Barjinder Singh Saini

A joint application of optimal threshold based discrete cosine transform and ASCII encoding for ECG data compression with its inherent encryption.

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 zero-sequence grouped DCT coefficients that were optimally thresholded via Regula-Falsi method. The generated sequence is encoded using Huffman coding. This encoded series is further converted to a valid ASCII code using the standard codebook for transmission purpose. Such a coded series possesses inherent encryption capability. The proposed technique is validated on all 48 records of standard MIT-BIH database using different measures for compression and encryption. The acquisition time has been taken in accordance to that existed in literature for the fair comparison with contemporary state-of-art approaches. The chosen measures are (1) compression ratio (CR), (2) percent root mean square difference (PRD), (3) percent root mean square difference without base (PRD1), (4) percent root mean square difference normalized (PRDN), (5) root mean square (RMS) error, (6) signal to noise ratio (SNR), (7) quality score (QS), (8) entropy, (9) Entropy score (ES) and (10) correlation coefficient (rx,y). Prominently the average values of CR, PRD and QS were equal to 18.03, 1.06, and 17.57 respectively. Similarly, the mean encryption metrics i.e. entropy, ES and rx,y were 7.9692, 0.9962 and 0.0113 respectively. The novelty in combining the approaches is well justified by the values of these metrics that are significantly higher than the comparison counterparts.

Heart Rate Variability-A Bibliographical Survey

The study of heart rate variability (HRV) provides a mean for observing the heart’s ability to respond to normal regulatory signals that affect its rhythm. The HRV analysis has proven useful in diagnosis, treatment and monitoring of various pathologies. The modern field of HRV processing is extremely diverse, involving many areas like spectral estimation, system modeling, nonlinear dynamics and chaotic analysis, etc. With the recognition of significant relationship between the autonomic nervous system and cardiovascular mortality, efforts for development of autonomic activity have led to the use of HRV as one of the most promising markers. Thus, there is an urgent need to keep a track of advancements and activities taking place in this emerging field. This paper gives a bibliographical survey and general backgrounds of research and development in the field of HRV based on over 83 published articles. The collected literature has been divided into many sections so that new researchers do not face any difficulty for obtaining literature in this field.

Optimized Multi Threshold Brain Tumor Image Segmentation Using Two Dimensional Minimum Cross Entropy Based on Co-occurrence Matrix

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) one using the GLCM, serves as a fitness function. The use of conventional exhaustive search based implementations for multilevel thresholding are computationally expensive. Under such conditions evolutionary algorithm like particle swarm optimization (PSO) has been used. The effectiveness of this method was tested on brain tumor MR images and comparison was done with seven other level set based segmentation algorithms, using three different measures (1) Jaccard, (2) Dice and (3) Root mean square error (RMSE). The results demonstrate that average metric values were equal to 0.881902, 0.936394 and 0.070123 for proposed approach, which were significantly better than existing techniques.

Cardiac Arrhythmia Classification Using Error Back Propagation Method

—Arrhythmia classification is a very demanding task in medical domain. A great need of handling voluminous ECG data has posed necessity of using artificial intelligence techniques such as artificial neural network (ANN) for detection and classification of these heart beats. In this paper a neural network technique with error back propagation method has been used to classify four different types of arrhythmias, namely, Left bundle branch block (LBBB), Right bundle branch block (RBBB), Atrial premature beat (APB) and Paced Beat (PB) with normal ECG signal. The multilayer perceptron feedforward neural network has been used for modeling the network architecture. The arrhythmic features, on which classification methodology is based, are chosen from morphology of QRS complex.

Adaptive correlation dimension method for analysing heart rate variability during the menstrual cycle

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 cycle. Thus, an adaptive CD method is presented here to calculate the optimum value of the time delay based upon the information content in the HRV signal. In the proposed method, the first step is to divide the HRV signal into overlapping windows. Afterwards, the time delay is calculated for each window based on the features of the signal. This procedure of finding the optimum time delay for each window is known as adaptive autocorrelation. Then, the CD for each window is calculated using optimum time delays. Finally, adaptive CD is calculated by averaging the CD of all windows. The proposed method is applied on two data sets: (i) the standard Physionet dataset and (ii) the dataset acquired using BIOPAC®MP150. The results show that the proposed method can accurately differentiate between normal and diseased subjects. Further, the results prove that the proposed method is more accurate in detecting HRV variations during the menstrual cycles of 74 young women in lying and standing postures. Three statistical parameters are used to find the effectiveness of adaptive autocorrelation in calculating time delays. The comparative analysis validates the superiority of the proposed method over detrended fluctuation analyses and conventional CD.

