A new statistic has been developed to quantify the amount of regularity in data. This statistic, ApEn (approximate entropy), appears to have potential application throughout medicine, notably in electrocardiogram and related heart rate data analyses and in the analysis of endocrine hormone release pulsatility. The focus of this article is ApEn. We commence with a simple example of what we are trying to discern. We then discuss exact regularity statistics and practical difficulties of using them in data analysis. The mathematic formula development for ApEn concludes the Solution section. We next discuss the two key input requirements, followed by an account of a pilot study successfully applying ApEn to neonatal heart rate analysis. We conclude with the important topic of ApEn as a relative (not absolute) measure, potential applications, and some caveats about appropriate usage of ApEn. Appendix A provides example ApEn and entropy computations to develop intuition about these measures. Appendix B contains a Fortran program for computing ApEn. This article can be read from at least three viewpoints. The practitioner who wishes to use a "black box" to measure regularity should concentrate on the exact formula, choices for the two input variables, potential applications, and caveats about appropriate usage. The physician who wishes to apply ApEn to heart rate analysis should particularly note the pilot study discussion. The more mathematically inclined reader will benefit from discussions of the relative (comparative) property of ApEn and from Appendix A.

A regularity statistic for medical data analysis

Electrocardiography (ECG) plays a fundamental role in the diagnostic work-up of a patient. It is amongst the most commonly used investigations; however, it is impossible to avoid misinterpretation of an unusual ECG. The novice may find it challenging to establish a diagnosis and the senior specialist may miss a subtle change in the ECG. Goldbergers 8th edition of Clinical Electrocardiography has been written for the practising clinician to equip him with the necessary knowledge in not only the correct approach to interpretting an ECG, but also in formulating a management plan. It is written to work as a guide for medical students and allied professionals who would like to understand the basic principles underlying the generation of an ECG as well as for the more experienced clinician who wants to refer to it for an abnormal pattern and compose a management plan. The book provides a systematic approach to ECG analysis and has been divided, for this purpose, into three sections, with individual illustrations and images to improve understanding. The first section covers basic principles of electrocardiography. Focus is given to relevant cardiac anatomy, physiology, normal ECG patterns and the major abnormal depolarisation and repolarisation patterns. The second part deals with the diagnosis of key rhythm disorders relevant to today’s clinical practice, along with management tips. The chapters are divided into brady-arrhythmias and tachy-arrhythmias, covering supra-ventricular and ventricular tachycardia and atrio-ventricular block, as well as dissociation. A chapter is also dedicated to ECG recognition of life-threatening arrhythmias that can cause cardiac arrest. The third part summarises a systematic approach to ECG analysis, highlighting a fourteen point plan in approaching an ECG. The aim is to minimise common mistakes, mostly due to errors of omission. In addition, major and avoidable pitfalls in ECG diagnosis are discussed, for example, marked prolongation of the QT interval, a potential precursor of torsades de pointes, and sudden cardiac arrest, which may be overlooked. Personally, as a trainee in cardiovascular sciences with broad experience in the interpretation of ECGs, I found this book a valuable reference which has enabled me to better develop my insight into ECG analysis through a wealth of clinical highlights, including diagnosis and management tips.

Book Review: Goldberger’s Clinical Electrocardiography. A Simplified Approach:

Power quality improvement in distribution system using distribution static compensator with super twisting sliding mode control

Static Synchronous Compensator (STATCOM) is one of the typical FACTS device which is used to improve the voltage profile and transient stability in power system by damping the low frequency oscillation in it. This paper presents a self tuning PI controller for STATCOM in which the PI controller gains are adopted with the system load impedance by Back Propagation Algorithm. In order to overcome the main drawback of Back Propagation Algorithm as it has slow convergence speed, A novel Bats Echolocation Algorithm is used to improve its speed. To demonstrate the effectiveness of the proposed controller, The Simulation model was realized and the simulation results are presented for different loading conditions. Results are compared with the fixed gain PI controller and Particle Swarm Optimization (PSO) based self tuning PI controllers.

Design of self tuning PI controller for STATCOM using Bats Echolocation Algorithm based Neural controller

Single-phase induction motors (SPIM) are quite effectively used in household machines and equally efficiently for industrial purposes. Mostly, current signature-based techniques are often used for fault diagnosis, which is not always a cost-effective solution. This paper presents a stator winding fault detection scheme of a single-phase induction motor employing a wavelet induced residual-convolutional neural network (W-RCNN) based on a deep learning framework. The structure of W-RCNN possesses a residual block with a skip connection. The importance of using the skip connection is not only to transfer the information of the input data towards the output layer but also to produce an alternative path for gradient transformation as well as feature fusion. In this work, experimental data collected from a healthy 1kW SPIM and another similar rated machine with three-types of winding fault are used as inputs to the W-RCNN architecture for the diagnosis of automated machine faults. The proposed detection scheme is protracted to a statistical study which shows marked improved performance metrics compared to the state-of-the-art methods.

