Resolving the Issues of Capon and APES Approach for Projecting Enhanced Spectral Estimation

Prasad, Kantipudi Mvv; Suresh, H. N.

doi:10.11591/ijece.v6i2.pp725-734

Cited by 1 publication

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Still, this work has the major drawbacks of increased complexity in design and high time consumption for processing. Dash et al 27 www.nature.com/scientificreports/ vector [28][29][30] . However, this detection system lacks the issues of reduced robustness and high noisy contents, which affects the entire classification performance.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection

Kantipudi,

Kumar,

Aluvalu

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Detection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes. So, most of the existing research works developed automated mechanisms for designing an EEG-based epileptic seizure detection system. Machine learning techniques are highly used for reduced time consumption, high accuracy, and optimal performance. Still, it limits by the issues of high complexity in algorithm design, increased error value, and reduced detection efficacy. Thus, the proposed work intends to develop an automated epileptic seizure detection system with an improved performance rate. Here, the Finite Linear Haar wavelet-based Filtering (FLHF) technique is used to filter the input signals and the relevant set of features are extracted from the normalized output with the help of Fractal Dimension (FD) analysis. Then, the Grasshopper Bio-Inspired Swarm Optimization (GBSO) technique is employed to select the optimal features by computing the best fitness value and the Temporal Activation Expansive Neural Network (TAENN) mechanism is used for classifying the EEG signals to determine whether normal or seizure affected. Numerous intelligence algorithms, such as preprocessing, optimization, and classification, are used in the literature to identify epileptic seizures based on EEG signals. The primary issues facing the majority of optimization approaches are reduced convergence rates and higher computational complexity. Furthermore, the problems with machine learning approaches include a significant method complexity, intricate mathematical calculations, and a decreased training speed. Therefore, the goal of the proposed work is to put into practice efficient algorithms for the recognition and categorization of epileptic seizures based on EEG signals. The combined effect of the proposed FLHF, FD, GBSO, and TAENN models might dramatically improve disease detection accuracy while decreasing complexity of system along with time consumption as compared to the prior techniques. By using the proposed methodology, the overall average epileptic seizure detection performance is increased to 99.6% with f-measure of 99% and G-mean of 98.9% values.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Step 6: Then, the conditional probability distribution function is estimated as shown in equation (29) Step 7: The activation function is formulated for the network with respect to the hyperbolic tangent function as shown in equation (30).…”

Section: Classificationmentioning

confidence: 99%