Classification of Atrial Fibrillation with Pre-Trained Convolutional Neural Network Models

Qayyum, Abdul; Mériaudeau, Fabrice; Chan, Genevieve C. Y.

doi:10.1109/iecbes.2018.8626624

Cited by 23 publications

(10 citation statements)

References 27 publications

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“…Convolutional neural networks (CNNs) take place as the category of deep neural networks, in terms of searching application to present categorization of images and analysis [10]. The categorization and attribute extraction is given by end-to-end learning architecture of CNNs.…”

Section: Deep Approaches Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Convolutional neural networks based efficient approach for classification of lung diseases

Demir

Şengür

Bajaj

2019

Health Inf Sci Syst

130

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Treatment of lung diseases, which are the third most common cause of death in the world, is of great importance in the medical field. Many studies using lung sounds recorded with stethoscope have been conducted in the literature in order to diagnose the lung diseases with artificial intelligence-compatible devices and to assist the experts in their diagnosis. In this paper, ICBHI 2017 database which includes different sample frequencies, noise and background sounds was used for the classification of lung sounds. The lung sound signals were initially converted to spectrogram images by using time-frequency method. The short time Fourier transform (STFT) method was considered as time-frequency transformation. Two deep learning based approaches were used for lung sound classification. In the first approach, a pre-trained deep convolutional neural networks (CNN) model was used for feature extraction and a support vector machine (SVM) classifier was used in classification of the lung sounds. In the second approach, the pre-trained deep CNN model was fine-tuned (transfer learning) via spectrogram images for lung sound classification. The accuracies of the proposed methods were tested by using the ten-fold cross validation. The accuracies for the first and second proposed methods were 65.5% and 63.09%, respectively. The obtained accuracies were then compared with some of the existing results and it was seen that obtained scores were better than the other results.

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Section: Deep Approaches Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Convolutional neural networks based efficient approach for classification of lung diseases

Demir

Şengür

Bajaj

2019

Health Inf Sci Syst

130

View full text Add to dashboard Cite

show abstract

“…There are various developments in Telemedicine such as detection of glucose level and oxygen concentration; ECG interfaces to monitor the heart rates, etc. Leveraging the IoT in healthcare can have a significant impact on early diagnosis and intervention in some terminal illnesses, such as heart diseases, and decrease the mortality rate [8]. Eysenbach et al [9] indicated that the telemedicine helped reduce the number of in-hospital admissions of the patients with diseases related to lungs, heart and stroke; hence a considerable decrease in mortality [3].…”

Section: Introductionmentioning

confidence: 99%

Classification Techniques for Arrhythmia Patterns Using Convolutional Neural Networks and Internet of Things (IoT) Devices

2022

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“…Xia et al applied short-term Fourier transform (STFT) and stationary wavelet transform (SWT) to obtain the 2D matrix input suitable for deep 2D CNN models (Xia et al, 2018). Qayyum et al converted ECG signals into 2D images by STFT, and used pre-trained CNN models for transfer learning (Qayyum et al, 2018). Lorenz plot imaging of ECG RR intervals was also used as input images to training a 2D CNN based model for AF classification (Hayano et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Premature Beats Rejection Strategy on Paroxysmal Atrial Fibrillation Detection

Zhang

Cai

et al. 2022

Front. Physiol.

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Paroxysmal atrial fibrillation (PAF) may related to the risk of thromboembolism and is the most common cardiac risk factor of cryptogenic stroke (CS). Due to its paroxysmal characteristics, it is usually diagnosed by continuous long-term ECG. Patients with paroxysmal atrial fibrillation usually have premature beats at the same time which is easy to be confused with the rhythm of atrial fibrillation. Therefore, in this article, we designed a screening algorithm for single premature beat, multi premature beats, bigeminy and trigeminy premature beats, according to their rhythm characteristics to reduce false detection caused by premature beats during the PAF detection process. The proposed elimination method was verified on ECG segments with different types of premature beats, and tested on long-term ECG data of PAF patients. ECG segments of different kinds of premature beats were selected from MIT Atrial Fibrillation database (MIT-AFDB), MIT-BIH Arrhythmia database (MIT-AR) and wearable ECG data from the China Physiological Signal Challenge 2021 (CPSC 2021). The proposed method can effectively eliminate single premature beat segments with 99.5% accuracy, and it also can eliminate more than 95% of ECG segments with other types of premature beats. We designed PAF-score as a new index to evaluate the accuracy of detection, and we also calculate the misjudged and missed segments to comprehensively evaluate the PAF detection algorithm. The proposed method get a PAF-score of 0.912 on MIT-AFDB. The proposed method also has the potential to implant low computing power wearable devices for real-time analysis.

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Classification of Atrial Fibrillation with Pre-Trained Convolutional Neural Network Models

Cited by 23 publications

References 27 publications

Convolutional neural networks based efficient approach for classification of lung diseases

Convolutional neural networks based efficient approach for classification of lung diseases

Classification Techniques for Arrhythmia Patterns Using Convolutional Neural Networks and Internet of Things (IoT) Devices

Premature Beats Rejection Strategy on Paroxysmal Atrial Fibrillation Detection

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