2 research outputs found
A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification
Electrocardiogram (ECG) signals, which capture the heart's electrical
activity, are used to diagnose and monitor cardiac problems. The accurate
classification of ECG signals, particularly for distinguishing among various
types of arrhythmias and myocardial infarctions, is crucial for the early
detection and treatment of heart-related diseases. This paper proposes a novel
approach based on an improved differential evolution (DE) algorithm for ECG
signal classification for enhancing the performance. In the initial stages of
our approach, the preprocessing step is followed by the extraction of several
significant features from the ECG signals. These extracted features are then
provided as inputs to an enhanced multi-layer perceptron (MLP). While MLPs are
still widely used for ECG signal classification, using gradient-based training
methods, the most widely used algorithm for the training process, has
significant disadvantages, such as the possibility of being stuck in local
optimums. This paper employs an enhanced differential evolution (DE) algorithm
for the training process as one of the most effective population-based
algorithms. To this end, we improved DE based on a clustering-based strategy,
opposition-based learning, and a local search. Clustering-based strategies can
act as crossover operators, while the goal of the opposition operator is to
improve the exploration of the DE algorithm. The weights and biases found by
the improved DE algorithm are then fed into six gradient-based local search
algorithms. In other words, the weights found by the DE are employed as an
initialization point. Therefore, we introduced six different algorithms for the
training process (in terms of different local search algorithms). In an
extensive set of experiments, we showed that our proposed training algorithm
could provide better results than the conventional training algorithms.Comment: 44 pages, 9 figure
Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain
Time - frequency (TF)-domain polarization analysis (PA) methods are widely used as a processing tool to decompose multicomponent seismic signals. However, as a drawback, they are unable to obtain sufficient resolution to discriminate between overlapping seismic phases, as they generally rely on a low-resolution time-frequency representation (TFR) method. In this article, we present a new approach to the TF-domain PA methods. More precisely, we provide an in-detailed discussion on rearranging the eigenvalue decomposition polarization analysis (EDPA) formalism in the frequency domain to obtain the frequency-dependent polarization properties from the Fourier coefficients owing to the Fourier space orthogonality. Then, by extending the formulation to the TF domain and incorporating sparsity promoting TFR (SP-TFR), we improve the resolution when estimating the TF-domain polarization parameters. Finally, an adaptive SP-TFF is applied to extract and filter different phases of the seismic wave. By processing earthquake wave-forms, we show that, by combining amplitude, directivity, and rectilinearity attributes on the sparse TF-domain polarization map of the signal, we are able to extract (or filter) different phases of seismic waves. The SP-TFF method is evaluated on synthetic and real data associated with the source mechanism of the M-w = 8.2 earthquake that occurred in the south-southwest of Tres Picos, Mexico. A discussion on the results is given, verifying the efficiency of the method in separating not only the Rayleigh waves from the Love waves hut also in discriminating them from the body and coda waves. The codes and datasets are available at https://github.com/SigProSeismology/SP-TFF, contributing to the geoscience community.info:eu-repo/semantics/publishedVersio