop on Xilinx VIVADO system generator and Analog Discovery kit Amrita VishwaVidyapeetham September 21 - 22, 2015 Application of Xilinx board
6. National Workshop on Embedded Design Flow using Xilinx ZYNQ SoC Amrita VishwaVidyapeetham February 27 - 28, 2015 Usage of Xilinx board

Academic Research – PG Projects

SNo Name of the Scholar Programme Specialization Duration Status
1. Rajkumar BME Bio signal processing 2018-19 Ongoing
2. AbijithUnni BME Bio signal processing 2017-18 Completed
3. Swetha V CSP Image Processing 2016-17 Completed
4. Haritha C CSP Bio signal processing 2015 -16 Completed
5. Vasisakh VLSI Electrical Fault analysis 2014 - 15 Completed
6. Swathi, O. N BME Bio signal processing   Completed
7. AthiraBalachandran VLSI Wavelet Design 2013 - 14 Completed
8. Ajin R Nair VLSI Biosignal Processing 2012 -13 Completed
9. Aryalekshmi BME Bio Signal Processing 2012 -13 Completed
10. Ennesai BME Image Processing 2009 -10 Completed

Projects Guided:

  • Support Vector Machine Based Classification Of ECG Features -  2014 by Athira
  • Ecg Arrhythmia Classification by Using Wavelet and Neural Networks – 2013 by   Aryalekshmi R.  
  • Classification of ECG based on HRV features using SVM classifier – 2012 by Ajin R.Nair
  • Temporal alignment of non-gated image sequences for 4D cardiac imaging using wavelets – 2010 by Ennesai
  • ECG monitoring of Cardiac Patient using Embedded system – 2014
  • Cardiac Output Measurement using Ballistocardiography   - 2013
  • Wireless ElectroCardiogram Monitoring for Cardiac patient on andriod Platform -2013
  • Gujarat University, Ahmedabad, India

Publications

Publication Type: Conference Proceedings

Year of Publication Title

2018

M. Ganesan, Dr. Lavanya R., and Sumesh, E. P., “A Survey on Ballistocardiogram to study the Mechanical Activity of Heart”, Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing, ICCSP 2017. Institute of Electrical and Electronics Engineers Inc., pp. 0557-0561, 2018.[Abstract]


Ballistocardiogram (BCG) refers to mechanical activity of the heart. It is a non-invasive technique that provides information on cardiovascular forces. Few studies have demonstrated the correlation that BCG exhibits with the cardiac output and pathological condition of the heart. However there is no clear proof of the relationship between cardiac cycles and BCG wave characteristics. Different BCG acquisition techniques exist, with differing sensor positions that include arm, feet and spine. To use BCG for clinical applications, the differences in the parameter of signals using different methods have to be reduced and a global template as that of Electrocardiogram (ECG) has to be obtained. So there is a need to understand the way BCG is captured and the nature of the signal. In this paper we have focused on the survey of various techniques for acquiring BCG, denoising methods and classification techniques. Recent developments in BCG are discussed along with comparative studies. The purpose of this paper is to investigate the feasibility of BCG in the field of medical diagnosis.

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2017

Dr. Lavanya R., Swathi, O. N., and Ganesan, M., “Peak Detection and Feature Extraction for the Diagnosis of Heart Diseases”, Proceedings in IEEE International Conference in Computing, Communications and Informatics. 2017.[Abstract]


Patient monitors with arrhythmia detection will enhance the quality of living of human by aiding in prediction of diseases in much early stage. In this work we have developed an algorithm for classifying the ECG signals into normal and arrhythmic signal. Here we have detected the R peaks from denoised ECG signal with an accuracy of 97.56%. Extracted features from the signal in both time and frequency domain and the signals are classified into normal and abnormal signals using support vector algorithm. The accuracy of the algorithm is tested by applying on MIT-BIH arrhythmia database and we obtained an overall 80% classifier accuracy.

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2017

O. N. Swathi, Ganesan, M., and Lavanya, R., “R Peak Detection and Feature Extraction for the Diagnosis of Heart Diseases”, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). pp. 2388-2391, 2017.[Abstract]


Patient monitors with arrhythmia detection will enhance the quality of living of human by aiding in prediction of diseases in much early stage. In this work we have developed an algorithm for classifying the ECG signals into normal and arrhythmic signal. Here we have detected the R peaks from denoised ECG signal with an accuracy of 97.56%. Extracted features from the signal in both time and frequency domain and the signals are classified into normal and abnormal signals using support vector algorithm. The accuracy of the algorithm is tested by applying on MIT-BIH arrhythmia database and we obtained an overall 80% classifier accuracy.

