Grammatical Evolution-Based Feature Extraction for Hemiplegia Type Detection

Abstract

Hemiplegia is a condition caused by brain injury and affects a significant percentage of the population. The effect of patients suffering from this condition is a varying degree of weakness, spasticity, and motor impairment to the left or right side of the body. This paper proposes an automatic feature selection and construction method based on grammatical evolution (GE) for radial basis function (RBF) networks that can classify the hemiplegia type between patients and healthy individuals. The proposed algorithm is tested in a dataset containing entries from the accelerometer sensors of the RehaGait mobile gait analysis system, which are placed in various patients’ body parts. The collected data were split into 2-second windows and underwent a manual pre-processing and feature extraction stage. Then, the extracted data are presented as input to the proposed GE-based method to create new, more efficient features, which are then introduced as input to an RBF network. The paper’s experimental part involved testing the proposed method with four classification methods: RBF network, multi-layer perceptron (MLP) trained with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) training algorithm, support vector machine (SVM), and a GE-based parallel tool for data classification (GenClass). The test results revealed that the proposed solution had the highest classification accuracy (90.07%) compared to the other four methods.

Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

“…Grammatical Evolution is an evolutionary algorithm, used to produce valid programs in any language defined by a BNF grammar, and it has been used in a variety of cases, such as solving trigonometric identities [49], automatic composition of music [50], combinatorial optimization problems [51], etc. The feature construction method was initially proposed by Gavrilis et al [52] and was applied in many real world problems, such as classification of EEG signals [53], prediction of COVID-19 cases [54], Hemiplegia type detection [55], etc. The feature construction method creates artificial features from the original ones through Grammatical Evolution, and every set of potential features is evaluated on the training set using a machine learning method.…”

“…The BFGS algorithm's capacity for non-convex optimization is leveraged to achieve convergence towards optimal parameter values, showcasing its potential as an alternative in neural network optimization paradigms. It has been utilized in various experimental studies using machine learning, for instance, in automatic EEG epilepsy detection [66], feature extraction for hemiplegia type detection [55], Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication [41], machine learning for the performance and early drop prediction for higher education students [67], and many more.…”

“…This section reports on the assessment of the proposed FC2RBF technique's efficacy in creating artificial features for feature learning and class prediction using the three datasets from the SmartSpeech project (see Section 3). These issues have been extensively examined by numerous scholars in the pertinent academic discourse, encompassing a diverse array of research domains spanning from economics to health [3,40,54,55,73,74].…”

Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders

“…Using an accelerometer sensor dataset, this approach was put to the test using four different classification techniques: RBF network, multi-layer perceptron (MLP) trained using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) training algorithm, support vector machine (SVM), and GenClass, a GEbased parallel tool for data classification. The test results showed that the suggested solution had the best classification accuracy (90.07%) [59]. Various approaches of neural networks and deep neural networks have been used for classification of speech quality and voice disorders with very promising results [43][44][45][46][47]60,61].…”

“…The suggested approach reported high accuracy results (89.95%), and thus, it was suited for direct drivers' mental state evaluation for road safety and accident avoidance in a future in-vehicle smart system. Further, for the hemiplegia type classification among patients and healthy individuals, an automatic feature selection and building method based on grammatical evolution (GE) for radial basis function (RBF) networks was presented [59]. Using an accelerometer sensor dataset, this approach was put to the test using four different classification techniques: RBF network, multi-layer perceptron (MLP) trained using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) training algorithm, support vector machine (SVM), and GenClass, a GEbased parallel tool for data classification.…”