Rationale and study design of the MINERVA study: Multicentre Investigation of Novel Electrocardiogram Risk markers in Ventricular Arrhythmia prediction-UK multicentre collaboration

Introduction The purpose of this study is to assess the ability of two new ECG markers (Regional Repolarisation Instability Index (R2I2) and Peak Electrical Restitution Slope) to predict sudden cardiac death (SCD) or ventricular arrhythmia (VA) events in patients with ischaemic cardiomyopathy undergoing implantation of an implantable cardioverter defibrillator for primary prevention indication. Methods and analysis Multicentre Investigation of Novel Electrocardiogram Risk markers in Ventricular Arrhythmia prediction is a prospective, open label, single blinded, multicentre observational study to establish the efficacy of two ECG biomarkers in predicting VA risk. 440 participants with ischaemic cardiomyopathy undergoing routine first time implantable cardioverter-defibrillator (ICD) implantation for primary prevention indication are currently being recruited. An electrophysiological (EP) study is performed using a non-invasive programmed electrical stimulation protocol via the implanted device. All participants will undergo the EP study hence no randomisation is required. Participants will be followed up over a minimum of 18 months and up to 3 years. The first patient was recruited in August 2016 and the study will be completed at the final participant follow-up visit. The primary endpoint is ventricular fibrillation or sustained ventricular tachycardia >200 beats/min as recorded by the ICD. The secondary endpoint is SCD. Analysis of the ECG data obtained during the EP study will be performed by the core lab where blinding of patient health status and endpoints will be maintained. Ethics and dissemination Ethical approval has been granted by Research Ethics Committees Northern Ireland (reference no. 16/NI/0069). The results will inform the design of a definitive Randomised Controlled Trial (RCT). Dissemination will include peer reviewed journal articles reporting the qualitative and quantitative results, as well as presentations at conferences and lay summaries

The Genome of Anopheles darlingi, the main neotropical malaria vector

Anopheles darlingi is the principal neotropical malaria vector, responsible for more than a million cases of malaria per year on the American continent. Anopheles darlingi diverged from the African and Asian malaria vectors ∼100 million years ago (mya) and successfully adapted to the New World environment. Here we present an annotated reference A. darlingi genome, sequenced from a wild population of males and females collected in the Brazilian Amazon. A total of 10 481 predicted protein-coding genes were annotated, 72% of which have their closest counterpart in Anopheles gambiae and 21% have highest similarity with other mosquito species. In spite of a long period of divergent evolution, conserved gene synteny was observed between A. darlingi and A. gambiae. More than 10 million single nucleotide polymorphisms and short indels with potential use as genetic markers were identified. Transposable elements correspond to 2.3% of the A. darlingi genome. Genes associated with hematophagy, immunity and insecticide resistance, directly involved in vectorhuman and vectorparasite interactions, were identified and discussed. This study represents the first effort to sequence the genome of a neotropical malaria vector, and opens a new window through which we can contemplate the evolutionary history of anopheline mosquitoes. It also provides valuable information that may lead to novel strategies to reduce malaria transmission on the South American continent. The A. darlingi genome is accessible at www.labinfo.lncc.br/index.php/anopheles- darlingi. © 2013 The Author(s)

Dominant frequency estimation for atrial fibrillation studies

This research work explores the feasibility of using frequency domain analysis in the study of arrhythmias. The research involves the application of spectrum analysis to obtain the dominant frequency (DF) of atrial electrograms (AE) at different sites in the atria. It is an alternative way of interpreting the chaotic electrical activity seen during AF and reveals critical sites to guide ablation. As longer ablation procedure time implies higher risk to the patient, DF estimation needs to be obtained as quickly as possible. Four techniques (FFT, Blackman-Tukey, Autoregressive and Multiple Signal Classification) were used to compare the computation times taken for spectrum estimation analysis. The FFT technique produces an accurate DF result with the shortest time. DF analysis was first used for ventricular fibrillation with data from the surface of the left ventricle (in animal studies). It was found that spectrograms show the DF drifting along time and with significant changes in power. This approach was then applied for bipolar AF signals (in human studies). The changes of the frequency along time were observed when the stimulation was given, either using high frequency stimulation or drug infusion. We have developed a novel technique for the removal of ventricular signals from virtual AE. The surface ECG is used to identify ventricular activity. A band pass filter (8 Hz to 20 Hz) followed by rectification and then a low pass filter (6 Hz) are used for QRS detection. QRST subtraction was performed using three different approaches: flat, linear and spline interpolation. QRST subtraction affects the power of the signals but not the DF. We also developed an adaptive power threshold tool to observe the distribution of the DFs with an adjustable power threshold setting. Using this tool the 3D maps can display the evolution of the DFs within a chosen threshold power bracket.EThOS - Electronic Theses Online ServiceSponsorship from the Malaysian Ministry of Higher Education and the Malaysian University of Technology (Universiti Teknologi Malaysia)GBUnited Kingdo