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2017

S. Aparna, S. Yashwanth, B., Nair, P. V., Ganesan, M., and Akshay, K., “Design of Wi-Fi based ECG system”, In Signal Processing and Communication (ICSPC), 2017 International Conference . pp. 7-11, 2017.

2017

I. Srilikhitha, Saikumar, M. M., Rajan, N., Neha, M. L., and Ganesan, M., “Automatic Irrigation System using Soil Moisture Sensor and Temperature Sensor with Microcontroller AT89S52”, 2017 International Conference on Signal Processing and Communication (ICSPC). pp. 186-190, 2017.[Abstract]


Food production techniques have to be improved because of rapid demand in food. Since India has agriculture as the main source of production, proper irrigation schemes are to be employed for an efficient outcome. This design automates the irrigation process thereby reducing the manual intervention and the water losses. It is more helpful in the places where water scarcity is seen more. It consists of 2 sensors which takes the values of temperature of surroundings and moisture level of soil. Output of these sensors are given to ADC and then to microcontroller. Microcontroller compares the values with the threshold values and drives the relay which controls the motor. LCD display is used to display data in the field. GSM modem is also a part of design which helps in transmitting SMS to farmer's phone number which contains the status of motor in field. The design is cost effective and also affordable.

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2017

S. Vinayan, Ganesan, M., Joy, N., Augustin, M. J., and Gupta, N., “Gamma Corrected Three-step Fringe Profilometry for the Detection of Surface Defects on Aircraft Surfaces”, Proceedings of IEEE International Conference on Signal Processing and Communication, ICSPC 2017, vol. 2018-January. Institute of Electrical and Electronics Engineers Inc., pp. 221-226, 2017.[Abstract]


Aircraft industry relies very much on visual inspection for routine inspection of aircrafts. A skilled inspector goes around the aircraft and checks the surface for damages such as dents, cracks and corrosion. This is however, a laborious task and sometimes maybe ineffective. This raises the need for a system to automate the process in order to make it faster, efficient and reliable. A probe going around the aircraft capturing its images and transferring them to a PC which performs image processing algorithms may be an alternative solution to the current visual inspection method. 2D image processing can also aid in detecting damages, but it also detects surface features such as writings, markings etc as damages which makes it difficult to identify the damage. 3D reconstruction of the surface can give clear cut idea about the surface and it can also help measurement. Fringe projection profilometry is one of the widely used methods to reconstruct surfaces, models, etc where fringe patterns are projected on the surface to be reconstructed and phase information is retrieved from the captured fringe images in order to reconstruct the surface. This necessitates the response of the projector to be linear so that the grayscale variations in the profile are projected properly. However, commercially available projectors are gamma distorted which needs to be corrected first. This paper describes studies carried out to study the effect of gamma correction in the surface reconstruction and the validation of a look up table (LUT) based gamma correction method. As the aircraft surfaces can be of different types, it is required to validate the methodology on different surfaces, with LUT made by capturing projected patterns on different surfaces. The results indicate that the method is independent of the surface used and the projector brightness levels.

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2016

C. Haritha, Ganesan, M., and Sumesh, E. P., “A Survey on Modern Trends in ECG Noise Removal Techniques”, Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2016. Institute of Electrical and Electronics Engineers Inc., 2016.[Abstract]


Unwanted signal contents always degrades the quality of ECG signal. Since ECG is a non-stationary signal, noise removal always is a complicated task. ECG signal is a raw material for diagnosis and analysis of almost all heart diseases and hence demands a good quality. A survey of various types of various denoising approaches emerged over recent years has been presented in this paper. FIR and IIR filtering, low and high frequency noise removal techniques, Quadrature filtering, Adaptive noise cancellation techniques, Non-Local Means denoising techniques (NLM), Empirical Mode Decomposition(EMD), Variational Mode Decomposition (VMD), Wavelet transform denoising methods and recent developments are discussed along with comparatives studies.

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2014

S. Bipin Palakollu, Manoj, P., Teja, S., Kumar, S., and Ganesan, M., “Ecg Monitoring Of A Cardiac Patient Using Embedded System”, Proceedings of IRF International Conference . Chennai, India, 2014.

