Large-scale Feature Extraction Research Articles

Grid-tuned ensemble models for 2D spectrogram-based autism classification

BackgroundAutism spectrum disorder (ASD) is a prevalent collection of developmental disorders that affects around one in every 36 children worldwide. Since autistic children exhibit aberrant electroencephalogram (EEG) signals in comparison to typically developing (TD) children, many authors have recently proposed several machine learning (ML) and deep learning (DL) models to classify ASD patients using EEG signals. However, these models lack extensive and large-scale feature extraction and apply multiple benchmark datasets. Furthermore, most of the proposed ML models need feature engineering to extract key features for classification. MethodThis study proposed a DL-based weighted ensemble model to avoid the need for feature engineering. The proposed model is based on finding the best suitable combination of weights using a grid search strategy, treating the highest weighted model as the most accurate, and giving more weightage. The strength of the suggested technique involves extracting features from EEG recordings by transforming raw EEG into multi-channel two-dimensional time–frequency maps (2D spectrograms) using the short-time Fourier transform (STFT). Furthermore, it applies two widely-researched public EEG datasets [i.e., the King Abdulaziz University (KAU) and the University of Sheffield (TUOS) databases] for performance comparison and better generalizability. Additionally, the performance of the proposed model has been compared with other well-known DL models, and our proposed straightforward, easily implemented models surpass these models in terms of accuracy and training time. ResultsThe suggested model yields an accuracy of 94.71% for ASD classification using the KAU dataset and 96.22% accuracy for utilizing the TUOS dataset. ConclusionsEnsembled models can efficiently enhance accuracy, stability, and reproducibility. The demonstrated finding solidifies the applicability of the weighted ensemble model to classify ASD patients utilizing EEG data. The suggested model might be used as a supplemental tool to assist neurologists in diagnosing ASD.

Channels and Features Identification: A Review and a Machine-Learning Based Model With Large Scale Feature Extraction for Emotions and ASD Classification

Autism Spectrum Disorder (ASD) is characterized by impairments in social and cognitive skills, emotional disorders, anxiety, and depression. The prolonged conventional ASD diagnosis raises the sheer need for early meaningful intervention. Recently different works have proposed potential for ASD diagnosis and intervention through emotions prediction using deep neural networks (DNN) and machine learning algorithms. However, these systems lack an extensive large-scale feature extraction (LSFE) analysis through multiple benchmark data sets. LSFE analysis is required to identify and utilize the most relevant features and channels for emotion recognition and ASD prediction. Considering these challenges, for the first time, we have analyzed and evaluated an extensive feature set to select the optimal features using LSFE and feature selection algorithms (FSA). A set of up to eight most suitable channels was identified using different best-case FSA. The subject-wise importance of channels and features is also identified. The proposed method provides the best-case accuracies, precision, and recall of 95, 92, and 90%, respectively, for emotions prediction using a linear support vector machine (LSVM) classifier. It also provides the best-case accuracy, precision, and recall of 100% for ASD classification. This work utilized the largest number of benchmark data sets (5) and subjects (99) for validation reported till now in the literature. The LSVM classification algorithm proposed and utilized in this work has significantly lower complexity than the DNN, convolutional neural network (CNN), Naïve Bayes, and dynamic graph CNN used in recent ASD and emotion prediction systems.