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Dive into the research topics where S. K. Mukhopadhyay is active.

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Featured researches published by S. K. Mukhopadhyay.


Computers & Electrical Engineering | 2011

A lossless ECG data compression technique using ASCII character encoding

S. K. Mukhopadhyay; Sucharita Mitra; Madhuchhanda Mitra

A software based lossless ECG compression algorithm is developed here. The algorithm is written in the C-platform. The algorithm has applied to various ECG data of all the 12 leads taken from PTB diagnostic ECG database (PTB-DB). Here, a difference array has been generated from the corresponding input ECG data and this is multiplied by a large number to convert the number of arrays into integers. Then those integers are grouped in both forward and reverse direction, out of which few are treated differently. Grouping has been done in such a way that every grouped number resides under valid ASCII value. Then all the grouped numbers along with sign bit and other necessary information are converted into their corresponding ASCII characters. The reconstruction algorithm has also been developed in using the reversed logic and it has been observed that data is reconstructed with almost negligible difference as compared with the original (PRD 0.023%).


Biomedical Signal Processing and Control | 2013

ECG signal compression using ASCII character encoding and transmission via SMS

S. K. Mukhopadhyay; Sucharita Mitra; Madhuchhanda Mitra

Abstract Software based efficient and reliable ECG data compression and transmission scheme is proposed here. The algorithm has been applied to various ECG data of all the 12 leads taken from PTB diagnostic ECG database (PTB-DB). First of all, R-peaks are detected by differentiation and squaring technique and QRS regions are located. To achieve a strict lossless compression in the QRS regions and a tolerable lossy compression in rest of the signal, two different compression algorithms have used. The whole compression scheme is such that the compressed file contains only ASCII characters. These characters are transmitted using internet based Short Message Service (SMS) and at the receiving end, original ECG signal is brought back using just the reverse logic of compression. It is observed that the proposed algorithm can reduce the file size significantly (compression ratio: 22.47) preserving ECG signal morphology.


2011 International Conference on Computer, Communication and Electrical Technology (ICCCET) | 2011

An ECG data compression method via R-Peak detection and ASCII Character Encoding

S. K. Mukhopadhyay; Madhuchhanda Mitra; Sucharita Mitra

Efficient and reliable electrocardiogram (ECG) compression system can increase the processing speed of real-time ECG transmission as well as reduce the amount of data storage in long-term ECG recording. In the present paper, a software based effective ECG data compression algorithm is proposed. The whole algorithm is written in C- platform. The algorithm is tested on various ECG data of all the 12 leads taken from PTB Diagnostic ECG Database (PTB-DB). In this compression methodology, all the R-Peaks are detected at first by differentiation technique and QRS regions are located. To achieve a strict lossless compression in QRS regions and a tolerable lossy compression in rest of the signal, two different compression algorithms have developed. In lossless compression method a difference array has been generated from the corresponding input ECG “Voltage” values and then those are multiplied by a considerably large integer number to convert them into integer. In the next step, theses integer numbers are grouped in both forward and reverse direction maintaining some logical criteria. Then all the grouped numbers along with sign bit and other necessary information (position of critical numbers, forward/reverse grouping etc.) are converted into their corresponding ASCII characters. Whereas in lossy area, first of all, the sampling frequency of the original ECG signal is reduced to one half and then, only the “Voltage” values are gathered from the corresponding input ECG data and those are amplified and grouped only in forward direction. Then all the grouped numbers along with sign bit and other necessary information are converted into their corresponding ASCII characters. It is observed that this proposed algorithm can reduce the file size significantly. The data reconstruction algorithm has also been developed using the reversed logic and it is seen that data is reconstructed preserving the significant ECG signal morphology.


Journal of Medical Engineering & Technology | 2012

ECG feature extraction using differentiation, Hilbert transform, variable threshold and slope reversal approach

S. K. Mukhopadhyay; Madhuchhanda Mitra; Sucharita Mitra

An accurate and reliable ECG feature extraction algorithm is presented in this paper. ECG samples are de-noised and its first derivative and Hilbert transform are computed. Sample having maximum amplitude in the transformed domain is found out and those samples having amplitudes within a lead wise specified threshold of that maximum are marked. In the original signal, where these marked samples undergo slope reversals are spotted as R-peak. On the left and right side of the R-peak, slope reversals are identified as Q and S peak, respectively. QRS onset-offset points, T and P waves are also detected. ECG baseline modulation correction is done after detecting characteristics points. The algorithm offers a good level of Sensitivity, Positive Predictivity and accuracy of R peak detection. Each wave and segment duration and each peak height is measured. Measurement errors of extracted ECG features are calculated. The algorithm is implemented on MATLAB 7.1 environment.