2014

A. Balachandran, Ganesan, M., and Sumesh, E. P., “Daubechies Algorithm for Highly Accurate ECG Feature Extraction”, 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE). Dr.NGP Institute of Technology, IEEE Xplore, 2014.[Abstract]


An ECG is a sensitive diagnostic tool used to detect various cardio-vascular diseases by measuring and recording the electrical activity of the heart. The efficiency and speed of the feature extraction scheme has a major role in the ECG diagnostic system. The proposed work tries to develop an ECG feature extraction system based on the multi-resolution wavelet transform. This system tries to improve the performance of ECG analysis system by extracting highly accurate ECG features. The Daubechies wavelet filter is used here for extracting ECG features. MIT-BIH Arrhythmia database is used in this work to obtain the digitized ECG input signal.

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2014

M. Ganesan and Sumesh, E. P., “Classification of ECG Arrthymia using Daubecius Wavelet and Neural Network”, International Conference on Signal and Speech Proceesing,TKM college Kollam. 2014.

2010

S. Ennesai, Narayanankutty, K. A., and Ganesan, M., “Temporal Alignment of Non-gated image sequences for 4D Cardiac imaging Using Wavelets”, 2010 International Conference on Computer and Communication Technology, ICCCT-2010. Allahabad, pp. 198-200, 2010.[Abstract]


Non-gated images (in the absence of external trigger) cannot reconstruct 3D volumes directly due to mismatch of acquisition intervals. Such a type of image needs synchronization for making direct reconstruction into 3D volume from 2D data set. Since, Spatio-temporal resolution is more important when dealing with reconstruction methods and noise reduction techniques, often wavelets are used. To reduce motion artifacts, wavelet denoising is performed. Wavelet correlation is performed on image sequences for synchronization. After correlating all image sequences, reconstruction will be performed to get 4D imaging. Comparison is made for different synchronization methods with and without wavelets by finding their root mean square error and peak signal-to-noise ratio

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2009

M. Ganesan, N. Kumar, M., and M. Nirmala Devi, “A Neuro-Hardware for Epilepsy classification using Modified Genetic Algorithm”, International conference on Electronic Design and Signal Processing ICEDSP09. MIT, Manipal , 2009.

2007

M. Ganesan, Sathidevi, P. S., and Indiradevi, K. P., “A Novel Approach for the Analysis of Epileptic Spikes in EEG”, International Conference onConference on Computational Intelligence and Multimedia Applications. pp. 297-31, 2007.[Abstract]


Various problems associated with epilepsy detection is that the epileptic spike essentially change from one patient to the other and we are in need of trained professional to classify normal brain activity, where the non-pathological events that resemble pathological ones. The aim of this work is the automatic detection of Epileptic and non-Epileptic spike in EEG which plays a vital role in the determination of epilepsy. The present study proposes a system that integrates wavelet transform, Feature extraction and Artificial Neural Network for the detection and classification of Epilepsy. The system was evaluated on testing data from 25 patients of which 86.0% of the epileptic spikes and 80% of the non-epileptic spikes were detected correctly. This system has good performance in detecting epileptic activities and it is found that the Wavelet based Artificial Neural Network approach is an appropriate way of detecting the epileptic spikes in EEG.

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Publication Type: Conference Paper

Year of Publication Title

2016

M. Ganesan and Sumesh, E. Pb, “Evaluating the force of contraction of heart using ballistocardiogram”, in 2016 3rd MEC International Conference on Big Data and Smart City, ICBDSC 2016, 2016, pp. 225-229.[Abstract]


Ballistocardiogram (BCG) a promising technique that records the vibrational movement of the body related to heartbeat of a person. In this paper, a electromechanical film (Emfi) sensor and Electrocardiogram (ECG) set up was used to measure the force of contraction by which the health condition of a person was indexed. The experimental setup was carried out with a base Emfi sensor and a standard Lead I ECG electrode system on a flat chair. Wavelet denoising was performed for both ECG and BCG to evaluate the QRS complex of ECG relevant to IJK complex of BCG. Force of contraction is the amount of blood that heart can pump. IJ peak of BCG corresponds to force of contraction. Based on physical activity, 20 subjects were selected for experimental setup, 10 normal adult subjects and 10 athlets. From the BCG readings, the IJ amplitude level, which is related to the average force of contraction, was calculated as 3.98mV for normal subject and 6.21mV for athletic subject. © 2016 IEEE. More »»

Publication Type: Journal Article

Year of Publication Title

2015

M. Ganesan and Sumesh, E. P., “Analysis of Ballistocardiogram with Multiwavelets in Evaluation of Cardiac Fitness”, Journal of Theoretical and Applied Information Technology , vol. 77, 2015.[Abstract]


