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EN
In this paper, an efficient method based on the Fourier decomposition method (FDM) is presented for noise removal of medical microscopic images. We propose an adaptive thresholding technique based FDM for denoising of heavily degraded images. An accurate image deconvolution is done with variance stabilization transformation and multi-scale Wiener filtering as a pre-processing step. The different series of frequency intrinsic band functions (FIBF’s) obtained with FDM which are further separated into noise and signal-significant FIBF’s based on cosine similarity index. The FDM adaptive thresholding technique is used to filter-out the unwanted frequency coefficients related to mixed Poisson-Gaussian noise (MPG). The thresholded FIBF’s and signal significant FIBF’s are combined to obtained reconstructed output. Finally, the optimization is done using mixed noise unbiased risk estimate (MNURE). To evaluate the effectiveness of proposed scheme, we have compared the results of the proposed scheme with six different state-of-the-art techniques. The simulation results verify, the effectiveness of proposed method. The proposed algorithm achieves better performance in terms of four quantitative evaluation measures by reducing the effect of noise.
2
Content available remote Hand movement recognition from sEMG signals using Fourier decomposition method
EN
Surface electromyogram (sEMG) provides a non-invasive way to collect EMG signals. The sEMG signals acquired from the muscles of the forearm can be used to recognize the hand grasps and gestures. In this work, an automatic recognition algorithm to identify hand movements using sEMG signals has been proposed. The signals are decomposed into Fourier intrinsic band functions (FIBFs) using the Fourier decomposition method (FDM). The features like entropy, kurtosis, and L1 norm are computed for each FIBF. Statistically relevant features are determined using the Kruskal Wallis test and used to train machine learning-based classifiers like support vector machine, k-nearest neighbor, ensemble bagged trees, and ensemble subspace discriminant. Two publicly available datasets are used to test the efficacy of the proposed algorithm. With an average accuracy of 99:49% on the UCI dataset and 93:53% on NinaPro DB5, the proposed method performs superior than the state-of-the-art algorithms. The performance of the proposed algorithm has also been analyzed in the presence of noise. The proposed method is based on Fourier theory, which makes it suitable for real-time implementation due to low computational complexity. It would help in the design of efficient and easy-to-use prosthetic hands.
EN
Sleep apnea is the most common sleep disorder that causes respiratory, cardiac and brain diseases. The heart rate variability (HRV) and the electrocardiogram-derived respiration (EDR) signals to capture the cardio-respiratory information and the features extracted from these two signals have been used for the detection of sleep apnea. Detection of sleep apnea using the combination of HRV and EDR signals may provide more information. This paper proposes a novel method for the automated detection of sleep apnea based on the features extracted from HRV and EDR signals. The method involves the extraction of features from the intrinsic band functions (IBFs) of both EDR and HRV signals, and the classification using kernel extreme learning machine (KELM). The IBFs of HRV and EDR signals are evaluated using the Fourier decomposition method (FDM). The energy and the fuzzy entropy (FE) features are extracted from these IBFs. The kernel extreme learning machine (KELM) classifier with four kernel functions such as 'linear', 'polynomial', 'radial basis function (RBF)' and 'cosine wavelet kernel' is used for the automated detection of sleep apnea. The proposed technique yielded a sensitivity and a specificity of 78.02% and 74.64%, respectively using the public database. The method outperformed some of the reported works using HRV and EDR signals.
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