Diagnosis of Stator Winding Fault of Single-Phase Induction Motor Employing Wavelet Induced Residual-Convolutional Neural Network

Heart of DSTATCOM is its controller. Several control algorithms are demonstrated and practiced in this context. This paper enumerates different control algorithms reported in the literature and classified them in distinct categories. It aims at providing a broad perspective on the status of control methodologies of DSTATCOM to the researchers and application engineers dealing with power quality aspects of Autonomous Systems. The references are presented in a classified manner which will serve them as quick and useful reference.

State of art control techniques for DSTATCOM

This paper aims to present an intelligent system for autonomous diagnosis of fetal arrhythmia based on fetal ECG recordings. The present scheme uses one dimensional (1D) convolution with a wavelet kernel to extract time domain features from subjects possessing normal fetal ECG and fetal arrhythmia ECG. Time- domain features obtained from the convoluted signals are fed to a trained artificial neural network (ANN) with gradient descent learning to identify and classify fetal ECG signals. The experimental evaluation of the proposed scheme has been tested with a six- channel fetal ECG signal, available in the NIFEADB database. An overall accuracy of 96% is obtained by evaluating standard performance metrics. The use of 1D convolution not only reduces the computational burden but also helps to specify the feature space to develop an intelligent system for portable embedded system applications.

A Non-Invasive Approach for Fetal Arrhythmia Detection and Classification from ECG Signals

Renewable energies like Photo Voltaic (PV)/ solar power are abundantly available and waiting to be harnessed. Being weak system renewable energy based power systems require reactive power generation close to load to unburden the source. Study reveals fixed tuned Proportional & Integral (PI) controller based DSTATCOM may not be able to provide satisfactory voltage regulation with wide load changes. DSTATCOM generally requires tuning of PI controllers by utility engineers during installation. This process is mostly trial and error approach. It is necessary to re-tune the DSTATCOM controller when there is change in operating condition. To ensure automatic control action irrespective of load conditions, soft-computing technique is implemented in the DSTATCOM. A supervised hybrid algorithm named Neuro- Fuzzy controller is adopted to ensure automatic adaptation of the controller parameters during changing load conditions. The paper presents a Synchronous Reference Frame theory (SRF) based DSTATCOM on MATLAB platform and uses a Neuro- Fuzzy inference system to get a better response in terms of dynamic voltage profile and Total Harmonic Distortion (THD) as compared to simple PI Controller.

A Supervised Hybrid Algorithm based DSTATCOM to Cater to Dynamic Load Changes

DSTATCOM is a shunt compensation device connected at Point of Common Coupling (PCC) of Distribution System to deliver quality power. The proportional & integral controller (PI) of DSTATCOM needs tuning at the time of installation as well as due to change of network parameters and working conditions, can provide a slower response. To ensure plug and play feature to the device with faster response Proportional Integral Derivative (PID) action with fuzzy interference system (FIS) is implemented in this paper. With respect to SRF theory based simple PI controller a better response is obtained with fuzzy PID controller in terms of dynamic voltage profile and Total Harmonic Distortion (THD).

Fuzzy PID Controlled DSTATCOM for Delivering Quality Power

Individual distributed energy resources (DER) because of their small capacity is not able to participate in energy trading. A novel concept has been used in this paper wherein Virtual Power Plant (VPP) acts as an aggregator and can provide scheduled dispatch of power and thereby participate in energy trading of the grid. VPP operator (VO) is the authority of transmit power from several sources maintaining their freedom in property and operation. VO conducts energy trading with the grid on behalf of the VPP. In this study an investigation is carried out to determine economic stature of all the players participating in a VPP system.

Epsita Das

Papers

State of art control techniques for DSTATCOM

A Non-Invasive Approach for Fetal Arrhythmia Detection and Classification from ECG Signals

A Supervised Hybrid Algorithm based DSTATCOM to Cater to Dynamic Load Changes

Fuzzy PID Controlled DSTATCOM for Delivering Quality Power

Economic Analysis for the Players Participating in a Hybrid Solar Virtual Power Plant