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    Arrhythmia diagnosis from ECG signal pulses with one-dimensional convolutional neural networks
    (Elsevier, 2023) Senturk, Umit; Polat, Kemal; Yucedag, Ibrahim; Alenezi, Fayadh
    A noninvasive technique, electrocardiography (ECG), is crucial in the detection and treatment of cardiovascular disorders. Periodic beats make up ECG signals, and these beats vary based on the internal dynamics of the cardiovascular system. It is highly challenging to categorize ECG beats that can be understood by specialists in the area. In recent years, attempts have been made to use artificial intelligence programs in conjunction with a database of specified ECG beats to identify autonomous cardiovascular illness. In this investigation, the PTB Diagnostic ECG Database and the MIT-BIH Arrhythmia Database were used to attempt to classify arrhythmias. A one-dimensional convolutional neural network (CNN, or ConvNet) model was used to estimate the arrhythmia classes. The ECG beats defined in the database are divided into five classes: normal (N), supraventricular premature (S), premature ventricular contraction (V), ventricular and normal fusion (F), and Unclassifiable beats (U). The utilized one-dimensional convolutional neural network (1D-CNN VGG16) model’s average accuracy in classifying arrhythmias was found to be 99.12%. With the aid of this study, a system of experts has been built to assist specialized doctors in the healthcare system. The high estimating success of the used model will help in combining the right diagnosis with the right therapy and saving lives. © 2023 Elsevier Inc. All rights reserved.
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    A novel hybrid model in the diagnosis and classification of Alzheimer's disease using EEG signals: Deep ensemble learning (DEL) approach
    (Elsevier Sci Ltd, 2024) Nour, Majid; Senturk, Umit; Polat, Kemal
    Recent years have witnessed a surge of sophisticated computer-aided diagnosis techniques involving Artificial Intelligence (AI) to accurately diagnose and classify Alzheimer's disease (AD) and other forms of Dementia. Despite these advancements, there is still a lack of reliable and accurate methods for distinguishing between (AD) and Healthy Controls (HC) using Electroencephalography signals (EEG). The main challenge is finding the right features from the intricate spectral-temporal EEG data, which can provide information sufficient for diagnosis. This study proposes a new approach integrating Deep Ensemble Learning (DEL) and 2-dimensional Convolutional Neural Networks (2D-CNN) to address these issues. Combining state-of-the-art supervised deep learning algorithms within an ensemble model architecture aims to accurately diagnose and classify EEG signals of AD and HC subjects. Public EEG-based Alzheimer's datasets have been classified in the DEL model without applying any feature extraction after cleaning from noise and artifacts. Furthermore, the proposed DEL model used 5 different 2D-CNN models as internal classifiers. As a result, the EEG-based DEL model proposed for the first time provided high accuracy in AD classification. The proposed DEL model reached an average accuracy of 97.9% in AD classification due to 5 cross-fold training. In conclusion, this work renders that incorporating ensemble learning techniques into automotive health applications create extensible and stable AI models needed for computer-aided diagnostic. However, although the reported results and evaluation are promising, further efforts will need to be made to improve the accuracy of our proposed model. In addition, a fine-grid evaluation will be necessary to accurately understand potential impacts in clinical applications, such as earlier diagnosis or treatment decisions.