Prediction of Decision Outcome via Observation of Brain Activity Signals During Decision-Making Process

Borawska, Anna; Alsakaa, Akeel

doi:10.1007/978-3-319-62938-4_17

Cited by 2 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We noted that Daubechies order 4 wavelet transformation (db4) mother wavelets were very extensive and were deployed to fix actual issues in biomedical engineering. The frequency bands (gamma, beta, alpha, theta and delta) are extracted by way of a 5-level decomposition [26] see in Figure 3 from each segment to transform it into a frequency-domain. The seven statistical moment (5) features are calculated for each subband.…”

Section: 𝐷𝑊𝑇(𝑖 𝑗mentioning

confidence: 99%

Effective electroencephalogram based epileptic seizure detection using support vector machine and statistical moment’s features

Alsakaa

Hussein

Nasralla

et al. 2022

IJECE

Self Cite

View full text Add to dashboard Cite

Epilepsy is one of the widespread disorders. It is a noncommunicable disease that affects the human nerve system. Seizures are abnormal patterns of behavior in the electricity of the brain which produce symptoms like losing consciousness, attention or convulsions in the whole body. This paper demonstrates an effective electroencephalogram (EEG) based seizure detection method using discrete wavelet transformation (DWT) for signal decomposition to extract features. An automatic channel selection method was proposed by the researcher to select the best channel from 23 channels based on maximum variance value. The records were segmented into a nonoverlapping segment with long 1-S. The support vector machine (SVM) model was used to automatically detect segments that contain seizures, using both frequency and time domain statistical moment features. The experimental result was obtained from 24 patients in CHB-MIT database. The average accuracy is 94.1, sensitivity is 93.5, specificity is 94.6 and the false positive rate average is 0.054.

show abstract

Section: 𝐷𝑊𝑇(𝑖 𝑗mentioning

confidence: 99%