Novel acoustic emission signal processing methods for bearing condition monitoring

Rolling Element Bearing is one of the most common mechanical components to be found in critical industrial rotating machinery. Since the failure of bearings will cause the machine to malfunction and may quickly lead to catastrophic failure of the machinery, it is very important to detect any bearing deterioration at an early stage. In this thesis, novel signal processing methods based on Acoustic Emission measurement are developed for bearing condition monitoring. The effectiveness of the proposed methods is experimentally demonstrated to detect and diagnose localised defects and incipient faults of rolling element bearings on a class of industrial rotating machinery – the iGX dry vacuum pump. Based on the cyclostationary signal model and probability law governing the interval distribution, the thesis proposes a simple signal processing method named LocMax-Interval on Acoustic Emission signals to detect a localised bearing defect. By examining whether the interval distribution is regular, a localised defect can be detected without a priori knowledge of shaft speed and bearing geometry. The Un-decimated Discrete Wavelet Transform denoising is then introduced as a pre-processing approach to improve the effective parameter range and the diagnostic capability of the LocMax-Interval method. The thesis also introduces Wavelet Packet quantifiers as a new tool for bearing fault detection and diagnosis. The quantifiers construct a quantitative time-frequency analysis of Acoustic Emission signals. The Bayesian method is studied to analyse and evaluate the performance of the quantifiers. This quantitative study method is also performed to investigate the relationships between the performance of the quantifiers and the factors which are important in implementation, including the wavelet order, length of signal segment and dimensionality of diagnostic scheme. The results of study provide useful directions for real-time implementation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Autoregressive based diagnostics scheme for detection of bearing faults

An investigation into the vibration characteristics of a ‘Roots and Claws’ based dry vacuum pump under different operating conditions was conducted. An AutoRegressive (AR)-based condition monitoring algorithm was developed and tested on both a fault-free and a pump with an implanted ceramic bearing with an inner race defect at the High Vacuum (HV) end. The investigation provided some in-depth understanding of the effects of different operating conditions such as speed and load on the vibration of the pump. The first key step in the fault detection scheme was accurate determination of the running speed of the pump. It was observed that the rotating speed of the pump’s rotor shaft on which the bearing case was directly connected to was often less than the set speed of the pump due to rotor slip. The second step was envelope demodulation of the time domain vibration signals where the resonance excited by the fault-induced impacts was identified and the vibration signal were bandpass filtered around the resonant peak. The third step is spectral estimation using parametric-based method of AR modelling. The advantage of the AR method is that it can work with smaller sample sizes and sampling rates compared to the more traditional approach of FFT (Fast Fourier Transform) and achieve far superior resolution capabilities. The analysis results showed that the effect of actual speed was predominant in the detection of bearing faults as this was the speed that was used in the calculations of the bearing defect frequencies and had to be determined very accurately. Initial results show that the fault diagnostic scheme is very promising and the bearing fault could be accurately determined at all speeds

