DESIGN OF EFFICIENT ALGORITHMS FOR BRAIN DISEASE IDENTIFICATION AND CLASSIFICATION

Abstract

Brain diseases encompass a wide range of disorders, such as neurodegenerative disorders, cerebrovascular disorders, neurodevelopmental disorders, seizure disorders, and brain tumors that impair cognitive, motor, and behavioral functions of human beings. Among these disorders, neurodegenerative disorders are considered the most prominent brain disorders due to their progressive nature, which leads to a continuous decline in cognitive, motor, and behavioral functions. Unlike other brain disorders, these disorders worsen over time and currently have no definitive cure, which makes them a major challenge for healthcare systems worldwide. Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) are the two most prevalent neurodegenerative disorders, affecting millions of people worldwide and imposing substantial social and economic burdens. AD primarily causes progressive memory loss and cognitive decline due to amyloid plaques and tau tangles, while PD mainly leads to motor and non-motor symptoms like tremor, rigidity, and bradykinesia due to dopaminergic neuron loss and Lewy bodies. Both AD and PD are irreversible, progressive neurodegenerative disorders with no particular cure, leading to a gradual decline in cognitive and motor functions and a significant deterioration in patients’ quality of life. Although the progression of these disorders can be slowed with certain medications and therapies. Early and accurate diagnosis is essential to provide timely and effective interventions. Therefore, early detection is crucial for ensuring the much-needed care and support for patients. This thesis aims to develop optimized, automated frameworks for early and ac- curate identification of these brain diseases using biomedical signal analysis and advanced machine learning (ML) and deep learning (DL) techniques. Biosignaling modalities such as electroencephalography (EEG), gait analysis, and speech pattern assessment are cost-efficient and non-invasive in nature. These signals capture essen- tial physiological and behavioral markers reflecting neurological impairments. These iv modalities enable the detection of subtle abnormalities in brain activity, motor func- tion, and communication, providing valuable insights into the onset and progression of neurodegenerative disorders without the need for invasive or expensive clinical procedures. Biomedical signals are typically high-dimensional and contain redundant or ir- relevant information. Identifying the most informative and discriminative features is crucial to improving classification accuracy and computational efficiency. Hence, feature selection-driven optimization models are developed for brain disease detection. For PD, EEG datasets obtained from OpenNeuro are analyzed using statistical and ensemble-based feature selection methods. The Kruskal–Wallis test and Extra tree classifier (ETC) are used to select the most discriminative EEG features. Additionally, a two-stage PD detection framework is developed to enhance diagnostic accuracy and computational efficiency. Initially, an ETC-based feature selection is employed to obtain the most relevant and discriminative speech features while eliminating re- dundant or non-informative ones. These optimal feature subsets effectively reduced dimensionality and improved the model’s ability to capture meaningful variations associated with PD. To address the issue of class imbalance commonly observed in biomedical datasets, the synthetic minority oversampling technique is applied to generate synthetic samples for the minority class. This ensured balanced training data and prevented bias toward the majority class. Then, a stacked ensemble model is em- ployed for classification, which leverages the complementary strengths of individual classifiers. The proposed two-stage framework significantly improved classification performance for PD detection using speech signals. Identifying the most affected brain regions and corresponding EEG channels is crucial for achieving accurate diagnosis and meaningful neuroscientific interpretation in AD. AD causes progressive neurodegeneration that disrupts neuronal connectivity and alters the brain’s rhythmic activity patterns. These abnormalities are not uniformly distributed but are concentrated in specific cortical areas. Therefore, it is essential to analyze EEG signals across multiple lobes: frontal, temporal, parietal, and occipital lobes to identify the dominant brain regions and EEG channels most influenced by v the disease. Identifying these regions enhances the interpretability of ML models, strengthens the physiological validity of classification outcomes, and supports targeted clinical assessments for early and precise AD diagnosis. For this purposes, a Fourier decomposition and Hilbert transform-based EEG signal analysis (FHESA) method is developed. The FHESA method integrates the Fourier Decomposition Method (FDM) and Hilbert Transform (HT) to extract meaningful features from the EEG signal for efficient classification and brain region analysis. The FHESA method aims to efficiently analyze the EEG data to identify the important brain regions vulnerable to AD, and to assess the impact of various EEG channels for the timely and early detection of AD. The accurate detection and classification of neurological disorders is one of the most challenging tasks due to the overlapping clinical symptoms and shared patholog- ical characteristics of diseases such as AD and Frontotemporal dementia (FTD). Both disorders lead to progressive cognitive decline and behavioral impairments, often resulting in misdiagnosis and delayed treatment. Traditional diagnostic methods heav- ily rely on neuroimaging and clinical assessments, which are both time-consuming and costly. Moreover, biomedical signals such as EEG exhibit non-linear and non- stationary behavior, making it difficult for conventional machine learning methods to capture underlying temporal–spectral dependencies. Therefore, there is a need for an algorithm that can extract robust, noise-invariant, and discriminative features capable of representing complex brain activities associated with different neurological conditions. To address this, wavelet scattering transform-based dementia identification and classification (WavDemNet) is proposed. The model leverages the wavelet scatter- ing transform (WST) to extract robust, noise-invariant features that capture essential time-frequency characteristics and a 1-D convolutional neural network (CNN) to learn discriminative patterns for accurate identification and classification of brain diseases. Manual analysis of biomedical signals is time-consuming. To assist clinicians in real-time decision-making, there is a requirement to develop automated and ef- ficient algorithms that can process signals, extract optimal features, and accurately classify neurological disorders with minimal human intervention. For this purpose, an vi automated algorithmic framework is developed for the early diagnosis of PD. The high- resolution superlet transform (SLT) technique is utilized to obtain the time-frequency representation (TFRs) of the signal. SLT employs multiple wavelets to achieve higher TF resolution while being less leaky than a single wavelet, which makes it more sus- tainable to apply to non-stationary signals. In order to identify PD and assess the PD severity rate, the TFRs are fed into deep neural network (DNN) models as input. This approach eliminates the need of additional handcrafted feature extraction methods, as the DNNs are capable of automatically learning hierarchical and discriminative patterns from the TFRs. This model captures signal variations associated with PD progression and results in accurate detection and severity assessment.