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 persistent and recurring neurological condition in a community of brain neurons that results from sudden and abnormal electrical discharges. This paper introduces a new form of assessment and interpretation of the changes in electroencephalography (EEG) recordings from different brain regions in epilepsy disorders based on graph analysis and statistical rescale range analysis. In this study, two different states of epilepsy EEG data (preictal and ictal phases), obtained from 17 subjects (18 channels each), were analyzed by a new method called state transfer network (STN). The analysis performed by STN yields a network metric called motifs, which are averaged over all channels and subjects in terms of their persistence level in the network. The results showed an increase of overall motif persistence during the ictal over the preictal phase, reflecting the synchronization increase during the seizure phase (ictal). An evaluation of intermotif cross-correlation indicated a definite manifestation of such synchronization. Moreover, these findings are compared with several other well-known methods such as synchronization likelihood (SL), visibility graph similarity (VGS), and global field synchronization (GFS). It is hinted that the STN method is in good agreement with approaches in the literature and more efficient. The most significant contribution of this research is introducing a novel nonlinear analysis technique of generalized synchronization. The STN method can be used for classifying epileptic seizures based on the synchronization changes between multichannel data.

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.

This research presents a new method for detecting obsessive-compulsive disorder (OCD) based on time-frequency analysis of multi-channel electroencephalogram (EEG) signals using the multi-variate synchrosqueezing transform (MSST). With the evolution of multi-channel sensor implementations, the employment of multi-channel techniques for the extraction of features arising from multi-channel dependency and mono-channel characteristics has become common. MSST has recently been proposed as a method for modeling the combined oscillatory mechanisms of multi-channel signals. It makes use of the concepts of instantaneous frequency (IF) and bandwidth. Electrophysiological data, like other nonstationary signals, necessitates both joint time-frequency analysis and independent time and frequency domain studies. The usefulness and effectiveness of a multi-variate, wavelet-based synchrosqueezing algorithm paired with a band extraction method are tested using electroencephalography data obtained from OCD patients and control groups in this research. The proposed methodology yields substantial results when analyzing differences between patient and control groups.

Obsessive-compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample [Formula: see text]-test and [Formula: see text]-test has shown the significance of such new methodology.

Lung cancer is one of the primary causes of cancer-related death worldwide. A computer-aided detection (CAD) can help radiologists by offering a second opinion and making the whole process faster at an early level. In this study, we propose a new classification approach for pulmonary nodule detection from CT imagery by using morphological features of nodule patterns. Ensemble learning approaches are used for classification process and overall detection performance is evaluated. Results are compared to similar techniques in the literature by using standard measures. The performance of the proposed system with random forest based on ensemble learning approaches results in an overall accuracy of 98.7 % with a sensitivity of 100 % and specificity of 97.3 % in training data set and an overall accuracy of 80.7 % with a sensitivity of 80.7 % and specificity of 80.6 % in testing dataset.

The use of space time block code (STBC) is analyzed in combination with spatial modulation (SM) for cooperative communications. A novel cooperation scenario and relay selection algorithm is proposed for wireless communications using cooperative spatial modulation. STBC is implemented in order to reduce the receiver complexity and achieve higher diversity gains. The proposed method is based on the selection of best relay from relay groups for cooperative STBC based SM. A low complexity maximum likelihood (ML) decoder is used to decide on the transmitted symbols and the relay indices. The bit error rates (BER) are calculated to test the transmission performance of the proposed method using computer simulations. Results show that the proposed STBC based cooperative SM scheme improves the bit error rate performance. Results demonstrate that our proposed method gives better performance than cooperative spatial modulation method proposed before, STBC-SM and STBC cooperative SM without relay selection.

We propose the discrete linear chirp transform (DLCT) for decomposing a non‐stationary signal into intrinsic mode chirp functions. The decomposition of a signal into a finite number of intrinsic mode functions (IMFs) was introduced by the empirical mode decomposition (EMD). It exploits the local time‐scale signal characteristics of the signal and provides spectral estimates obtained via the Hilbert transform. Although efficient, the EMD does not provide an analytic representation of the IMFs and is susceptible to noise and to closeness or overlap of the frequency of the IMFs. Using linear chirps as IMFs, the DLCT, a joint frequency instantaneous frequency procedure, provides a parsimonious local orthogonal representation of non‐stationary signals. Moreover, the DLCT allows a parametric estimation of the instantaneous frequency of the signal that is robust to noise and to closeness or overlap in the instantaneous frequency of the modes. More importantly, the DLCT can be used to represent and process signals that are sparse in a joint time–frequency sense. The performance of the DLCT and the EMD are illustrated and compared when used to estimate the instantaneous frequency of individual signal components, to obtain signal decompositions at different frequency bands and to process frequency modulated signals with time‐varying amplitude.

In the new generation wireless communication systems where high data rates are desired, orthogonal frequency division multiplexing (OFDM) has become the standard method because of its advantages over single carrier modulation schemes on multi-path, frequency selective fading channels. However, inter-carrier interference due to Doppler frequency shifts, and multi-path fading severely degrades the performance of OFDM systems. Estimation of channel parameters is required at the receiver. In this paper, we present a time-varying channel modeling and estimation method based on the discrete evolutionary transform that provides a time-frequency procedure to obtain a complete characterization of a multi-path, fading and frequency selective channel. Performance of the proposed method is tested on different levels of channel noise, and Doppler frequency shifts.

