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Author

Binish Fatimah

Bio: Binish Fatimah is an academic researcher from CMR Institute of Technology. The author has contributed to research in topics: Computer science & Filter bank. The author has an hindex of 4, co-authored 21 publications receiving 101 citations.

Papers
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Journal ArticleDOI
TL;DR: A new methodology based on the Fourier decomposition method (FDM) to separate both BW and PLI simultaneously from the recorded ECG signal and obtain clean ECG data and has low computational complexity which makes it suitable for real-time pre-processing of ECG signals.

93 citations

Journal ArticleDOI
TL;DR: The single-lead ECG signal is divided into 1-min segments, and separated into frequency bands using Fourier decomposition method, which makes it computationally efficient and can be used for real-time sleep apnea detection.

63 citations

Journal ArticleDOI
TL;DR: In this article , a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images are summarized and reviewed, and an outline of the current state-of-the-art advances and a critical discussion of open challenges are presented.

46 citations

Journal ArticleDOI
TL;DR: An automatic recognition algorithm to identify hand movements using sEMG signals using Fourier intrinsic band functions using the Fourier decomposition method based on Fourier theory, which makes it suitable for real-time implementation due to low computational complexity.

24 citations

Proceedings ArticleDOI
28 Jul 2020
TL;DR: A mental arithmetic task detection algorithm from a single lead EEG signal used to decompose the signal into M uniform sub-bands and features, like energy, entropy, and variance, are computed from each of these sub-band.
Abstract: Solving an arithmetic problem is a complex task which involves fact retrieval, memory, sequencing and decision making. Automatic detection of such an activity from EEG signals will help in understanding of brain response to these cognitive tasks. In this work, we propose a mental arithmetic task detection algorithm from a single lead EEG signal. Fourier Decomposition method is used to decompose the signal into M uniform sub-bands and features, like energy, entropy, and variance, are computed from each of these sub-bands. Kruskal-Wallis method has been used to select only the statistically relevant features. These selected features are, then, used to classify the given EEG dataset into two classes using support vector machine with cubic kernel. To validate the efficacy of the proposed algorithm, simulation results are presented using dataset available on MIT PhysioNet, titled EEG during mental arithmetic task.

21 citations


Cited by
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01 Jan 1993
TL;DR: In this paper, it was shown that 1/f processes are optimally represented in terms of orthonormal wavelet bases, and the wavelet expansion's role as a Karhunen-Loeve-type expansion was developed.
Abstract: The 1/f family of fractal random processes model a truly extraordinary range of natural and man-made phenomena, many of which arise in a variety of signal processing scenarios. Yet despite their apparent importance, the lack of convenient representations for 1/f processes has, at least until recently, strongly limited their popularity. In this paper, we demonstrate that 1/f processes are, in a broad sense, optimally represented in terms of orthonormal wavelet bases. Specifically, via a useful frequency domain characterization for 1/f processes, we develop the wavelet expansion's role as a Karhunen-Loeve-type expansion for 1/f processes. As an illustration of potential, we show that wavelet based representations naturally lead to highly efficient solutions to some fundamental detection and estimation problems involving 1/f processes

314 citations

Journal ArticleDOI
TL;DR: Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance and the experimental result showed that the proposed stationary wavelet transform based ECGDenoising technique outperformed the other ECG Denoising techniques as more ECGs signal components are preserved than other denoised algorithms.
Abstract: Electrocardiogram (ECG) signals are used to diagnose cardiovascular diseases. During ECG signal acquisition, various noises like power line interference, baseline wandering, motion artifacts, and electromyogram noise corrupt the ECG signal. As an ECG signal is non-stationary, removing these noises from the recorded ECG signal is quite tricky. In this paper, along with the proposed denoising technique using stationary wavelet transform, various denoising techniques like lowpass filtering, highpass filtering, empirical mode decomposition, Fourier decomposition method, discrete wavelet transform are studied to denoise an ECG signal corrupted with noise. Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance. The experimental result showed that the proposed stationary wavelet transform based ECG denoising technique outperformed the other ECG denoising techniques as more ECG signal components are preserved than other denoising algorithms.

77 citations

Journal ArticleDOI
TL;DR: The single-lead ECG signal is divided into 1-min segments, and separated into frequency bands using Fourier decomposition method, which makes it computationally efficient and can be used for real-time sleep apnea detection.

63 citations

Journal ArticleDOI
TL;DR: Chaos theory and ARTFA have been considered in this paper to estimate reliable and robust thresholds for R-peak detection and show that the proposed technique enhances sensitivity, positive predictive value and accuracy thereby improving the detection of arrhythmias.
Abstract: Timely detection of cardiac abnormalities from an Electrocardiogram (ECG) signal is very essential. This requires its appropriate and efficient processing. In the literature, most of the researchers focussed on linear techniques that were applied on filtered ECG datasets leaving an ample scope for exploring the use of non-linear techniques in the presence and absence of natural noise-processes. Therefore, there is a need of supplementing the existing research on ECG signal interpretation by using non-linear techniques on noisy ECG data. Non-linear techniques are expected to yield supplementary clues about the non-linearities in the underlying cardiovascular system. One such promising non-linear technique, known as chaos theory (analysis), has been considered here to estimate reliable and robust thresholds for R-peak detection using fractal dimension, Approximate Entropy, Sample Entropy, correlation dimension, and Lyapunov exponent based on time-delay dimension (embedding). Also, time–frequency analysis techniques have shown their effectiveness for analyzing such types of non-linear and non-stationary signals due to simultaneous interpretation of the signal in both time and frequency domain. Among existing TFA techniques such as wavelet transform, short time Fourier transform, Hilbert transform, Auto-regressive Time Frequency Analysis (ARTFA) offers good time–frequency resolution. Therefore, Chaos theory and ARTFA have been considered in this paper. First, raw ECG signal was filtered using Savitzky Golay Digital Filtering (SGDF) because it retains all important signal features after filtering. Second, a novel optimal trajectory detection step was proposed on the basis of phase space reconstruction (attractors) in chaos theory. Third, ARTFA has been used to find the spectral components of the extracted features using chaos theory. Here, ARTFA has been used for finding the autoregressive coefficients in the first step and time–frequency description in the second step. Burg method has been considered for Auto-regressive modeling to fit an ARTFA model for analyzing ECG signal by minimizing (least squares) the forward and backward prediction errors. MIT-BIH arrhythmia database has been considered for validating the present research effort. Some real time signals were also tested to explore direct usage of the proposed technique in practical applications. The obtained results show that the proposed technique enhances sensitivity, positive predictive value and accuracy thereby improving the detection of arrhythmias. Computational cost of the proposed technique is reduced to a great extent by using the chaos theory (analysis) yielding efficient detection performance. All results have been obtained in MATLAB environment R2011a. Improved values viz. 99.96% SE, 99.97% PPV, and 99.93% ACC are obtained.

46 citations