A novel feature selection method for brain tumor MR image classification based on the Fisher criterion and parameter-free Bat optimization

The present paper proposes a novel feature selection technique for the MR brain tumor image classification that aims to choose the optimal feature subset with maximum discriminatory ability in the minimum amount of time. It is based on the fusion of the Fisher and the parameter-free Bat (PFree Bat) optimization algorithm. As the conventional Bat algorithm is bad at exploration, a modification is proposed that guides the Bat by the pulse frequency, global best and the local best position. This improved version of Bat referred to as the PFree Bat algorithm eliminates the velocity equation and directly updates the Bat position. Subsequently, this method in conjunction with the Fisher criteria has been used to select the best set of features for brain tumor classification. The chosen features are then fed to the commonly used least square (LS) support vector machine (SVM) classifier to categorize the area of interest into the high or low grade. For the evaluation of the proposed attribute selection method, tenfold cross-validation has been conducted on a set of 95 ROIs taken from the BRATS 2012 dataset. On an extensive comparison with the other hybrid approaches, the proposed approach brought about the 100% recognition rate in the smallest amount of time. Furthermore, an integrated index is proposed that uniquely identifies the best performing algorithm, taking into account the accuracy, number of features and the computational time. For the fair comparison, the performance of the proposed method has also been examined on breast cancer dataset taken from UCI repository. The obtained results validate that the designed algorithm has better average accuracy than existing state-of-the-art works.

A joint intensity and edge magnitude-based multilevel thresholding algorithm for the automatic segmentation of pathological MR brain images

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 contrast. So, segmenting these images using solely histogram or texture attributes of GLCM proves to be insufficient. This paper proposes a novel multilevel thresholding approach for automatic segmentation of tumour lesions from magnetic resonance images. The proposed technique exploits both intensity and edge magnitude information present in image histogram and GLCM to compute the multiple thresholds. Subsequently, using both attributes, a hybrid fitness function has been formulated which can capture the variations in intensity and the edge magnitude present in different tumour groups effectively. Mutation-based particle swarm optimization (MPSO) technique has been used to optimize the fitness function so as to mitigate the problem of high computational complexity existing in the exhaustive search methods. Moreover, MPSO has better exploration capabilities as compared to conventional particle swarm optimization. The performance of the devised technique has been evaluated and compared with two other intensity- and texture-based approaches using three different measures: Jaccard, Dice and misclassification error. To compute these quantitative metrics, experiments were conducted on a series of images, including low-grade glioma tumour volumes taken from brain tumour image segmentation benchmark 2012 and 2015 data sets and real clinical tumour images. Experimental results show that the proposed approach outperforms the other competing algorithms by achieving an average value equal to 0.752, 0.854, 0.0052; 0.648, 0.762, 0.0177; 0.710, 0.813, 0.0148 and 0.886, 0.937, 0.0037 for four different data sets.

A 2D electrocardiogram data compression method using a sample entropy-based complexity sorting approach

Exploitation of inter and intra beat correlation for 2D ECG data compression. A progressive pre-processing techniques to foster the uniformity in 2D ECG array. Quasi-periodic ECG compression using entropy based nonlinear complexity sorting. Proposed method outperforms the variance based linear complexity sorting approach. 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 following steps: (1) QRS detection, (2) Length normalization, (3) Dc equalization, (4) SampEn based nonlinear complexity sorting and (5) Compression using JPEG2000 Codec. The performance has been evaluated over 48 records from the MIT-BIH arrhythmia database. The average quality score (QS) measurements at different residual errors were 42.25, 4.73, and 2.75 for percentage root mean square difference (PRD), PRD1024, and PRD Normalized respectively. The work also reports extensive experimentations on the compressor for various durations of the ECG records (5-30 min, with 5-min increment). The proposed algorithm demonstrates significantly better performance in comparison to the contemporary state-of-the-art works present in the literature. Display Omitted

Segmentation of cancerous regions in liver using an edge-based and phase congruent region enhancement method

Display Omitted Novel level set method for segmenting low contrast cancerous regions is presented.Proposed method uses new stopping function based on edge and phase information.Result indicates the superiority of the proposed method in 2D and 3D CT images. Segmenting low-contrast cancerous regions from Computed Tomography (CT) images is an important task. Region and edge-based active contours fail to perform with such images. Thus, edge-based phase congruent region enhancement is proposed for detecting low-contrast boundaries using new stopping function. The stages of the proposed method are: region separation, region enhancement and Distance Regularised Level Set Evolution (DRLSE) with new stopping function. First, cancerous region is delineated by creating phase map and converting it into an edge map by thresholding. Second, the feature map is created by enhancing features at the boundaries of edge map. The feature map is combined with original image to generate final image. Finally, stopping function is constructed for DRLSE based on the gradient of final image. Experiments were performed on 20 two dimensional and 4 three dimensional CT scans. The proposed method was compared with two active contours. Results prove the superiority of proposed method.

Multiscale Joint Symbolic Transfer Entropy for Quantification of Causal Interactions Between Heart Rate and Blood Pressure Variability Under Postural Stress

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 ability of the approach based on JSTE to detect SBP–RR causal coupling is tested on 42 healthy subjects in supine and upright position along with 21 subjects of EUROBAVAR dataset. In addition, lack of causal coupling from SBP to RR was assessed on 20 post-acute myocardial infarction (AMI) patients. Results demonstrate that (i) standard deviation (SD) of RR interval series and SBP series decreases with time scale τ = 1 to 10 for all types of subjects. (ii) SD in supine is more than that of upright position at each time scale irrespective of types of subjects. (iii) JSTE decreases with time delay for healthy and AMI patients but does not follow decreasing trend for barofle...