In wireless communications, the channel is typically modeled as a random linear time-varying system that spreads the transmitted signal in both time and frequency due to multipath and Doppler effects. In this paper, we show how time-frequency analysis can be used to model and estimate the channel of a multi-carrier spread spectrum (MC-SS) system with a complex quadratic spreading sequence. We show that in this case the effects of time delays and Doppler frequency shifts can be characterized effectively as time-shifts. Using the discrete evolutionary transform (DET) we are able to estimate these effective time shifts via a spreading function and use them to equalize the channel. To illustrate the performance of the proposed method we perform several simulations with different levels of channel noise, jamming and Doppler frequency shifts.

Power spectral density of a signal is calculated from the second order statistics and provides valuable information for the characterization of stationary signals. This information is only sufficient for Gaussian and linear processes. Whereas, most real-life signals, such as biomedical, speech, and seismic signals may have non-Gaussian, non-linear and non-stationary properties. Higher order statistics (HOS) are useful for the analysis of such signals. Time-frequency (TF) analysis methods have been developed to analyze the time-varying properties of nonstationary signals. In this work, we combine the HOS and the TF approaches, and present a method for the calculation of a time-dependent bispectrum based on the positive distributed evolutionary spectrum.

In this work, we present a multiple window evolutionary spectral analysis on a non-rectangular time-frequency lattice based on a discrete fractional Gabor expansion. The traditional Gabor expansion uses a fixed, and rectangular time-frequency plane tiling. Many of the practical signals such as speech, music, etc., require a more flexible, nonrectangular time-frequency lattice for a compact representation. The proposed method uses a set of basis functions that are related to the fractional Fourier basis and generates a parallelogram-shaped tiling. Simulation results are given to illustrate the performance of our algorithm.

We present a fractional Gabor expansion on a general, non-rectangular time-frequency lattice. The traditional Gabor expansion represents a signal in terms of time- and frequency-shifted basis functions, called Gabor logons. This constant-bandwidth analysis results in a fixed, rectangular time frequency plane tiling. Many of the practical signals require a more flexible, non-rectangular time-frequency lattice for a compact representation. The proposed fractional Gabor expansion uses a set of basis functions that are related to the fractional Fourier basis and generate a non-rectangular tiling. The completeness and bi-orthogonality conditions of the new Gabor basis are discussed.

We propose a method - based on the discrete evolutionary transform (DET) - to estimate the instantaneous frequency of a signal embedded in noise or noise-like signals. The DET provides a representation for non-stationary signals and a time-frequency kernel that permit us to obtain the time-dependent spectrum of the signal. We show the instantaneous phase and the corresponding instantaneous frequency (IF) can also be computed from the evolutionary kernel. Estimation of instantaneous frequency is of general interest in time-frequency analysis, and of special interest in the excision of jammers in the direct sequence spread spectrum. Implementation of the IF estimation is done by masking and a recursive nonlinear correction procedure. The proposed estimation is valid for monocomponent as well as multicomponent signals in the noiseless and noisy situations. Its application to jammer excision in direct sequence spread spectrum communication is considered as an important application. The estimation procedure is illustrated with several examples.

In this paper, we present a Gabor representation based on a nonrectangular tiling of the time-frequency plane and use it to improve the time and frequency resolutions of evolutionary spectra. In the traditional Gabor expansion, a signal is decomposed into a weighted combination of sinusoidally modulated windows resulting in a rectangular time-frequency plane tiling. Poor time and frequency localizations occur in the evolutionary spectrum when the corresponding signal is not modeled well by this fixed-window analysis. We are thus proposing the warped Gabor representation based on a linear chirp model for the signal. By means of a frequency transformation we are able to use the previous sinusoidal representation and choose the Gabor coefficients according to either a frequency masking or an energy concentration measure. Examples are given to illustrate our procedures.

We present a connection between the discrete Gabor expansion and the evolutionary spectral theory. Including a scale parameter in the Gabor expansion, we obtain a new representation for deterministic signals that is analogous to the Wold-Cramer decomposition for non-stationary processes. The energy distribution resulting from the expansion is easily calculated from the Gabor coefficients. By choosing Gaussian windows and appropriate scales, the expansion can represent narrow-band and wide-band signals, as well as their combination. As an application, we consider the masking of signals in the time-frequency space and provide an approximate implementation using the new Gabor expansion. Examples illustrating the time-frequency analysis and the masking are given.

We propose an adaptive procedure to model non-stationary signals using autoregressive systems with time-varying parameters. A non-stationary signal that is representable by a time-varying autoregressive system has parameters which are expandable in terms of a set of basis functions. The parameters can be found by posing a minimum least-squares modeling problem and solving a large set of normal equations. The costly calculations involved in this problem make an adaptive solution quite desirable. Using the parameter expansions, we convert the modeling into a linear prediction problem and solve it adaptively for a given set of basis functions. We apply our procedure in the modeling of a segment of speech and in the estimation of the evolutionary spectrum of a non-stationary signal.>