Rahul Kher | Gujarat Technological University (original) (raw)

Papers by Rahul Kher

Digital Watermarking embeds identifying information in an image, which is not always hidden, in s... more Digital Watermarking embeds identifying information in an image, which is not always hidden, in such a manner that it cannot easily be removed. This is used to identify the owner of the work, to authenticate the content, to trace illegal copies of the work. So many digital image watermarking techniques have been implemented to stop the illegal used of digital content. This correlation based watermarking techniques use for generation of visible watermarked image. In this paper, check robustness of correlation based detection watermarking schemes using White Gaussian Noise (WGN) against different Order Statistics Filters Attack in Spatial Domain. The robustness of the Watermarked images has been verified on the parameters of PSNR (Peak Signal to Noise Ratio), NCC (Normalized Cross Correlation) and NAE (Normalized Absolute Error).

The Open Biomedical Engineering Journal, 2021

IoT Applications for Healthcare Systems

In this era of stress-filled life styles and cut-throat competitions, cardiovascular diseases and... more In this era of stress-filled life styles and cut-throat competitions, cardiovascular diseases and heart abnormalities are becoming common in the people of early age groups. In order to detect the cardiac abnormalities, if any, earlier and get properly treated it is necessary to have routine body check-ups and even hospitalization at regular intervals. However, due to time and other constraints it may not be possible, for everyone who is at potential cardiac risk, to maintain regularity in this respect. An easy and convenient option to hospitalization is to use the wearable devices (WD). A compact, light-weighted, rugged and full of features yet affordable WDs are now available for healthcare monitoring. These devices are capable of monitoring and recording vital physiological parameters like body temperature, blood pressure, heart rate and many more for hours and even for days. One such physiologically useful and important device is wearable ambulatory electrocardiogram recorder popularly known as W-ECG or A-ECG recorder. The modern W-ECG/A-ECG recorders not only record the ECG signals and related physiological parameters, but are also capable, due to advancements in telemedicine, of updating the physician whenever an abnormal cardiac event or arrhythmia occurs to the wearer. In this study we have focused on such a wearable ambulatory ECG (A-ECG) recorder and its implications on the recorded A-ECG signals. Although the A-ECG recorders have numerous advantages, the most important being a convenient option to hospitalization, it has several drawbacks as well. The most prominent drawback of an A-ECG recorder is the motion artifacts induced in the recorded A-ECG signals due to various physical activities (PAs) or body movement activities (BMAs) of the subject or the wearer like arms movements, legs movements, walking, climbing stairs up/down, twisting waist or neck, sitting down/standing up or even some light exercises like cycling, jumping, stretching etc. The prime objective of this study is to investigate the impact analysis of these BMAs on A-ECG signal the classification of various types of BMAs. There are several issues related to the A-ECG signal and the motion artifacts associated with it. We mainly focused on the detection of motion artifacts episodes in the A-ECG signals and its impact analysis; the spectral study of motion artifacts; extraction of motion artifacts from A-ECG signals; extracting peculiar features from motion artifact signal and proposing various methods for BMA classification. For detection of motion artifacts and its impact analysis we implemented a recursive principal component analysis (RPCA) based algorithm suggested by Pawar et al. [7], [8]. The RPCA algorithm works on the ECG beats and not on the samples of ECG signal; hence, it is necessary to generate aligned ECG beats of fixed size, with respect to a common R-peak, from the input ECG samples. This requires accurate QRS detection; hence first and second derivative based QRS complex detection algorithms have been implemented as a prerequisite of RPCA algorithm realization. The artifact episodes have been synthetically generated by low-pass filtering the Gaussian noise of three different SNR levels (variable) and four bandwidths (0-5 Hz, 0-10 Hz, 0-15 Hz and 0-20 Hz) and mixed with the ECG signals, available from MIT-BIH arrhythmia database on Physionet. The impact analysis and quantification of RPCA algorithm has been carried out by performing 25 × 36 experiments (36 simulations‒ four noise bandwidths, three SNRs and three forgetting factors‒ on 25 ECG signals). The spectral characteristics of motion artifacts contained in A-ECG signals, recorded by the Biopac MP 36 data acquisition system and the wearable ECG recorder, have been studied using the principal component analysis (PCA) and Wavelet transform based approaches. The A-ECG signals each of duration 300 seconds and in lead II configuration with following BMAs: left arm up-down movement, right arm up-down movement, waist-twist movement and sitting down-standing up walking / movement of five healthy subjects have been recorded using these recorders. The residuals, obtained by applying PCA on recorded A-ECG signals with 5, 10 and 15 principal components, have been regarded as motion artifact signals. Similarly, in wavelet transform based approach the A-ECG signals have been decomposed upto fifth level using ‘bior 3.7’, ‘symlet 4’ and ‘coiflet 5’ wavelets and the motion artifacts have been obtained by collecting the wavelet residuals. The spectral characteristics of the motion artifacts signals obtained by the two approaches have been studied using power spectral density (PSD) plots. In order to classify the BMAs of an individual person, it is necessary to extract the predominant time/frequency features of motion artifact signals. These features have been extracted using Gabor transform and these feature vectors have been fed to three different types of classifiers:…

The Open Biomedical Engineering Journal, 2021

Journal of Health & Medical Economics, 2016

EEG signals of various subjects in text files are uploaded. It can be useful for various EEG sign... more EEG signals of various subjects in text files are uploaded. It can be useful for various EEG signal processing algorithms- filtering, linear prediction, abnormality detection, PCA, ICA etc.

