EEG-Based Anamoly Detection for Autistic Kids – A Pilot Study

Radhakrishnan, Menaka; Boruah, Swagata; Ramamurthy, Karthik

doi:10.18280/ts.390327

Cited by 3 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 . We compared 25 supervised regression algorithms (for vault prediction, Additional file 1 : Table S1) and 18 supervised classification models (for ICL size selection, Additional file 1 : Table S1) by the PyCaret library, a python-based framework for automating machine learning workflows [ 30 ]. Stratified tenfold cross-validation was used for metric evaluation on the training set.…”

Section: Methodsmentioning

confidence: 99%

Predicting post-operative vault and optimal implantable collamer lens size using machine learning based on various ophthalmic device combinations

Chen

Yao

et al. 2023

BioMed Eng OnLine

View full text Add to dashboard Cite

Background Implantable Collamer Lens (ICL) surgery has been proven to be a safe, effective, and predictable method for correcting myopia and myopic astigmatism. However, predicting the vault and ideal ICL size remains technically challenging. Despite the growing use of artificial intelligence (AI) in ophthalmology, no AI studies have provided available choices of different instruments and combinations for further vault and size predictions. This study aimed to fill this gap and predict post-operative vault and appropriate ICL size utilizing the comparison of numerous AI algorithms, stacking ensemble learning, and data from various ophthalmic devices and combinations. Results This retrospective and cross-sectional study included 1941 eyes of 1941 patients from Zhongshan Ophthalmic Center. For both vault prediction and ICL size selection, the combination containing Pentacam, Sirius, and UBM demonstrated the best results in test sets [R2 = 0.499 (95% CI 0.470–0.528), mean absolute error = 130.655 (95% CI 128.949–132.111), accuracy = 0.895 (95% CI 0.883–0.907), AUC = 0.928 (95% CI 0.916–0.941)]. Sulcus-to-sulcus (STS), a parameter from UBM, ranked among the top five significant contributors to both post-operative vault and optimal ICL size prediction, consistently outperforming white-to-white (WTW). Moreover, dual-device combinations or single-device parameters could also effectively predict vault and ideal ICL size, and excellent ICL selection prediction was achievable using only UBM parameters. Conclusions Strategies based on multiple machine learning algorithms for different ophthalmic devices and combinations are applicable for vault predicting and ICL sizing, potentially improving the safety of the ICL implantation. Moreover, our findings emphasize the crucial role of UBM in the perioperative period of ICL surgery, as it provides key STS measurements that outperformed WTW measurements in predicting post-operative vault and optimal ICL size, highlighting its potential to enhance ICL implantation safety and accuracy.

show abstract

Section: Methodsmentioning

confidence: 99%

Predicting post-operative vault and optimal implantable collamer lens size using machine learning based on various ophthalmic device combinations

Chen

Yao

et al. 2023

BioMed Eng OnLine

View full text Add to dashboard Cite

show abstract

“…When E(h(d)) → C(n), S → 0.5, that is, when all the data is returned to S of 0.5, there is no obvious abnormal value in all samples; When E(h(d)) → n − 1, S → 0, that is, when the S of the data is much less than 0.5, then they have a big potential to be evaluated as normal. When E(h(d)) → 0, S → 1, that is, when the S of the data return is very close to 1, then they are outliers [25][26][27].…”

Section: Isolation Forest Algorithmmentioning

confidence: 99%

Faulty Detection System Based on SPC and Machine Learning Techniques

Benrabah¹,

Kadri²,

Mouss³

et al. 2022

RIA

View full text Add to dashboard Cite

Starting from a worrying observation, that companies have difficulties controlling the anomalies of their manufacturing processes, in order to have a better control over them, we have realized a case study on the practical data of the Fertial Complex to analyze the main parameters of the ammonia neutralization by nitric acid process. This article proposes a precise diagnostic of this process to detect dysfunction problems affecting the final product. We start with a general diagnosis of the process using the SPC method, this approach is considered an excellent way to monitor and improve the product quality and provides very useful observations that allowed us to detect the parameters that suffer from problems affecting the quality. After the discovery of the parameters incapable to produce the quality required by the standards, we applies two machine learning technologies dedicated to the type of data of these parameters for detected the anomaly, the first technique called The kernel connectivity-based outlier factor (COF) algorithm consists in recording for each object the degree of being an outlier, the second technique called the Isolation Forest, its principle is to establish a forest to facilitate the calculation and description. The results obtained were compared in order to choose which is the best algorithm to monitor and detect the problems of these parameters, we find that the COF method is more efficient than the isolation forest which leads us to rely on this technology in this kind of process in order to avoid passing a bad quality to the customer in future.

show abstract

“…Despite containing crucial information about brain function, EEG signals have lacked a standardized approach for analysis. Previous studies have employed anomaly detection methods such as Isolation Forest, Angle-based Outlier Detector, and Minimum Covariance Determinant models to analyze EEG patterns in autistic children [7]. The coherence between EEG channels has assessed using the NeuCube architecture [8].…”

Section: Introductionmentioning

confidence: 99%

Autism Classification and Identification of Significant Brain Lobe Using Cepstral Coefficients

Shanmugam,

Radhakrishnan

2024

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by restricted, repetitive behaviors and impaired social interaction. Currently, the identification of individuals with ASD largely relies on subjective assessments, presenting a challenge for researchers to distinguish between Typically Developing (TD) children and those with ASD. This study analyzes EEG data from 10 children with ASD and 10 TD children in response to an audio-video stimulus. Two separate analyses were performed on EEG frequency bands within the range of 0-70 Hz and specific frequency bands of 8-30 Hz, aiming to identify the brain lobe region that yields the most significant discrimination between ASD and TD. Parameters such as Linear Frequency Cepstral Coefficients (LFCC), Cepstral energy, signal energy, delta, and delta-delta derivatives were utilized for the analysis. The study deployed classification techniques including K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Decision Tree, Support Vector Machine (SVM), Bagging KNN, and Random Forest (RF). The results indicated that KNN surpassed all other classification models for frequency bands within the range of 0-70Hz, achieving a discriminating accuracy of 98.3% for ASD and TD in the central lobe region (C3, C4, Cz). However, KNN did not yield a significant level of accuracy when applied to a specific frequency band; it was improved by employing Bagging KNN, reaching 93.8% in the central lobe region (C3, C4, Cz). The electrode combination in the central lobe (C3, C4, and Cz) demonstrated superior discrimination between TD and ASD compared to other brain lobes.

show abstract

EEG-Based Anamoly Detection for Autistic Kids – A Pilot Study

Cited by 3 publications

References 29 publications

Predicting post-operative vault and optimal implantable collamer lens size using machine learning based on various ophthalmic device combinations

Predicting post-operative vault and optimal implantable collamer lens size using machine learning based on various ophthalmic device combinations

Faulty Detection System Based on SPC and Machine Learning Techniques

Autism Classification and Identification of Significant Brain Lobe Using Cepstral Coefficients

Contact Info

Product

Resources

About