This paper aims at providing efficient and accurate analysis of the cardiac activity of patient acquired from Electrocardiogram and Ballistocardiogram (BCG) signals using discrete multiwavelets de-noising analysis. The functioning of heart is studied from the Electrocardiogram (ECG), a noninvasive technique recorded by using skin electrodes and from BCG, an electrode-less technique that calculates the fitness of the heart. The setup of BCG and ECG was successfully made. The corresponding signals acquired, to calculate the cardiac output with respect to the parameters like R peak of ECG, J peak of BCG and R-J interval. Efficient analysis of ECG and BCG were done after removing the baseline drift and power line interference. Coiflet, Daubechies wavelets and multiwavelets were used to study the performance for better denoising and pre- processing to extract the features. Multiwavelet was found to be a better choice for finding the optimum performance of heart activity. 20 subjects (Normal life style adults and athletes) recordings have been taken for study and analysis on heart rate and cardiac output were made for each subject. The analyses were made 2 times a day (Morning and Night) to determine the index in evaluating the fitness of the heart. Cardiac output of athletes was found to be better than normal subjects. More »»

2010

M. Ganesan, .E.P, S., and .R, V., “Multi-Stage, Multi-Resolution Method for Automatic Characterization of Epileptic Spikes in EEG”, International Journal of Signal Processing, Image Processing and Pattern Recognition , vol. 3, no. 2, 2010.[Abstract]


In this paper, a technique is proposed for the automatic detection of the spikes in long term 18 channel human electroencephalograms (EEG) with less number of data set. The scheme for detecting epileptic and non epileptic spikes in EEG is based on a multi resolution, multi-level analysis and Artificial Neural Network(ANN) approach. Wavelet Transform (WT) is a powerful tool for signal compression, recognition, restoration and multi-resolution analysis of non-stationary signal. The signal on each EEG channel is decomposed into six sub bands using a non-decimated WT. Each sub band is analyzed by using a non-linear energy operator, in order to detect spikes. A parameter extraction stage extracts the parameters of the detected spikes that can be given as the input to ANN classifier. A robust system that combines multiple signal-processing methods in a multistage scheme, integrating wavelet transform and artificial neural network is proposed here. This system is experimented on a simulated EEG pattern waveform as well as with real patient data. The system is evaluated on testing data from 81 patients, totaling more than 800 hours of recordings. 90.0% of the epileptic events were correctly detected and the detection rate of non epileptic events was 98.0%. We conclude that the proposed system has good performance in detecting epileptic form activities; further the multistage multiresolution approach is an appropriate way of automatic classification problems in EEG. More »»

Publication Type: Book Chapter

Year of Publication Title

2014

B. Kurumaddali, Ganesan, M., S. Venkatesh, M., Suresh, R., Syam, B. S., and Suresh, V., “Cardiac Output Measurement Using Ballistocardiogram”, in The 15th International Conference on Biomedical Engineering: ICBME 2013, 4th to 7th December 2013, Singapore, vol. 43, J. Goh, Ed. Cham: Springer International Publishing, 2014, pp. 861–864.[Abstract]


Ballistocardiogram (BCG) is a non-invasive technique to measure cardiac parameters. It was popularized by Dr. Isaac Staar in 1940. The Ballistocardiogram signal is generated due to the vibrational activity of the heart. BCG was considered as a promising technique but was replaced by Electrocardiogram due to the difficulty involved in detecting and analysing the BCG waveforms. With the increase in processing power and better signal processing techniques over the last few decades, BCG has regained its prominence and is being considered to be used as a continuous patient monitoring system. The usability of BCG was limited in the earlier days due to the large size of the equipment and the lack of signal processing systems to analyse this complicated signal. Cardiac output is defined as the amount of blood pumped out by the heart in a minute. This parameter can be utilized to determine the state of the heart. One method to determine the cardiac output from BCG waveform has been discussed in section II of this paper. The sensor used for our experiment is a lightweight and flexible sheet type electromechanical film which is placed on the seat of the chair. The setup used has a two-stage amplifier which is connected to a data acquisition card which is in turn connected to a laptop. The signal processing is done using NI’s software LabView. The BCG setup was made and the signal was successfully validated with ECG. The R-J interval, which is the interval between the R peak of the ECG signal and J peak of the BCG signal, was determined. Echocardiogram, another cardiac measurement instrument, was kept as a standard basis for determining the cardiac output from the BCG signal. Recording of 14 different subjects have been taken and the cardiac output has been determined for each case. More »»
Faculty Research Interest: