Barjinder Singh Saini's research while affiliated with National Institute of Technology Jalandhar and other places
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Publications (78)
Parkinson’s disease (PD), a neurodegenerative disorder, is caused due to the lack of dopamine neurotransmitters throughout the substantia nigra. Its diagnosis in the earlier stages is a very intricate process due to non-identified onset symptoms. Thus, it becomes imperative to establish its manifestations, causes, and treatment for better managemen...
Purpose:
An accurate and well-defined survival prediction of High Grade Gliomas (HGGs) is indispensable because of its high incidence and aggressiveness. Therefore, this paper presents a unified framework for fully automatic overall survival classification and its interpretation.
Methods and materials:
Initially, a glioma detection model is util...
The glioma segmentation from the Magnetic Resonance Imaging (MRI) is known to be a tedious task because of the variability in the tumor’s morphology, extent and localization. The commonly used deep learning loss functions need advancement to segment the extremely small and multiple objective areas present in a single MRI. Dice loss is well known fo...
Background
Parkinson’s disease is one of the non-curable diseases and occurs by the prominent loss of neurotransmitter (dopamine) in substantia nigra pars compacta (SNpc). The main cause behind this is not yet identified and even its diagnosis is very intricate phase due to non-identified onset symptoms. Despite the fact that PD has been extensivel...
In the past decade, there has been a remarkable evolution of convolutional neural networks (CNN) for biomedical image processing. These improvements are inculcated in the basic deep learning-based models for computer-aided detection and prognosis of various ailments. But implementation of these CNN based networks is highly dependent on large data i...
Electrocardiogram (ECG) is one of the best representatives of physiological signal that provides the state of the autonomic nervous system, primarily responsible for the cardiac activity. The ECG data compression plays a significant role in localized digital storage or efficient communication channel utilization in telemedicine applications. The lo...
Diabetic retinopathy is a globally rising disease and needs to be taken in concern. It is the problem with vision of diabetic patients due to a disease in the retina of diabetic patients.Diabetic patients have high glucose level in the blood.Our major concern is to predict the disease at early stages.The studies focusses on the modern techniques us...
Parkinson’s disease is perhaps the most well-known neurodegenerative disorder that mainly occurs due to the loss of dopamine-producing neurons and consists of motor/non-motor symptoms. The progression of the symptoms is often varying from one person to another to the diversity of the disease. The condition causes a huge burden both on those affecte...
A significant analysis is routine for Brain Tumor patients and it depends on accurate segmentation of Region of Interest. In automatic segmentation, field deep learning algorithms are attaining interest after they have performed very well in various ImageNet competitions. This review focuses on state-of-the-art Deep Learning Algorithms which are ap...
The primary objective of this chapter is to analyze the existing tools and techniques for medical data security. Typically, medical data includes either medical signals such as electrocardiogram, electroencephalogram, electromyography, or medical imaging like digital imaging and communications in medicine, joint photographic experts group format. T...
This paper aims at presenting a complete picture of advances till now in the field of computer-aided detection of Pulmonary Tuberculosis using Chest X-ray Images. Advances are analyzed in chronological order as they happen and are divided into three phases in which technology shifted into new paradigms. Study concludes that although techniques that...
In this paper, a novel feature selection method is proposed for the categorization of electrocardiogram (ECG) heartbeats. The proposed technique uses the Fisher ratio and BAT optimization algorithm to obtain the best feature set for ECG classification. The MIT-BIH arrhythmia database contains sixteen classes of the ECG heartbeats. The MIT-BIH ECG a...
In this Chapter, a MATLAB-based approach is presented for compression of Electrocardiogram (ECG) data. The methodology employs in three different domains namely direct, transformed and parameter extraction methods. The selected techniques from direct ECG compression methods are TP, AZTEC, Fan, and Cortes. Moreover selected techniques from transform...
The most important factor involved in heart rate variability (HRV) analysis is cardiac input signal, which is achieved in the form of electrocardiogram (ECG). The ECG signal is used for identifying many electrical defects associated with the heart. In this chapter, many issues involved while ECG recording such as type of the recording instrument, v...
This paper presents a novel method for detection of arrhythmia using adaptive continuous Morlet wavelet transform and back-propagation neural network. The detection is based on extracted features in Time–Frequency (T–F) domain from heart rate variability (HRV) signals. For this, HRV signal is segmented into small length (48 inter beat interval). Th...
This paper presents a quality controlled reconstruction of ECG signal by the formulation of 2D Discrete Cosine Transform (DCT) coefficient and iterative JPEG2000 encoding scheme for Electrocardiogram (ECG) data compression. It employs variable blockwise (L = 32 to 2048 samples) DCT to exploit inter and intra-beat correlation inherent in ECG. The Re...
This paper presents an automatic diagnosis system for the tumor grade classification through magnetic resonance imaging (MRI). The diagnosis system involves a region of interest (ROI) delineation using intensity and edge magnitude based multilevel thresholding algorithm. Then the intensity and the texture attributes are extracted from the segregate...
The present paper proposes a complexity sorting and coupled chaotic map mutation mechanism for compression-then-encryption of the Electrocardiogram (ECG) signals. The compressed-then-encrypted ECG is wirelessly transmitted using orthogonal frequency division multiplexing scheme modified to perform impair sample correction. The compression based on...
Signal processing technology comprehends fundamental theory and implementations for processing data. The processed data is stored in different formats. The mechanism of electrocardiogram (ECG) steganography hides the secret information in the spatial or transformed domain. Patient information is embedded into the ECG signal without sacrificing the...
Multilevel thresholding is segmenting the image into several distinct regions. Medical data like magnetic resonance images (MRI) contain important clinical information that is crucial for diagnosis. Hence, automatic segregation of tissue constituents is of key interest to clinician. In the chapter, standard entropies (i.e., Kapur and Tsallis) are e...
The primary objective of this chapter is to analyze the existing tools and techniques for medical data security. Typically, medical data includes either medical signals such as electrocardiogram, electroencephalogram, electromyography, or medical imaging like digital imaging and communications in medicine, joint photographic experts group format. T...
In the chapter, dynamic time domain features are extracted in the proposed approach for the accurate classification of electrocardiogram (ECG) heartbeats. The dynamic time-domain information such as RR, pre-RR, post-RR, ratio of pre-post RR, and ratio of post-pre RR intervals to be extracted from the ECG beats in proposed approach for heartbeat cla...
In this chapter, the BAT-optimized fuzzy k-nearest neighbor (FKNN-BAT) algorithm is proposed for discrimination of the electrocardiogram (ECG) beats. The five types of beats (i.e., normal [N], right bundle block branch [RBBB], left bundle block branch [LBBB], atrial premature contraction [APC], and premature ventricular contraction [PVC]) are taken...
In telecardiology applications, research has been underway to protect the patient's confidential information from unauthorized access using Electrocardiogram (ECG) steganography or encryption approaches. A novel Fused Coupled Chaotic Map (FCCM) structure based integrated embedding-then-encryption approach is proposed for patient's confidential data...
Multilevel thresholding is one of the most popular image segmentation techniques due to its simplicity and accuracy. Most of the thresholding approaches use either the histogram of an image or information from the grey-level co-occurrence matrix (GLCM) to compute the threshold. The medical images like MRI usually have vague boundaries and poor cont...
The electrocardiogram (ECG) non-invasively monitors the electrical activities of the heart to diagnose the heart-related diseases. The baseline wandering noise affects the diagnosis of the heart diseases. In this paper, the baseline wandering noise removal is done using forward–backward Riemann Liouville (RL) fractional integral-based empirical wav...
The electrocardiogram (ECG) non-invasively monitors the electrical activities of the heart. During the process of recording and transmission, ECG signals are often corrupted by various types of noises. Minimizations of these noises facilitate accurate detection of various anomalies. In the present paper, Alexander fractional differential window (AF...
Objective
Cardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate va...
Cardiac diseases are major reason of death in the world populace and the numeral of cases is upsurging every year. Due to cardiac artery disease (CAD), the strength of heart muscles becomes weak and heart pumping is disturbed which may eventually lead to abnormal heart beat and heart failure. Therefore, the beginning stage detection of CAD and card...
Objective
The aims of this study, to investigate the interaction among heart rate variability (HRV), respiratory, systolic arterial blood pressure variability (SABPV) and systolic arterial pressure interval variability (APIV) signals for understanding of cardiovascular control.
Methods
In this study, three methods referred as adaptive continuous M...
In the present work, a fused metabolite ratio is proposed that integrates the conventional metabolite ratios in a weighted manner to improve the diagnostic accuracy of glioma brain tumor categorization. Each metabolite ratio is weighted by the value generated by the Fisher and the Parameter-Free BAT (PFree BAT) optimization algorithm. Here, feature...
Since the QRS complex varies with different cardiac health conditions, therefore efficient and automatic detection of QRS complex and is essential for reliable health condition monitoring. In this work an empirical wavelet transform (EWT)-based algorithm has been used for accurate detection of QRS complex. EWT is one of the adaptive time-frequency...
In this paper, a hybrid filter based on the concept of fractional calculus and Alexander polynomial is proposed. The hybrid filtering mask is constructed by convolving the designed Alexander fractional differential and integral masks. The hybrid mask shows high robustness for images corrupted with Gaussian, salt & pepper, and speckle noises. For th...
