Journal of The Institution of Engineers : Series B 

About: Journal of The Institution of Engineers : Series B is an academic journal. The journal publishes majorly in the area(s): Electric power system & AC power. Over the lifetime, 667 publications have been published receiving 2059 citations.

A Review of Particle Swarm Optimization

Abstract: This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 1995–2017. Fifty two papers have been reviewed. They have been categorized into nine categories based on various aspects. This technique has attracted many researchers because of its simplicity which led to many improvements and modifications of the basic PSO. Some researchers carried out the hybridization of PSO with other evolutionary techniques. This paper discusses the progress of PSO, its improvements, modifications and applications.

QRS Complex Detection Using STFT, Chaos Analysis, and PCA in Standard and Real-Time ECG Databases

Abstract: The early detection of heart abnormalities through electrocardiography (ECG) is essential for reducing the prevalence of cardiac arrest worldwide. Often, subjects are unaware of the condition of their hearts until detected at the last stage. In this study, various records in real-time and PhysioNet databases were examined using chaos analysis. Chaos analysis was implemented by plotting different attractors against various time-delay dimensions. The main advantages of chaos analysis approach include: (1) a preprocessing stage is not demanded to the recorded ECG signal, and (2) it helps to estimate the reliable and robust thresholds for QRS detection using time-delay dimension (embedding), correlation dimension, Lyapunov exponent, and entropy. ECG may be a useful candidate to classify heart diseases; however, visualization through ECG may not be sufficient because of the minute differences that exist in the ECG recordings. Therefore, the effective automatic detection of ECG signals is essential. Further, ECG datasets should be analyzed using time–frequency representations for getting frequency contents of the signal at each time point. ECG signals are nonstationary in nature; the assumption of stationarity is valid on a short-time basis. For this purpose, a short-time spectrum is computed using the short-time Fourier transform (STFT) as a feature extraction tool in this paper. Noise and baseline wander are filtered before the STFT operation to ensure correct frequency components of the QRS complex. For filtering, a digital band-pass filter has been used since its filtering characteristics are invariant with drift and temperature. The automatic detection of QRS complex has been proposed which is useful in early diagnosis of cardiac diseases. The essential feature of detection stage is to build feature selection approach for having a minimal feature set which includes ample information about data for the planned application. In this paper, the QRS complex is detected by applying principal component analysis (PCA) on the fused results of individual features extracted using chaos analysis and STFT. Using PCA, the estimated principal components show the degree of morphological beat-to-beat variability. The detection performance is evaluated in terms of sensitivity (Se), positive predictivity (PP), detection error rate (DER), and accuracy (Acc). The proposed technique yields encouraging performance parameter values such as 99.93% Se, 99.97% PP, 0.0895% DER, and 99.91% Acc in the analysis of data from the PhysioNet database and 99.93% Se, 99.96% PP, 0.097% DER, and 99.90% Acc in the analysis of data from the real-time database. Suitable comparisons have been presented with the existing techniques.

ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform

Abstract: Electrocardiogram (ECG) is used to record the electrical activity of the heart. The ECG signal being non-stationary in nature, makes the analysis and interpretation of the signal very difficult. Hence accurate analysis of ECG signal with a powerful tool like discrete wavelet transform (DWT) becomes imperative. In this paper, ECG signal is denoised to remove the artifacts and analyzed using Wavelet Transform to detect the QRS complex and arrhythmia. This work is implemented in MATLAB software for MIT/BIH Arrhythmia database and yields the sensitivity of 99.85 %, positive predictivity of 99.92 % and detection error rate of 0.221 % with wavelet transform. It is also inferred that DWT outperforms principle component analysis technique in detection of ECG signal.

A Critical Review of Feature Extraction Techniques for ECG Signal Analysis

Abstract: An Electrocardiogram (ECG) is a primary and most prevalent non-invasive test performed on the subjects’ (i.e. patients’) with suspected heart problems. It helps in diagnosing important cardiological status of the subject’s heart i.e. normal or abnormal by investigating rhythm of the heart. This interpretation is not always possible using naked eyes, especially for minute aberrations. Therefore, advanced feature extraction methods are required for investigating these minute differences that might be a challenge to be detected by the human eye. Hence, a critical review of feature extraction techniques presented in this paper will help the researchers to make an informed choice of an appropriate technique for developing efficient methodologies for ECG signal processing.

Approximate Computing: A Survey of Recent Trends—Bringing Greenness to Computing and Communication

Abstract: Energy-efficient computing is a much needed technological advantage for future. Approximate or inexact computing is a computing paradigm that can trade energy and computing time with accuracy of output. Recent years have seen a lot of researches in industry as well as academia. The aim of these researches is to fruitfully realize the dream of a greener and energy-efficient computing era. This paper presents a comprehensive and concise survey of the current research trends and contributions in energy-efficient computing from computational point of view. Recent developments in approximate computing hardware, software and approximate data communication have also been discussed in this article.