Alzheimer’s disease (AD) is a neurodegenerative disorder and the most common type of dementia. It leads to impairments in cognitive functions and seriously affects quality of life. Early diagnosis of the disease is crucial for effective treatment and management. This study proposes a new method using a modified ResNet18 CNN architecture to detect and monitor AD using electroencephalography (EEG) signals. The standard ResNet18 CNN architecture was simplified to use fewer layers and lower filter degrees to expedite the training procedures. In the proposed method, the scalogram images obtained using the Continuous Wavelet Transform (CWT) from 5 sec EEG segments of the AD and control groups are used as input to the modified ResNet18 CNN architecture. 2D time-frequency images of EEG segments are generated using both Bump wavelet CWT and the Short-Time Fourier Transform (STFT), for comparison. Calculated images are used to train the standard ResNet18, and modified ResNet18 CNN architectures to classify the EEG segments. Experimental results show that the CWT approach achieved higher performance compared to the STFT, and the proposed modified ResNet18 CNN architecture (93.74% accuracy) demonstrated more balanced performance than other architectures, exhibiting no overfitting, and completed the training much faster than other models, providing significant time savings.

Dementia is a prevalent neurological disorder that results in cognitive function decline, significantly impacting the quality of life. In this study, a signal decomposition based method is proposed for the detection and follow-up Alzheimer's Dementia (AD) by using Electroencephalography (EEG) signals. The proposed approach uses the Intrinsic Time Scale Decomposition (ITD) to classify EEG segments of AD patients and control subjects. Signal decomposition process is conducted with 5 seconds EEG segment duration. Proper Rotation Components (PRCs) extracted from the EEG segments are used to train a 1-Dimensional Convolutional Neural Network (1D CNN). The proposed method is compared with classification of 5s duration EEG segments using the same CNN architecture. The experimental results demonstrate that utilizing ITD based approach yields better classification performance when compared to using the plain EEG signals.

The majority of psychogenic nonepileptic seizures (PNESs) are brought on by psychogenic causes, but because their symptoms resemble those of epilepsy, they are frequently misdiagnosed. Although EEG signals are normal in PNES cases, electroencephalography (EEG) recordings alone are not sufficient to identify the illness. Hence, accurate diagnosis and effective treatment depend on long-term video EEG data and a complete patient history. Video EEG setup, however, is more expensive than using standard EEG equipment. To distinguish PNES signals from conventional epileptic seizure (ES) signals, it is crucial to develop methods solely based on EEG recordings. The proposed study presents a technique utilizing short-term EEG data for the classification of inter-PNES, PNES, and ES segments using time-frequency methods such as the Continuous Wavelet transform (CWT), Short-Time Fourier transform (STFT), CWT-based synchrosqueezed transform (WSST), and STFT-based SST (FSST), which provide high-resolution time-frequency representations (TFRs). TFRs of EEG segments are utilized to generate 13 joint TF (J-TF)-based features, four gray-level co-occurrence matrix (GLCM)-based features, and 16 higher-order joint TF moment (HOJ-Mom)-based features. These features are then employed in the classification procedure. Both three-class (inter-PNES versus PNES versus ES: ACC: 80.9%, SEN: 81.8%, and PRE: 84.7%) and two-class (Inter-PNES versus PNES: ACC: 88.2%, SEN: 87.2%, and PRE: 86.1%; PNES versus ES: ACC: 98.5%, SEN: 99.3%, and PRE: 98.9%) classification algorithms performed well, according to the experimental results. The STFT and FSST strategies surpass the CWT and WSST strategies in terms of classification accuracy, sensitivity, and precision. Moreover, the J-TF-based feature sets often perform better than the other two.

Dementia is one of the most common neurological disorders causing defection of cognitive functions, and seriously affects the quality of life. In this study, various methods have been proposed for the detection and follow-up of Alzheimer's dementia (AD) with advanced signal processing methods by using electroencephalography (EEG) signals. Signal decomposition-based approaches such as empirical mode decomposition (EMD), ensemble EMD (EEMD), and discrete wavelet transform (DWT) are presented to classify EEG segments of control subjects (CSs) and AD patients. Intrinsic mode functions (IMFs) are obtained from the signals using the EMD and EEMD methods, and the IMFs showing the most significant differences between the two groups are selected by applying previously suggested selection procedures. Five-time-domain and 5-spectral-domain features are calculated using selected IMFs, and five detail and approximation coefficients of DWT. Signal decomposition processes are conducted for both 1 min and 5 s EEG segment durations. For the 1 min segment duration, all the proposed approaches yield prominent classification performances. While the highest classification accuracies are obtained using EMD (91.8%) and EEMD (94.1%) approaches from the temporal/right brain cluster, the highest classification accuracy for the DWT (95.2%) approach is obtained from the temporal/left brain cluster for 1 min segment duration.

Epilepsy is a neurological disease that is very common worldwide. Patient’s electroencephalography (EEG) signals are frequently used for the detection of epileptic seizure segments. In this paper, a high-resolution time-frequency (TF) representation called Synchrosqueezing Transform (SST) is used to detect epileptic seizures. Two different EEG data sets, the IKCU data set we collected, and the publicly available CHB-MIT data set are analyzed to test the performance of the proposed model in seizure detection. The SST representations of seizure and nonseizure (pre-seizure or inter-seizure) EEG segments of epilepsy patients are calculated. Various features like higher-order joint TF (HOJ-TF) moments and gray-level co-occurrence matrix (GLCM)-based features are calculated using the SST representation. By using single and ensemble machine learning methods such as k-Nearest Neighbor (kNN), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Boosted Trees (BT), and Subspace kNN (S-kNN), EEG features are classified. The proposed SST-based approach achieved 95.1% ACC, 96.87% PRE, 95.54% REC values for the IKCU data set, and 95.13% ACC, 93.37% PRE, 90.30% REC values for the CHB-MIT data set in seizure detection. Results show that the proposed SST-based method utilizing novel TF features outperforms the short-time Fourier transform (STFT)-based approach, providing over 95% accuracy for most cases, and compares well with the existing methods.

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.