for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of ‘Fourier intrinsic band functions’ (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time–frequency–energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms.

(SUI), and in all languages, were included. Two reviewers extracted data on participants' characteristics, study quality, intervention, cure and adverse effects independently. The data were analysed using Review Manager 5 software. RESULTSThere were 12 RCTs that compared TOT with TVT, and 15 that compared TVTO vs TVT for treating SUI. There were four direct comparison RCTs of TVTO vs TOT. When compared at 1-44 months, the subjective (odds ratio 1.16; 95% confidence interval 0.83-1.6) and objective (0.94; 0.66-1.32) cure of TOT was similar to TVT. For TVTO, the subjective (1.06, 0.85-1.33) and objective cure (1.03, 0.77-1.39) was also similar to TVT. Adverse events such as bladder injuries (TOT, odds ratio 0.11, 0.05-0.25; TVTO, 0.15, 0.06-0.35) and haematomas (0.06, 0.01-0.30) were less in the TOT than TVT. Voiding difficulties (TOT, odds ratio 0.61, 0.35-1.07); TVTO, 0.81, 0.48-1.31) were slightly lower in TOT but this was not statistically significant. Groin/thigh pain (TVTO, odds ratio 8.05, 3.78-17.16) and vaginal injuries (TOT, 5.82, TVTO, 1.69, were more common in the transobturator tapes. Mesh erosion in TVTO (0.77, 0.22-2.72) and TOT (1.11, 0.54-2.28) was similar to TVT. The effectiveness data over 6 months available from four direct comparison studies of TVTO vs TOT suggested equivalent results for objective cure (1.06, 0.65-1.73) and subjective cure (1.37, 0.93-2.00). When compared indirectly, TVTO has similar subjective (1.23, 0.83-1.82) and objective cure (0.97, 0.62-1.52) to TOT. On indirect comparison, the de novo risk of urgency was similar in the two groups but voiding difficulties seemed to be less in the inside-out group. CONCLUSIONThe evidence for the equivalent effectiveness of TOT and TVTO when compared with each other is established over the short-term. Bladder injuries and voiding difficulties seem to be less with inside-out tapes on indirect comparison. An adequate long-term followup of the RCTs is desirable to establish the long-term continued effectiveness of transobturator tapes. KEYWORDStransobturator tape, systematic review, meta-analysis Study Type -Therapy (meta-analysis) Level of Evidence 1a

COVID-19 is caused by a novel coronavirus and has played havoc on many countries across the globe. A majority of the world population is now living in a restricted environment for more than a month with minimal economic activities, to prevent exposure to this highly infectious disease. Medical professionals are going through a stressful period while trying to save the larger population. In this paper, we develop two different models to capture the trend of a number of cases and also predict the cases in the days to come, so that appropriate preparations can be made to fight this disease. The first one is a mathematical model accounting for various parameters relating to the spread of the virus, while the second one is a non-parametric model based on the Fourier decomposition method (FDM), fitted on the available data. The study is performed for various countries, but detailed results are provided for the India, Italy, and United States of America (USA). The turnaround dates for the trend of infected cases are estimated. The end-dates are also predicted and are found to agree well with a very popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the total number of expected cases and deaths are 12.7 × 10 6 and 5.27 × 10 5 , respectively, predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study produces promising results with the potential to serve as a good complement to existing methods for continuous predictive monitoring of the COVID-19 pandemic.

This paper presents a signal modeling-based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform. The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self-similar Gaussian random processes, namely, fractional Brownian motion and fractional Gaussian noises. The statistics of these processes are modeled using a single parameter called the Hurst exponent. In the last stage, the value of Hurst exponent and autoregressive moving average parameters are used as features to design a binary support vector machine classifier to classify pre-ictal, inter-ictal (epileptic with seizure free interval), and ictal (seizure) EEG segments. The performance of the classifier is assessed via extensive analysis on two widely used data set and is observed to provide good accuracy on both the data set. Thus, this paper proposes a novel signal model for EEG data that best captures the attributes of these signals and hence, allows to boost the classification accuracy of seizure and seizure-free epochs.