This dataset provides the ECG signals recorded in ambulatory (moving) conditions of subjects. The... more This dataset provides the ECG signals recorded in ambulatory (moving) conditions of subjects. The ambulatory ECG (A-ECG) data acquired with two different recorders viz. Biopac MP36 Acquisition system and a self-developed wearable ECG recorder are made available. Total 10 subjects' (with avg. age of 27 years, 1 female and 9 males) ECG signals with four body movements- Left & Right arm up/down, Sitting down & standing up and Waist twist are uploaded.

The Open Biomedical Engineering Journal, 2021

This paper presents a comprehensive review of the wearable healthcare monitoring systems proposed... more This paper presents a comprehensive review of the wearable healthcare monitoring systems proposed by the researchers to date. One of the earliest wearable recorders, named “a silicon locket for ECG monitoring”, was developed at the Indian Institute of Technology, Bombay, in 2003. Thus, the wearable health monitoring systems, started with the acquisition of a single signal/ parameter to the present generation smart and affordable multi-parameter recording/monitoring systems, have evolved manifolds in these two decades. Wearable systems have dramatically changed in terms of size, cost, functionality, and accuracy. The early-day wearable recorders were with limited functionalities against today’s systems, e.g., Apple’s iWatch which comprises abundant health monitoring features like heart rate monitoring, breathing app, accelerometers, smart walking/ activity monitoring, and alerts. Most of the present-day smartphones are not only capable of recording various health features like body t...

Heart ailments have replaced communicable diseases as the biggest killer in rural & urban India. ... more Heart ailments have replaced communicable diseases as the biggest killer in rural & urban India. So diagnosis of heart diseases is a crucial part in preventing the fatalities occurring because of these diseases. These diseases are commonly known as arrhythmias of electrocardiogram. An electrocardiogram is a significant recording for measuring the electrical activity of the heart. It provides us with a complete picture of the condition of heart. The main purpose of this study is to develop an efficient arrhythmia detection algorithm based on the morphological characteristics of arrhythmias in ECG signal. Each arrhythmia of ECG has different morphology; we exploit this fact to minimize the computation required in classical methods. This system is ideal for use in real time applications.

Compressed Sensing (CS) is a novel approach of reconstructing a sparse signal much below the sign... more Compressed Sensing (CS) is a novel approach of reconstructing a sparse signal much below the significant Nyquist rate of sampling. Due to the fact that ECG signals can be well approximated by the few linear combinations of wavelet basis, this work introduces a comparison of the reconstructed 10 ECG signals based on different wavelet families, by evaluating the performance measures as MSE (Mean Square Error), PSNR (Peak Signal To Noise Ratio), PRD (Percentage Root Mean Square Difference) and CoC (Correlation Coefficient). Reconstruction of the ECG signal is a linear optimization process which consider the sparsity in the wavelet domain, perceived by the fact that higher the sparsity, more better the recovery. L1 minimization is used as the recovery algorithm. The reconstruction results are comprehensively analyzed for three compression ratios, i.e. 2:1, 4:1 and 6:1. The results indicate that reverse biorthogonal wavelet family can give better results for all CRs compared to other fam...

In this paper two digital watermarking schemes for embedding a monochromic watermark image into c... more In this paper two digital watermarking schemes for embedding a monochromic watermark image into color cover image for different noise sequence like Pseudorandom (PN) and White Gaussian Noise (WGN) have been implemented. In order to exploit the correlation properties of both PN and WGN, the correlation based watermarking techniques like- Threshold Based Detection and Comparison Based Detection in Spatial domain have been analyzed for different noise power values and block size. Although the watermarked images appear quite different, visibly from the original image after noise sequence is embedded. As a result of our work, invisible watermarked image is generated by using PN Sequence and visible watermarked image is generated by using WGN Sequence for high value of noise. This paper also gives comparison of these schemes for different noise power values and watermarked images have been verified on the parameters of PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error).

