Classification of Normal, Ictal and Inter-ictal EEG via Direct Quadrature and Random Forest Tree

Abdulhay, Enas; Alafeef, Maha; Abdelhay, Arwa; Al-Bashir, Areen K.

doi:10.1007/s40846-017-0239-z

Cited by 24 publications

(15 citation statements)

References 54 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Gel quantification software (Bio-Rad ImageLab) • PrimerExplorerV5 (https://primerexplorer.jp/e/) • Sensitivity, specificity and accuracy has been calculated using MedCalc Free statistical calculators (https://www.medcalc.org/calc/diagnostic_test.php) based on the following equations: [75][76][77][78][79][80] Accuracy is the total number of samples correctly diagnosed over the total number of tested samples. The accuracy can be calculated using the following equation:…”

Section: Softwarementioning

confidence: 99%

RNA-extraction-free nano-amplified colorimetric test for point-of-care clinical diagnosis of COVID-19

et al. 2021

Self Cite

View full text Add to dashboard Cite

The global pandemic of coronavirus disease 2019 highlights the shortcomings of the current testing paradigm for viral disease diagnostics. Here, we report a stepwise protocol for an RNA-extraction-free nano-amplified colorimetric test for rapid and naked-eye molecular diagnosis of COVID-19. The test employs a unique dual-prong approach that integrates nucleic acid (NA) amplification and plasmonic sensing for point-of-care detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with a sample-to-assay response time of <1 h. The RNA-extraction-free nanoamplified colorimetric test utilizes plasmonic gold nanoparticles capped with antisense oligonucleotides (ASOs) as a colorimetric reporter to detect the amplified nucleic acid from the COVID-19 causative virus, SARS-CoV-2. The ASOs are specific for the SARS-CoV-2 N-gene, and binding of the ASOs to their target sequence results in the aggregation of the plasmonic gold nanoparticles. This highly specific agglomeration step leads to a change in the plasmonic response of the nanoparticles. Furthermore, when tested using clinical samples, the accuracy, sensitivity and specificity of the test were found to be >98.4%, >96.6% and 100%, respectively, with a detection limit of 10 copies/μL. The test can easily be adapted to diagnose other viral infections with a simple modification of the ASOs and primer sequences. It also provides a low-cost, rapid approach requiring minimal instrumentation that can be used as a screening tool for the diagnosis of COVID-19 at point-of-care settings in resource-poor situations. The colorimetric readout of the test can even be monitored using a handheld optical reader to obtain a quantitative response. Therefore, we anticipate that this protocol will be widely useful for the development of biosensors for the molecular diagnostics of COVID-19 and other infectious diseases.

show abstract

Section: Softwarementioning

confidence: 99%

RNA-extraction-free nano-amplified colorimetric test for point-of-care clinical diagnosis of COVID-19

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…(4) There are less parameters which need to set in RF compared to other state-of-the-art classifiers. The number of the base decision trees is always the only variable need to set in RF; according to the study in [ 42 , 43 ], the number of the base learners is set to 150 in this work.…”

Section: Methodsmentioning

confidence: 99%

Autodetection of J Wave Based on Random Forest with Synchrosqueezed Wavelet Transform

Zhao

et al. 2018

BioMed Research International

View full text Add to dashboard Cite

J wave is the bulge generated in the descending slope of the terminal portion of the QRS complex in the electrocardiogram. The presence of J wave may lead to sudden death. However, the diagnosis of J wave variation only depends on doctor's clinical experiences at present and missed diagnosis is easy to occur. In this paper, a new method is proposed to realize the automatic detection of J wave. First, the synchrosqueezed wavelet transform is used to obtain the precise time-frequency information of the ECG. Then, the inverse transformation of SST is computed to get the intrinsic mode function of the ECG. At last, the time-frequency features and SST-based and the entropy features based on modes are fed to Random forest to realize the automatic detection of J wave. As the experimental results shown, the proposed method has achieved the highest accuracy, sensitivity, and specificity compared with existing techniques.

show abstract

“…An epileptic seizure is one of those complex abnormalities detected by EEG. A vast number of methods have been developed for automatic detection of seizures from EEG recordings (see [3][4][5]). Extracting features that best describe the behavior of EEGs is of great importance for automatic seizure detection systems' performance.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

et al. 2020

View full text Add to dashboard Cite

In this paper, we propose a new method based on the dynamic mode decomposition (DMD) to find a distinctive contrast between the ictal and interictal patterns in epileptic electroencephalography (EEG) data. The features extracted from the method of DMD clearly capture the phase transition of a specific frequency among the channels corresponding to the ictal state and the channel corresponding to the interictal state, such as direct current shift (DC-shift or ictal slow shifts) and high-frequency oscillation (HFO). By performing classification tests with Electrocorticography (ECoG) recordings of one patient measured at different timings, it is shown that the captured phenomenon is the unique pattern that occurs in the ictal onset zone of the patient. We eventually explain how advantageously the DMD captures some specific characteristics to distinguish the ictal state and the interictal state. The method presented in this study allows simultaneous interpretation of changes in the channel correlation and particular information for activity related to an epileptic seizure so that it can be applied to identification and prediction of the ictal state and analysis of the mechanism on its dynamics.

show abstract

Classification of Normal, Ictal and Inter-ictal EEG via Direct Quadrature and Random Forest Tree

Cited by 24 publications

References 54 publications

RNA-extraction-free nano-amplified colorimetric test for point-of-care clinical diagnosis of COVID-19

RNA-extraction-free nano-amplified colorimetric test for point-of-care clinical diagnosis of COVID-19

Autodetection of J Wave Based on Random Forest with Synchrosqueezed Wavelet Transform

Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

Contact Info

Product

Resources

About