Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

https://www.repositorio.ufop.br/bitstream/123456789/7011/1/ARTIGO_ECGHeartBeat.pdf

ECG-based heartbeat classification for arrhythmia detection

Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network

Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.

Smart health monitoring systems: an overview of design and modeling.

A survey on ECG analysis

Usage of compressed ECG for fast and efficient telecardiology application is crucial, as ECG signals are enormously large in size. However, conventional ECG diagnosis algorithms require the compressed ECG packets to be decompressed before diagnosis can be performed. This added step of decompression before performing diagnosis for every ECG packet introduces unnecessary delay, which is undesirable for cardiovascular diseased (CVD) patients. In this paper, we are demonstrating an innovative technique that performs real-time classification of CVD. With the help of this real-time classification of CVD, the emergency personnel or the hospital can automatically be notified via SMS/MMS/e-mail when a life-threatening cardiac abnormality of the CVD affected patient is detected. Our proposed system initially uses data mining techniques, such as attribute selection (i.e., selects only a few features from the compressed ECG) and expectation maximization (EM)-based clustering. These data mining techniques running on a hospital server generate a set of constraints for representing each of the abnormalities. Then, the patient's mobile phone receives these set of constraints and employs a rule-based system that can identify each of abnormal beats in real time. Our experimentation results on 50 MIT-BIH ECG entries reveal that the proposed approach can successfully detect cardiac abnormalities (e.g., ventricular flutter/fibrillation, premature ventricular contraction, atrial fibrillation, etc.) with 97% accuracy on average. This innovative data mining technique on compressed ECG packets enables faster identification of cardiac abnormality directly from the compressed ECG, helping to build an efficient telecardiology diagnosis system.

Diagnosis of Cardiovascular Abnormalities From Compressed ECG: A Data Mining-Based Approach

With the rapid development wireless technologies, mobile phones are gaining acceptance to become an effective tool for cardiovascular monitoring. However, existing technologies have limitations in terms of efficient transmission of compressed ECG over text messaging communications like SMS and MMS. In this paper, we first propose an ECG compression algorithm which allows lossless transmission of compressed ECG over bandwidth constrained wireless link. Then, we propose several algorithms for cardiovascular abnormality detection directly from the compressed ECG maintaining end to end security, patient privacy while offering the benefits of faster diagnosis. Next, we show that our mobile phone based cardiovascular monitoring solution is capable of harnessing up to 6.72 times faster diagnosis compared to existing technologies. As the decompression time on a doctor's mobile phone could be significant, our method will be highly advantageous in patient wellness monitoring system where a doctor has to read and diagnose from compressed ECGs of several patients assigned to him. Finally, we successfully implemented the prototype system by establishing mobile phone based cardiovascular patient monitoring.

Novel methods of faster cardiovascular diagnosis in wireless telecardiology

Mobile phones have become an integral part of modern life. Due to the ever increasing processing power, mobile phones are rapidly expanding its arena from a sole device of telecommunication to organizer, calculator, gaming device, web browser, music player, audio/video recording device, navigator etc. The processing power of modern mobile phones has been utilized by many innovative purposes. In this paper, we are proposing the utilization of mobile phones for monitoring and analysis of biosignal. The computation performed inside the mobile phone's processor will now be exploited for healthcare delivery. We performed literature review on RR interval detection from ECG and selected few PC based algorithms. Then, three of those existing RR interval detection algorithms were programmed on JavaTM platform. Performance monitoring and comparison studies were carried out on three different mobile devices to determine their application on a realtime telemonitoring scenario.

/pdf/ecg-r-r-peak-detection-on-mobile-phones-5c6pk4jprq.pdf

ECG R-R Peak Detection on Mobile Phones

A biometric system performs template matching of acquired biometric data against template biometric data [17.1]. These biometric data can be acquired from several sources like deoxyribonucleic acid (DNA), ear, face, facial thermogram, fingerprints, gait, hand geometry, hand veins, iris, keystroke, odor, palm print, retina, signature, voice, etc. According to previous research, DNA, iris and odor provide high measurement for biometric identifiers including universalities, distinctiveness and performance [17.1]. DNA provides a one dimensional ultimate unique code for accurate identification for a person, except for the case of identical twins. In biological terms “Central Dogma” refers to the basic concept that, in nature, genetic information generally flows from the DNA to RNA (ribonucleic acid) to protein. Eventually protein is responsible for the uniqueness provided by other biometric data (finger print, iris, face, retina, etc.). Therefore, it can be inferred that the uniqueness provided by the existing biometric entities is inherited from the uniqueness of DNA. It is imperative to note that shape of the hand or palm print or face or even the shape of particular organs like the heart has distinctive features which can be useful for successful identification. The composition, mechanism and electrical activity of the human heart inherit uniqueness from the individuality of DNA. An electrocardiogram (ECG) represents the electrical activities of the heart. Figure 17.1 shows the inheritance of uniqueness for ECG inherited from the DNA.

ECG-Based Authentication

Existing electrocardiography (ECG) based biometric systems are constantly being challenged by higher misclassification error, longer acquisition time, larger template size, slower processing time and pertinence of abnormal beats within the biometric template. These challenges are the prime hindrance for ECG based biometric being commercialized as a pervasive authentication mechanism. At least,ECGbased biometric can provide a secured mechanism for cardiac patients being monitored over telephony network. In this paper, we present a polynomial distance measurement (PDM) method for ECG based biometric authentication for the very first time, according to the literature and to the best of our knowledge. The proposed PDM method is up to 12 times faster than existing algorithms, requires up to 6.5 times less template storage, needs only 2.49 (average) acquisition time with the highest accuracy rate (up to 100 per cent) when experimented on a population size of 15. Moreover, this proposed ECG based biometric system was deployed on a mobile phone based telemonitoring scenario with multilayer authentication mechanism upholding its applicability.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sec.76

Fahim Sufi

Papers

Diagnosis of Cardiovascular Abnormalities From Compressed ECG: A Data Mining-Based Approach

Novel methods of faster cardiovascular diagnosis in wireless telecardiology

ECG R-R Peak Detection on Mobile Phones

ECG-Based Authentication

Polynomial distance measurement for ECG based biometric authentication