Rahul Kher

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).

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:…

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.

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. 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 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 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).

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...

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 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).

An image data processing method for restoring an image worsened by non-linear characteristics of a transforming system, noises and others is disclosed, by which a quantity called energy function, in which the noises and others described above are taken into consideration, is defined, and treatments for modifying stochastically the value of the gray level of each of the pixels are repeated so that a probability determined by this energy function is made greatest and the function E({xi,j}) represented by the following equation is adopted for the energy function stated above; where xk,l is the value of the gray level of the group of pixels directly adjacent to the sides of the object pixel xi,j; yi,j is the value of the gray level at that time of the object pixel; f(xi,j) is the value corresponding to the true gray level of the object pixel obtained through the transformation system for transforming image signals into a first image data; and alpha is a coefficient.

An image data processing method for restoring an image worsened by non-linear characteristics of a transforming system, noises and others is disclosed, by which a quantity called energy function, in which the noises and others described above are taken into consideration, is defined, and treatments for modifying stochastically the value of the gray level of each of the pixels are repeated so that a probability determined by this energy function is made greatest and the function E({xi,j}) represented by the following equation is adopted for the energy function stated above; where xk,l is the value of the gray level of the group of pixels directly adjacent to the sides of the object pixel xi,j; yi,j is the value of the gray level at that time of the object pixel; f(xi,j) is the value corresponding to the true gray level of the object pixel obtained through the transformation system for transforming image signals into a first image data; and alpha is a coefficient.

Nowadays various automation techniques are being adopted & researched on for increase in productivity, for better accuracy, eliminating the human errors and for safety. Machine Vision is one such advancement in automatic systems. Machine vision performs the tasks that are equivalent to human vision. It helps to automate the systems where there are limitations of human vision like detecting various shades of colors or determining high precise dimensions and thus permitting human employees to serve in more appropriate positions. Now, what happens when the questions turn to "Is this part of correct color?" or "Which parts are blue and which red? " So in our system, colour based identification of the parts will be done and then it will be sorted according to different colours. After recognizing the colour of the object, robotic arm will automatically pick & place it accordingly. If the colour of the work piece is not found in accordance to the required one then it wi...

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 considers the sparsity in the wavelet domain. 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 families.

In this paper, a recursive principal component analysis (RPCA)-based algorithm is applied for detecting and quantifying the motion artifact episodes encountered in an ECG signal. The motion artifact signal is synthesized by low-pass filtering a random noise signal with different spectral ranges of LPF (low pass filter): 0-5 Hz, 0-10 Hz, 0-15 Hz and 0-20 Hz. Further, the analysis of the algorithm is carried out for different values of SNR levels and forgetting factors (α) of an RPCA algorithm. The algorithm derives an error signal, wherever a motion artifact episode (noise) is present in the entire ECG signal with 100% accuracy. The RPCA error magnitude is almost zero for the clean signal portion and considerably high wherever the motion artifacts (noisy episodes) are encountered in the ECG signals. Further, the general trend of the algorithm is to produce a smaller magnitude of error for higher SNR (i.e. low level of noise) and vice versa. The quantification of the RPCA algorithm has been made by applying it over 25 ECG data-sets of different morphologies and genres with three different values of SNRs for each forgetting factor and for each of four spectral ranges.

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.An EEG signals dataset is also provided here.

Today a large attention is given to the development of innovative wearable or ambulatory systems that able to monitor physio-pathological parameters of individuals in their daily activities. The goal to progressively move the health costs from cure to prevention leads to focus on the wellness and preventive cardio fitness. This report describes Wearable ECG recording system aiming to providing continuous monitoring of ECG signal which is amplified by the instrumentation amplifier (INA 321 from Texas Instruments).The acquired ECG signals are converted into the suitable format by the ultra low power microcontroller (MSP 430FG439) for storage in SD card.

Wearable ambulatory ECG (A-ECG) signals obtained using wearable ECG recorders inherently contain the motion artifacts due to various physicals activities of the subject. Classification of four such physical activities (PAs) - left arm up-down, right arm up-down, waist twisting and walking- of five healthy subjects has been performed using neuro-fuzzy classifier (NFC). The Gabor energy feature vectors have been used to train the NFC. The overall PA classification accuracy achieved by the NFC classifier is almost 95% for single-fold as well as ten-fold experiments.

The wearable electrocardiogram (W-ECG) signal inherently contains motion artifacts due to various body movements of the wearer. The W-ECG signals with four body movement activities (BMAs)‒ left arm up-down, right arm up-down, waist-twist and walking have been captured using the wearable ECG recorder. The classification of these four BMAs
has been performed using artificial neural networks (ANN). In the process, the motion artifacts contained in the captured W-ECG signals have been extracted using Wavelet transform and the features of the motion artifacts have been extracted using Gabor transform.

The wearable electrocardiogram (W-ECG) signal inherently contains motion artifacts due to various body movements of the wearer. The W-ECG signals with four body movement activit ies (BMAs) - left arm up-down, right arm up-down, waist-twist and walking of five healthy subjects have been acquired using the wearable ECG recorder. The classification of these four BMAs has been performed using artificial neural networks (A NN). In the process, the motion artifacts contained in the captured W-ECG signals have been extracted using Wavelet transform and the features of the motion artifacts have been extracted using Gabor transform. These feature vectors are fed to a mu lti-layered perceptron neural network (M LPNN) consisting of ten neuron hidden layer. The overall classification accuracy achieved using ANN is close to 92%.

In medical treatment, diagnosis plays very important role. Here we are proposing a reliable, affordable wireless patient monitoring system. We have designed a system that will measure the physiological parameters-body temperature, oxygen saturation in blood (SPO2), heart rate, glucose measure in blood, as well as two bioelectrical signals electrocardiogram (ECG) signals and electroencephalogram (EEG). The recorded parameters and signals will then be transferred via bluetooth communication protocol to an android based smartphone. We also intended to generate an SMS alert in case of emergency situation with location information.

The use of wearable ECG recorders is becoming common nowadays for the people suffering from cardiac disorders. Although it is a convenient option for hospitalization, it has an inherent drawback of recorded ECG being contaminated by motion artifacts due to various body movement activities of the wearer. In this paper, the spectral characteristics of motion artifacts occurring in wearable ECG (W-ECG) signals have been studied using principal component analysis (PCA) and wavelet transform. The residuals of PCA and wavelet transform characterize the spectral behaviour of the motion artifacts occurring in WECG signals. The ECG signals have been acquired from Biopac MP-36 system and a self-developed wearable ECG recorder. The performance is evaluated by power spectral density (PSD) plots of PCA residual errors as well as statistical parameters like mean, median and variance of PCA and wavelet residuals. The PSD plots indicate that the peak frequency of the motion artifacts occurring due ...

Ambulatory ECG (A-ECG) monitoring provides electrical activity of the heart when a person is involved in doing normal routine activities. Thus, the recorded ECG signal consists of cardiac signal along with motion artifacts introduced due to person’s body movements during routine activities. Detection of motion artifacts due to different physical activities might help in further cardiac diagnosis. Ambulatory ECG signal analysis for detection of various body movements using Discrete Wavelet Transform (DWT) and adaptive filtering approaches has been addressed in this paper. The ECG signals of five healthy subjects (aged between 22 to 30 years) were recorded while the person performs various body movements like up and down movement of left hand, up and down movement of right hand, waist twisting movement while standing and change from sitting down on chair to standing up movement in lead I configuration using BIOPAC MP 36 data acquisition system. The features of motion artifact signal, extracted using Gabor transform, have been fed to the train the artificial neural network (ANN) for classifying body movements.

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.