Journal of Medical Engineering & Technology | 2015

A combined application of lossless and lossy compression in ECG processing and transmission via GSM-based SMS

S. K. Mukhopadhyay; Sucharita Mitra; Madhuchhanda Mitra

Abstract This paper presents a software-based scheme for reliable and robust Electrocardiogram (ECG) data compression and its efficient transmission using Second Generation (2G) Global System for Mobile Communication (GSM) based Short Message Service (SMS). To achieve a firm lossless compression in high standard deviating QRS complex regions and an acceptable lossy compression in the rest of the signal, two different algorithms have been used. The combined compression module is such that it outputs only American Standard Code for Information Interchange (ASCII) characters and, hence, SMS service is found to be most suitable for transmitting the compressed signal. At the receiving end, the ECG signal is reconstructed using just the reverse algorithm. The module has been tested to all the 12 leads of different types of ECG signals (healthy and abnormal) collected from the PTB Diagnostic ECG Database. The compression algorithm achieves an average compression ratio of ∼22.51, without any major alteration of clinical morphology.


international conference on control instrumentation energy communication | 2014

Noise reduction and ECG feature extraction using interpolation and Hilbert transform

S. Dhar; S. K. Mukhopadhyay; Sucharita Mitra; M. M. Baig; Madhuchhanda Mitra

An effective and reliable noise reduction and Electrocardiogram (ECG) feature extraction algorithm is proposed in this paper. Contaminated ECG samples are de-noised using a Butterworth lowpass and IIR notch filter. First derivative using Lagrange Five Point Interpolation formula and Hilbert Transform of those ECG samples are computed. Sample having maximum amplitude is found out from the transformed data and those samples having amplitude within a lead wise specific threshold of that maximum are selected. The point where those selected samples undergo slope alteration in the original time domain ECG signal is marked as R peak. After successful identification of R peak points, base line modulation correction is implemented using an empirically determined formula. Q and S points are identified by finding minimum amplitude on the either side of the most recently detected R peak. QRS onset and offset points are also detected. After detecting all these characteristic points, Heart Rate, R, Q and S peak heights and QRS duration are measured. Errors in these extracted ECG features are also calculated. The algorithm offers a good level of Sensitivity (99.84%), Positive Predictivity (99.84%) and Detection Accuracy (99.84%) of R peak. Different types of ECG data of all the 12 leads taken from PTB diagnostic ECG database (PTB-DB) is used for testing the performance of the proposed module.


international conference on control instrumentation energy communication | 2014

Noise reduction and lossless ECG encoding

S. Dhar; S. K. Mukhopadhyay; Sucharita Mitra; M. M. Baig; Madhuchhanda Mitra

This paper proposes an efficient technique to eliminate high frequency and power line interference noise from digitized Electrocardiogram (ECG) signal and compression of that enhanced signal in a lossless manner. ECG data of different diseases taken from PTB diagnostic ECG database (PTB-DB) is used to test the performance of the module. At first, contaminated ECG signal is passed through a Butterworth lowpass filter to remove high frequency noises whose order is chosen on experimental basis. To remove power line interference noise, an IIR notch filter is designed properly. Enhanced signal is compressed using a strict lossless technique. The proposed module can reduce noise as well as ECG data file size efficiently.


2013 IEEE Point-of-Care Healthcare Technologies (PHT) | 2013

ECG signal processing: Lossless compression, transmission via GSM network and feature extraction using Hilbert transform

S. K. Mukhopadhyay; Madhuchhanda Mitra; S. Mitra

Software based new, efficient and reliable lossless ECG data compression, transmission and feature extraction scheme is proposed here. The compression and reconstruction algorithm is implemented on C-platform. The compression scheme is such that the compressed file contains only ASCII characters. These characters are transmitted using internet based Short Message Service (SMS) system and at the receiving end, original ECG signal is brought back using just the reverse logic of compression. Reconstructed ECG signal is de-noised and R peaks are detected using Lagrange Five Point Interpolation formula and Hilbert transform. ECG baseline modulation correction is done and Q, S, QRS onset-offset points are identified. The whole module has been applied to various ECG data of all the 12 leads taken from PTB diagnostic ECG database (PTB-DB). It is observed that the compression module gives a moderate to high compression ratio (CR=7.18), an excellent Quality Score (QS=312.17) and the difference between original and reconstructed ECG signal is negligible (PRD=0.023%). Also the feature extraction module offers a good level of Sensitivity and Positive Predictivity (99.91%) of R peak detection. Measurement errors in extracted ECG features are also calculated.


Measurement | 2012

An ECG signal compression technique using ASCII character encoding

S. K. Mukhopadhyay; Sucharita Mitra; Madhuchhanda Mitra


2011 International Conference on Communication and Industrial Application | 2011

Time plane ECG feature extraction using Hilbert transform, variable threshold and slope reversal approach

S. K. Mukhopadhyay; Madhuchhanda Mitra; Sucharita Mitra

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S. Dhar

University of Calcutta

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M. M. Baig

Auckland University of Technology

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