Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals

Pham, The‐Hanh; Vicnesh, Jahmunah; Wei, Joel Koh En; Oh, Shu Lih; Arunkumar, N.; Abdulhay, Enas; Ciaccio, Edward J.; Acharya, U. Rajendra

doi:10.3390/ijerph17030971

Cited by 68 publications

(45 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although Subudhi et al [78] and Tawhid et al [79] had obtained higher accuracies than our study, the authors had used lesser data as compared to our study. Pham et al [72] did a similar classification to that of our study and achieved the same accuracy as ours (98.7%). However, Pham et al [72] extracted the HOS bispectrum and nonlinear texture features and employed LSDA, while in our study, we extracted different nonlinear features and had employed MFA.…”

Section: Resultssupporting

confidence: 87%

“…Pham et al [72] did a similar classification to that of our study and achieved the same accuracy as ours (98.7%). However, Pham et al [72] extracted the HOS bispectrum and nonlinear texture features and employed LSDA, while in our study, we extracted different nonlinear features and had employed MFA. Furthermore, we uniquely developed an ASD index.…”

Section: Resultssupporting

confidence: 87%

“…The analysis of variance (ANOVA) test was then applied to differentiate the two groups. The averaged MSE values were reported to be larger in mildly autistic Pham et al [72] obtained EEG signals from 37 healthy subjects and 40 autistic patients and employed higher order spectra (HOS) bispectrum to convert the acquired signals to images and extracted nonlinear features from the images. The locality sensitivity discriminant analysis (LSDA) was then used to condense the features.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

A novel automated autism spectrum disorder detection system

Vicnesh

Arunkumar

et al. 2021

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

show abstract

Section: Resultssupporting

confidence: 87%

Section: Resultssupporting

confidence: 87%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A novel automated autism spectrum disorder detection system

Vicnesh

Arunkumar

et al. 2021

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…LDA is a well-known generic method used for dimensionality reduction and classification [ 25 , 26 ]. LDA tries to find a low dimensionality space for different categories.…”

Section: Methodsmentioning

confidence: 99%

Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals

Shih

Chen

et al. 2020

IJERPH

View full text Add to dashboard Cite

Developing effective risk prediction models is a cost-effective approach to predicting complications of chronic kidney disease (CKD) and mortality rates; however, there is inadequate evidence to support screening for CKD. In this study, four data mining algorithms, including a classification and regression tree, a C4.5 decision tree, a linear discriminant analysis, and an extreme learning machine, are used to predict early CKD. The study includes datasets from 19,270 patients, provided by an adult health examination program from 32 chain clinics and three special physical examination centers, between 2015 and 2019. There were 11 independent variables, and the glomerular filtration rate (GFR) was used as the predictive variable. The C4.5 decision tree algorithm outperformed the three comparison models for predicting early CKD based on accuracy, sensitivity, specificity, and area under the curve metrics. It is, therefore, a promising method for early CKD prediction. The experimental results showed that Urine protein and creatinine ratio (UPCR), Proteinuria (PRO), Red blood cells (RBC), Glucose Fasting (GLU), Triglycerides (TG), Total Cholesterol (T-CHO), age, and gender are important risk factors. CKD care is closely related to primary care level and is recognized as a healthcare priority in national strategy. The proposed risk prediction models can support the important influence of personality and health examination representations in predicting early CKD.

show abstract

“…This class of DL networks is widely used for the detection of epileptic seizures using EEG signals. In two-dimensional convolutional neural networks (2D-CNN), the one-dimensional (1D) EEG signals are first transformed into two-dimensional plots by employing visualization methods such as spectrogram [ 43 ], higher-order bispectrum [ 44 , 45 ], and wavelet transforms, and are then applied to the input of the convolutional network. In 1D architectures, the EEG signals are applied in the one-dimensional form to the input of convolutional networks.…”

Section: Epileptic Seizures Detection Based On DL Techniquesmentioning

confidence: 99%

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

Self Cite

283

109

View full text Add to dashboard Cite

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

show abstract

Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals

Cited by 68 publications

References 59 publications

A novel automated autism spectrum disorder detection system

A novel automated autism spectrum disorder detection system

Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Contact Info

Product

Resources

About