The Open Biomedical Engineering Journal, 2021

IoT Applications for Healthcare Systems

The Open Biomedical Engineering Journal, 2021

Journal of Health & Medical Economics, 2016

The Open Biomedical Engineering Journal, 2021

Sardar Patel University, 2014

In this era of stress-filled life styles and cut-throat competitions, cardiovascular diseases and... more In this era of stress-filled life styles and cut-throat competitions, cardiovascular diseases and heart abnormalities are becoming common in the people of early age groups. In order to detect the cardiac abnormalities, if any, earlier and get properly treated it is necessary to have routine body check-ups and even hospitalization at regular intervals. However, due to time and other constraints it may not be possible, for everyone who is at potential cardiac risk, to maintain regularity in this respect. An easy and convenient option to hospitalization is to use the wearable devices (WD). A compact, light-weighted, rugged and full of features yet affordable WDs are now available for healthcare monitoring. These devices are capable of monitoring and recording vital physiological parameters like body temperature, blood pressure, heart rate and many more for hours and even for days. One such physiologically useful and important device is wearable ambulatory electrocardiogram recorder popularly known as W-ECG or A-ECG recorder. The modern W-ECG/A-ECG recorders not only record the ECG signals and related physiological parameters, but are also capable, due to advancements in telemedicine, of updating the physician whenever an abnormal cardiac event or arrhythmia occurs to the wearer.
In this study we have focused on such a wearable ambulatory ECG (A-ECG) recorder and its implications on the recorded A-ECG signals. Although the A-ECG recorders have numerous advantages, the most important being a convenient option to hospitalization, it has several drawbacks as well. The most prominent drawback of an A-ECG recorder is the motion artifacts induced in the recorded A-ECG signals due to various physical activities (PAs) or body movement activities (BMAs) of the subject or the wearer like arms movements, legs movements, walking, climbing stairs up/down, twisting waist or neck, sitting down/standing up or even some light exercises like cycling, jumping, stretching etc. The prime objective of this study is to investigate the impact analysis of these BMAs on A-ECG signal the classification of various types of BMAs. There are several issues related to the A-ECG signal and the motion artifacts associated with it. We mainly focused on the detection of motion artifacts episodes in the A-ECG signals and its impact analysis; the spectral study of motion artifacts; extraction of motion artifacts from A-ECG signals; extracting peculiar features from motion artifact signal and proposing various methods for BMA classification.
For detection of motion artifacts and its impact analysis we implemented a recursive principal component analysis (RPCA) based algorithm suggested by Pawar et al. [7], [8]. The RPCA algorithm works on the ECG beats and not on the samples of ECG signal; hence, it is necessary to generate aligned ECG beats of fixed size, with respect to a common R-peak, from the input ECG samples. This requires accurate QRS detection; hence first and second derivative based QRS complex detection algorithms have been implemented as a prerequisite of RPCA algorithm realization. The artifact episodes have been synthetically generated by low-pass filtering the Gaussian noise of three different SNR levels (variable) and four bandwidths (0-5 Hz, 0-10 Hz, 0-15 Hz and 0-20 Hz) and mixed with the ECG signals, available from MIT-BIH arrhythmia database on Physionet. The impact analysis and quantification of RPCA algorithm has been carried out by performing 25 × 36 experiments (36 simulations‒ four noise bandwidths, three SNRs and three forgetting factors‒ on 25 ECG signals).
The spectral characteristics of motion artifacts contained in A-ECG signals, recorded by the Biopac MP 36 data acquisition system and the wearable ECG recorder, have been studied using the principal component analysis (PCA) and Wavelet transform based approaches. The A-ECG signals each of duration 300 seconds and in lead II configuration with following BMAs: left arm up-down movement, right arm up-down movement, waist-twist movement and sitting down-standing up walking / movement of five healthy subjects have been recorded using these recorders. The residuals, obtained by applying PCA on recorded A-ECG signals with 5, 10 and 15 principal components, have been regarded as motion artifact signals. Similarly, in wavelet transform based approach the A-ECG signals have been decomposed upto fifth level using ‘bior 3.7’, ‘symlet 4’ and ‘coiflet 5’ wavelets and the motion artifacts have been obtained by collecting the wavelet residuals. The spectral characteristics of the motion artifacts signals obtained by the two approaches have been studied using power spectral density (PSD) plots.
In order to classify the BMAs of an individual person, it is necessary to extract the predominant time/frequency features of motion artifact signals. These features have been extracted using Gabor transform and these feature vectors have been fed to three different types of classifiers: artificial neural network (ANN), neuro-fuzzy classifier (NFC) and support vector machine (SVM) for BMA classification. Using these classifiers single-fold and ten-fold validation experiments have been performed for BMA classification of five subjects. Although, all the three classifiers have achieved an overall classification rate of over 95%, i.e. only 5% of wrong or misclassification, it is the time in which the classification is achieved distinguishes them. It is observed that the NFC due to its more complex structure takes highest classification time, whereas the SVM is the fastest of them all with more rugged and consistent classification performance.