In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor from magnetic resonance images having vague boundaries and poor contrast. This novel technique takes into account both the image histogram and the uncertainty infor...
In this paper, a fractional differential zero phase (FDZP) 2D filter is constructed which is based on the technique of zero-phase filtering utilizing the concept of R–L integral with fractional differentiation. The constructed filter mask is used to denoise an image with the forward-backward processing which gives high robustness for images corrupt...
This paper presents a patient’s confidential data hiding scheme in electrocardiogram (ECG) signal and its subsequent wireless transmission. Patient’s confidential data is embedded in ECG (called stego-ECG) using chaotic map and the sample value difference approach. The sample value difference approach effectually hides the patient’s confidential da...
Heartbeat classification algorithm for detection of different arrhythmia
Every woman experiences an extensive fluctuation in HRV during her menstrual cycle and even after menopause. A woman who lives long enough will experience menopause as a normal physiologic event. The study of the influence of premenopausal and postmenopausal symptoms on HRV has not been adequate. During this period, health management is an importan...
In this paper, a correlation guided sparse representation model is proposed for medical images that can be used for up sampling the images. For estimating some of the parameters of the sparse model, the repetitive patterns in the image are analysed. The relation between sparse model and content estimation of the image is explored and this approach...
Cardiovascular Disease (CVD) is globally acknowledged research problem. The continuous Electrocardiogram (ECG) monitoring can assist in tackling the problem of CVD. The redundancy in the monitoring of ECG signal is reduced by various signal processing techniques either in 1D or 2D domain. This chapter is having the sole objective of reviewing the e...
In this paper, a correlation guided sparse representation model is proposed for medical images that can be used for up sampling the images. For estimating some of the parameters of the sparse model, the repetitive patterns in the image are analysed. The relation between sparse model and content estimation of the image is explored and this approach...
Heart rate variability (HRV) plays an important role in regulation of cardiac functioning during the menstrual and menopausal stages of women life. There are limited studies which considered the combined effect of ageing and postural changes on HRV of women. The objective of this study is: 1) to analyse and reveal the cardiac autonomic status of pr...
In this Chapter, a MATLAB-based approach is presented for compression of Electrocardiogram (ECG) data. The methodology employs in three different domains namely direct, transformed and parameter extraction methods. The selected techniques from direct ECG compression methods are TP, AZTEC, Fan, and Cortes. Moreover selected techniques from transform...
This paper proposes an effectual sample entropy (SampEn) based complexity sorting pre-processing technique for two dimensional electrocardiogram (ECG) data compression. The novelty of the approach lies in its ability to compress the quasi-periodic ECG signal by exploiting the intra and inter-beat correlations. The proposed method comprises the foll...
In this paper, a joint use of the discrete cosine transform (DCT), and differential pulse code modulation (DPCM) based quantization is presented for predefined quality controlled electrocardiogram (ECG) data compression. The formulated approach exploits the energy compaction property in transformed domain. The DPCM quantization has been applied to...
The present chapter proposes an automatic segmentation method that performs multilevel image thresholding by using the spatial information encoded in the gray level co-occurrence matrix (GLCM). The 2D local cross entropy approach that has been designed by extending the one dimensional (1-D) cross entropy thresholding method to a two dimensional (2D...
An algorithm is presented for designing a new class of wavelets matched to the Heart Rate Variability (HRV) signals of the menstrual cycle. The proposed wavelets are used to find HRV variations between phases of menstrual cycle. The method finds the signal matching characteristics by minimising the shape feature error using Least Mean Square method...
Multiscale approximate entropy (MAE) is used to quantify the complexity of a time series as a function of time scale τ. Approximate entropy (ApEn) tolerance threshold selection ‘r’ is based on either: (1) arbitrary selection in the recommended range (0.1–0.25) times standard deviation of time series (2) or finding maximum ApEn (ApEnmax) i.e., the p...
Telecardiology includes variety of applications and is one of the fastest-growing fields in telemedicine. In telecardiology the amount of recorded ECG data is very much high, hence the necessity of efficient data compression methods for biomedical signals is currently widely recognized. In this paper improved version of existing ASCII character enc...
Postmenopause is the naturally occurring stage in woman’s life after the permanent cessation of the menstruation longer than 12 months. The transition from the young to the postmenopausal stage impacts the variation in the heart rate. It is important to analyze and detect the variation in Heart Rate Variability (HRV) between young and postmenopausa...