Autoregressive Order Selection for Rotating Machinery

This paper provides a practical rule for determining the minimum model order for Autoregressive (AR) based spectrum analysis of data from rotating machinery. The use of parametric methods for spectral estimation, though having superior frequency resolution than Fast Fourier Transform (FFT) based methods, has remained less favoured mainly because of the difficulties in estimating the model order. The minimum model order pmin required is the ratio of the sampling rate and the rotating speed of the machine. This is the number of samples in one shaft revolution. Traditional model order selection criteria, Akaike Information Criterion (AIC), Finite Information Criterion (FPE), Minimum Description Length (MDL), Criterion Autoregressive Transfer-function (CAT), and Finite Information Criterion (FIC) are used to estimate the optimal order. These asymptotic criteria for model order estimation are functions of the prediction error and the optimal order of an AR model is chosen as the minimum of this function. Experimental results, using vibration data taken from a dry vacuum pump at different sampling rates and rotating speeds, show that at there is a pmin marked reduction in the prediction error. For low speed rotating machinery, the optimal order is pmin. As the speed of the rotating machine increases, there is some advantage in using twice or thrice pmin, to produce more accurate frequency estimates. The Box- Jenkins method of order determination using autocorrelation and partial autocorrelations plots are also used for justification of the selection of this minimal order

Discrete wavelet-based thresholding study on acoustic emission signals to detect bearing defect on a rotating machine

A five stage “Roots and Claw” dry vacuum pump is a typical kind of quasi-steady state high speed rotating machine. The research using the novel Acoustic Emission measurement and Wavelet technique aims to develop advanced detection methods for dry vacuum pumps to prevent pumps’ failure. In this paper, denoising problem of Acoustic Emission signal is studied by using Discrete Wavelet Transform thresholding methods. The Donoho-Johnstone threshold method and parameter method are studied and compared. The Birgé-Massart strategy outperforms other estimators in our case. The denoised Acoustic Emission signals enable detection of the defect and identification of the type of bearing defect. Care has to be taken on proper selecting wavelet basis to reduce the bias and error. The study shows us the Discrete Wavelet Transform-based thresholding method is suitable for Acoustic Emission signals to detect bearing defect of rotating machines

A fault detection tool using analysis from an autoregressive model pole trajectory

A new scheme is proposed that combines autoregressive (AR) modelling techniques and pole-related spectral decomposition for the study of incipient single-point bearing defects for a vibration-based condition monitoring system. Vibration signals obtained from the ball bearings from the high vacuum (HV) and low vacuum (LV) ends of a dry vacuum pump run in normal and faulty conditions are modelled as time-variant AR series. The appearance of spurious peaks in the frequency domain of the vibration signatures translates to the onset of defects in the rolling elements. As the extent of the defects worsens, the amplitudes of the characteristic defect frequencies’ spectral peaks increase. This can be seen as the AR poles moving closer to the unit circle as the severity of the defects increase. The number of poles equals the AR model order. Although not all of the poles are of interest to the user. It is only the poles that have angular frequencies close to the characteristic bearing defect frequencies that are termed the ‘critical poles’ and are tracked for quantification of the main spectral peaks. The time-varying distance, power and frequency components can be monitored by tracking the movement of critical poles. To test the efficacy of the scheme, the proposed method was applied to increasing frame sizes of vibration data captured from a pump in the laboratory. It was found that a sample size of 4000 samples per frame was sufficient for almost perfect detection and classification when the AR poles’ distance from the centre of unit circle was used as the fault indicator. The power of the migratory poles was an alternative perfect classifier, which can be used as a fault indicator. The analysis has been validated with actual data obtained from the pump. The proposed method has interesting potential applications in condition monitoring, diagnostic and prognostic-related systems

Comparison of computation time for estimation of dominant frequency of atrial electrograms: Fast fourier transform, blackman tukey, autoregressive and multiple signal classification

Dominant frequency (DF) of electrophysiological data is an effective approach to estimate the activation rate during Atrial Fibrillation (AF) and it is important to understand the pathophysiology of AF and to help select candidate sites for ablation. Frequency analysis is used to find and track DF. It is important to minimize the catheter insertion time in the atria as it contributes to the risk for the patients during this procedure, so DF estimation needs to be obtained as quickly as possible. A comparison of computation tim- es taken for spectrum estimation analysis is presented in this paper. Fast Fourier Transform (FFT), Blackman-Tukey (BT), Autoregressive (AR) and Multiple Signal Classification (MUSIC) methods are used to obtain the frequency spectrum of the signals. The time to produce DF was measured for each method. The method which takes the shortest time for analysis is selected for real time application purpose