Shipra Saraswat

Review: Comparison of QRS detection algorithms

An Electrocardiogram (ECG) is graphical representation of the electrical movement of human heart and used in diagnosis of various heart diseases. The primary function of ECG analysis is correct detection of QRS complex and other ECG characteristics [1]. In this review paper, researchers provide a comparative study between four popular algorithms: namely Window pair algorithm, Dynamic Plosion Index algorithm, KNN algorithm, Slope vector waveform algorithm [2-5] against three assessment criteria 1) Cross Validation, 2) Threshold and 3) Robustness to noise in order to obtain a fast, robust and highly accurate QRS detection algorithm which helps in the development of a more robust clinical instrument by making the front end signal processing more effective [6].

Decomposition of ECG Signals Using Discrete Wavelet Transform for Wolff Parkinson White Syndrome Patients

Todays biggest problem in front of healthcare professionals is to achieve a highest accuracy while classifying ECG signals. This paper explores diverse possibilities of the decomposition using DWT method in order to classify Wolff Parkinson White Syndrome ECG signals. In this work, ECG signals are discretely sampled till 5th resolution level of decomposition tree using DWT with daubechies wavelet of order 4 (db4), which helps in smoothing the feature more appropriate for detecting changes in signals. The MIT-BIH database were used for some experimental results.

Classification of ECG Signals Related to Paroxysmal Atrial Fibrillation

Paroxysmal atrial fibrillation is a life threatening arrhythmia which leads to sudden cardiac death. Cardiac professionals are always looking to obtain a maximum accuracy in identifying and treating heart disorders. The new method of automatic feature extraction and classification of paroxysmal atrial fibrillation is proposed in this paper. The first step toward classifying paroxysmal disorder is to decompose the ECG signals (healthy and unhealthy) using wavelet transformation techniques. Corresponding to these decomposed levels, the values of ECG signals are computed on the basis of entropy by using the method of cross recurrence quantification analysis. The classification was implemented by probabilistic neural network (PNN) concept. Overall gained accuracy by using PNN classifier is 86.6%. The purpose of this work is to develop a smart method for the proper classification of paroxysmal AF arrhythmias. Long-Term AF Database (Itafdb) and MIT-BIH Fantasia Database (fantasia) have been chosen from Physio Bank ATM for carrying out this work.

Classification of ECG signals using cross-recurrence quantification analysis and probabilistic neural network classifier for ventricular tachycardia patients

Ventricular Tachycardia (VT) is one of the leading causes of sudden cardiac death in the world. Prediction of VT is usually diagnosed by using Electrocardiogram (ECG) and requires expeditious treatment which reduces the mortality rate. The cross recurrence plot (CRP) toolbox is used for computing the recurrence rate values for both (healthy and unhealthy) subjects and artificial neural network (ANN) toolbox in Matlab is used for generating the accurate results. Radial basis function neural network (RBFNN) is used for designing the probabilistic neural network classifier for discriminating the normal from abnormal (VT) signals based on the recurrence rate values. This paper illustrates the cross recurrence quantification analysis (CRQA) of ECG signals followed by the decomposition method using discrete wavelet transform (DWT) for the analysis of cardiac disorders with sensitivity, specificity of 98.5% and 97.6% respectively and overall accuracy achieved is 98.7%. This paper is useful in adopting automated approach for detecting the cardiac arrhythmias efficiently.

Emotion recognition through wireless signal

Emotion Recognition has expanding significance in helping human-PC collaboration issues. It is a difficult task to understand how other people feel but it becomes even worse to perceive these emotions through a computer. With the advancement in technology and increase in application of artificial intelligence, it has become a necessity to automatically recognize the emotions of the user for the human-computer interactions. The need for emotion recognition keeps increasing and it has become applicable in various fields now days. This paper explores the way to recognize different human emotions from our body through wireless signals.

The detailed experimental analysis of bucket sort

The bucket sort is a non-comparison sorting algorithm in which elements are scattered over the buckets. We have concluded, based on state-of-art that most of the researchers have been using the insertion sort within buckets. The other sorting technique is also used in many papers over the buckets. From the state-of-art of bucket sort, we have analyzed that insertion sort is preferable in case of low volume of data to be sorted. In this work, authors have used the merge, count and insertion sort separately over the buckets and the results are compared with each other. The sorting benchmark has been used to test the algorithms. For testing the algorithms, sorting benchmark has been used. We have defined the threshold (τ) defined the threshold for saving the time and space of the algorithms. Results indicate that, count sort comes out to be more efficient within the buckets for every type of dataset.

Wavelet Transform Based Feature Extraction and Classification of Atrial Fibrillation Arrhythmia

A new approach of automatic classification of atrial fibrillation (AF) arrhythmia is proposed in this paper. Our approach is based on discrete wavelet transform method followed by cross recurrence quantification analysis (CRQA) for extracting the features of experimental ECG signals. The features like laminarity, determinism, entropy, trapping time and transitivity are used for measuring the RQA measures. After that, the classification process has been performed using the concept of probabilistic neural network (PNN) approach. This method is applied to make a differentiation between normal persons and the persons having atrial fibrillation arrhythmia. For testing our approach PHYSIOBANK database of ECG signals have been used. The significance of this classification method has been shown in our Matlab generated results. The outcome of this paper will be very beneficial in treating AF patients. We achieved 100% accuracy by using this method.

Different Scheduling Options in YARN

Todays world, it is critical to manage huge information as the volume of digital information is increasing day by day. The popular handling system like Hadoop to process information proficiently and utilize such planning calculations accurately in brisk time. The Map Reduce structure has turned into the genuine plan for versatile half organized and not organized information handling lately. The Hadoop environment has developed into the next era, which embraces exquisite asset administration plans for occupation booking. It is a framework for reducing the overall length while doing mapping of jobs. As the time has passed MapReduce has achieved few of its impediments with its various pluggable schedulers. So with a specific end goal to conquer the constraints of MapReduce, the upcoming era of MapReduce has been created called as YARN (Yet Another Resource Negotiator). This paper presents various pluggable schedulers that can be configured in a Hadoop cluster along with their implementation and further discussing recently developed scheduling techniques with a brief prologue to YARN.