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 by him 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 College University Year
M.Tech. National Institute of Technology Calicut University 2007
PGDMIT Coimbatore Institute of Technology Bharathiar University 2002
B.E. Mepco Schlenk Engg college Madurai Kamaraj 2001


  • Digital Systems
  • Signals and System
  • Digital Signal processing
  • Wavelets and its Application

Research Expertise

Ongoing Research:

  • PhD in the area of Biomedical Signal Processing

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


Publication Type: Conference Paper
Year of Publication Publication Type Title
2016 Conference Paper 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 »»
2014 Conference Paper A. Balachandran, Ganesan, M., and Sumesh, E. P., “Daubechies algorithm for highly accurate ECG feature extraction”, in International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), 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. More »»
2010 Conference Paper S. Ennesai, Narayanankutty, K. A., and Ganesan, M., “Temporal Alignment of Non-gated image sequences for 4D Cardiac imaging Using Wavelets”, in 2010 International Conference on Computer and Communication Technology, ICCCT-2010, Allahabad, 2010, pp. 198-200.[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 (PSNR). ©2010 IEEE.

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2007 Conference Paper M. Ganesan, Sathidevi, P. S., and Indiradevi, K. P., “A Novel Approach for the Analysis of Epileptic Spikes in EEG”, in International Conference onConference on Computational Intelligence and Multimedia Applications, 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. More »»
Publication Type: Journal Article
Year of Publication Publication Type Title
2015 Journal Article 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 Journal Article 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: Conference Proceedings
Year of Publication Publication Type Title
2014 Conference Proceedings 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.
2014 Conference Proceedings 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.
2009 Conference Proceedings 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.
Publication Type: Book Chapter
Year of Publication Publication Type Title
2014 Book Chapter 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 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 »»
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