The aim of this study is to propose a new baroreflex sensitivity (BRS) index using improved Hilbert–Huang transform (HHT) using weighted coherence (CW) criterion and apply it to assess baroreflex in supine and standing postures. Improved HHT is obtained by addressing the mode mixing and end effect problems associated with empirical mode decompositi...
Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden in telemedicine applications. In this paper, an improved ECG data compression method using ASCII character encoding is proposed. A QRS detection algorithm firstly applied to separate QRS and non-QRS region. Then, the QRS regions of ECG signal are compre...
In this paper, joint symbolic transfer entropy (JSTE) is explored to quantify causal interactions between systolic blood pressure (SBP) and RR intervals (peak-to-peak distance between consecutive R-peaks) at multiple time scales. SBP →RR coupling (Cs-r) and RR → SBP coupling (Cr-s) coupling is analyzed at multiple time scales and delays. The abilit...
Correlation dimension (CD) is used for analysing the chaotic behaviour of the nonlinear heart rate variability (HRV) time series. In CD, the autocorrelation function is used to calculate the time delay. However, it does not provide optimum values of time delays, which leads to an inaccurate estimation of the HRV between phases of the menstrual cycl...
This study presents an alternative approach to approximate entropy (ApEn) threshold value (r) selection. There are two limitations of traditional ApEn algorithm: (1) the occurrence of undefined conditional probability (CPu) where no template match is found and (2) use of a crisp tolerance (radius) threshold ‘r’. To overcome these limitations, CPu i...
The present paper, proposes an efficient denoising algorithm which works well for images corrupted with Gaussian and speckle noise. The denoising algorithm utilizes the alexander fractional integral filter which works by the construction of fractional masks window computed using alexander polynomial. Prior to the application of the designed filter,...
Citations
... Tumors are segmented using both local and global features. According to Isin et al. [19], one of the most difficult tasks in medicine is brain tumor segmentation. An early diagnosis of, a brain tumor increases the patient's life expectancy. ...
... Abhilasha Agarwal et al. [7] designed a coaxial cavity band pass filter with large bandwidth suitable to be used in spacecraft control applications. ...
... This approach chooses the most powerful subset of features from the original set derived from the segno and covers images to boost the accuracy. Verma et al. [79] developed an FS method that uses the Fisher ratio and BA to categorize electrocardiogram (ECG) heartbeats to achieve the most refined feature set. ...
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... It is an essential tool for diagnosing various cardiac diseases. It is also commonly applied to derive another signal-heart rate variability (HRV), whose high diagnostic ability has been demonstrated in recent years [1][2][3]. In particular, the analysis of frequency components, including high (0.15-0.4 Hz), low (0.04-0.15 Hz), very low (0.004-0.04 Hz), and ultra-low (<0.004 ...
... As a result of decrease of dissipation nodes in hardware implementations, digital techniques offer the primary benefit of lower power consumption and are therefore preferred in applications of wireless [9]. There are compression techniques for ECG signals that are really based on Fourier, Wavelet, or fractal-based transforms [14][15][16][17][18]. These methods may be able to successfully recover the original ECG signal. ...
... The experimental results revealed that the HRV descriptors are effective measures for AF identification. In another study, Singh et al. (2019) proposed an arrhythmia detection technique using a timefrequency (T-F) analysis of HRV features [144]. The back-propagation neural networks, in combination with three different types of decision rules, were employed to achieve the classification accuracy of 95.98% for the low-frequency (LF) band and 97.13% for the highfrequency (HF) band [144]. ...
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... In the optimization context, an adaptive fuzzy K-nearest neighbor classifier was enhanced with a parameter-free optimization technique by extracting the intensity and texture attributes from the region of interest. It can easily outperform the existing enhanced fuzzy K-nearest neighbor classifier in prediction accuracy [26]. A multistage classifier was designed to improve the prediction accuracy and diagnosis by integrating support vector machines, Naive Bayes, and k-nearest neighbor classifiers with an improved particle swarm optimization-based feature selection method [27]. ...
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... It can be categorized into two classes: bimodal and multimodal thresholding. Bimodal segments the image into two separate regions, whereas multimodal type divides an image into a number of separate regions [17], which is out of the scope of this paper. ...
... As presented in Table 4, the proposed approach is compared with hybrid classifiers i.e. Naive Bayes with Bacterial Foraging [21] and Genetic Algorithm [22], K-Nearest Neighbour with Bacterial Foraging and Genetic Algorithm [23,24], the proposed approach achieves a higher accuracy of 86% by optimizing LSTM. ...
... Many medical devices are also widely using operating systems, so they are also vulnerable to attacks, conventional computers. However, devices with special operating systems can also be exposed to cyber-attacks, and a software update mechanism is often used for it (Pandey et al., 2019). ...
