Automatic Arrhythmia Detection Using One-Dimensional Convolutional Neural Network

Liu, Zhong; Wang, Xinan

doi:10.21203/rs.3.rs-51189/v1

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…To the best of our knowledge, our transfer learning model performance (AUC = 0.906) is the highest compared to other studies using transfer learning and Kaggle datasets (31)(32) as shown in Table-2. Our AUC performance using the Kaggle dataset was also higher compared to performance metrics on the ADNI dataset, likely due to the fact that the Kaggle dataset had 2.5 times the number of images as the ADNI dataset.…”

Section: Discussionmentioning

confidence: 70%

A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging

Ganesh,

Vanamu

2022

J Emerg Invest

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Since there is no cure for Alzheimer’s disease (AD), early detection is essential to mitigate the symptoms. However, early detection is extremely challenging because brain MRI scans during these initial stages look very similar to normal MRI scans to the human eye. To assist in early disease detection, we developed a convolution neural network deep learning model using a transfer learning approach to extract features and a custom dense layer to detect and classify the early stages of AD. We collected MRI scans from Kaggle and ADNI institutes at several stages of AD (mild, moderate, severe) as well as healthy control scans. We used Resnet-50, VGG-16, and DenseNet-169 base models and compared their performances for classifying the stages of AD. The test area under the curve (AUC) for each of the base models Resnet-50, VGG-16 and DenseNet-169 were 0.8334, 0.9047 and 0.8898, respectively, based on 1279 Kaggle MRI images and 0.7332, 0.7383 and 0.7133, respectively, based on 132 ADNI MRI images. VGG-16 outperformed both DenseNet-169 and Resnet-50 models and showed more accurate results. Our deep learning approach also detected and extracted the region of interest (ROI) as the superior frontal gyrus (SFG) and the hippocampus for the Kaggle and ADNI brain axial MRI images, respectively.

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Section: Discussionmentioning

confidence: 70%

A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging

Ganesh,

Vanamu

2022

J Emerg Invest

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“…Many articles present the utilization of a CNN to classify arrhythmias, although none of them includes a class for individuals suffering from ARVC. An 1D‐CNN is utilized to classify normal and abnormal ECG heartbeats into three classes, 28,29 while in References 30,31 the classification concerns one and two additional types of arrhythmias respectively. Other articles highlight the usage of 2D‐CNNs for multiclass heartbeat classification, for example, Reference 32 proposes a 11‐layer 2D‐CNN to classify ECGs belonging to MIT‐BIH arrhythmia database into normal, supraventricular, ventricular, fusion and unknown class with 98.3% accuracy, while a 5 class classification of arrhythmias based on a 2D‐CNN is also presented in References 33,34.…”

Section: Introductionmentioning

confidence: 99%

Analysis of digitalized ECG signals based on artificial intelligence and spectral analysis methods specialized in ARVC

Papageorgiou

Zegkos

Efthimiadis

et al. 2022

Numer Methods Biomed Eng

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Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that appears between the second and forth decade of a patient's life, being responsible for 20% of sudden cardiac deaths before the age of 35. The effective and punctual diagnosis of this disease based on electrocardiograms (ECGs) could have a vital role in reducing premature cardiovascular mortality. In our analysis, we first outline the digitalization process of paperbased ECG signals enhanced by a spatial filter aiming to eliminate dark regions in the dataset's images that do not correspond to ECG waveform, producing undesirable noise. Next, we propose the utilization of a low-complexity convolutional neural network for the detection of an arrhythmogenic heart disease, that has not been studied through the usage of deep learning methodology to date, achieving high classification accuracy, namely 99.98% training and 98.6% testing accuracy, on a disease the major identification criterion of which are infinitesimal millivolt variations in the ECG's morphology, in contrast with other arrhythmogenic abnormalities. Finally, by performing spectral analysis we investigate significant differentiations in the field of frequencies between normal ECGs and ECGs corresponding to patients suffering from ARVC. In 16 out of the 18 frequencies where we encounter statistically significant differentiations, the normal ECGs are characterized by greater normalized amplitudes compared to the abnormal ones. The overall research carried out in this article highlights the importance of integrating mathematical methods into the examination and effective diagnosis of various diseases, aiming to a substantial contribution to their successful treatment.

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Automatic Arrhythmia Detection Using One-Dimensional Convolutional Neural Network

Cited by 2 publications

References 16 publications

A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging

A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging

Analysis of digitalized ECG signals based on artificial intelligence and spectral analysis methods specialized in ARVC

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