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.

In this paper, we present a memristor emulator based on voltage difference transconductance amplifier (VDTA). The proposed memristor emulator circuit contains only one VDTA as an active element and single grounded capacitor which benefits from the integrated circuit. Furthermore, it can be utilized MOS-capacitance instead of the external capacitor in the circuit. The complete memristor emulator is laid by using Cadence Environment using TSMC 0.18 μm process parameters. It occupies an area of 35.7 μm × 29 μm. Its simulation results are given to demonstrate the performance of the presented memristor emulator in different operating frequencies, process corner, and radical temperature changes. Moreover, prototype circuit is implemented to confirm the theoretical analysis by employing the single LM13700 commercial device as an active element. Experimental results of the designed memristor emulator are given to investigate its ability for different operating frequencies, the capacitance value and resistor value and DC supply voltage. The experimental results are in accordance with theoretical analyses and simulation results.