Classification of Premature Ventricular Contraction Using Deep Learning

Marco, Fabiola De; Finlay, Dewar; Bond, Raymond

doi:10.22489/cinc.2020.311

Cited by 15 publications

(4 citation statements)

References 9 publications

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“…By definition they do not have a common pattern that allows them to be identified uniquely, but the morphology is very often very similar to other arrhythmias and in some cases, it can be almost imperceptible. For this reason, classifiers very often confuse non-PVC QRS complexes from PVC QRS complexes [19]. False predictions have caught the interest of cardiologists who are always on the lookout for systems that can accurately detect arrhythmias and heart problems.…”

Section: Plos Onementioning

confidence: 99%

Classification of QRS complexes to detect Premature Ventricular Contraction using machine learning techniques

et al. 2022

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Detection of Premature Ventricular Contractions (PVC) is of crucial importance in the cardiology field, not only to improve the health system but also to reduce the workload of experts who analyze electrocardiograms (ECG) manually. PVC is a non-harmful common occurrence represented by extra heartbeats, whose diagnosis is not always easily identifiable, especially when done by long-term manual ECG analysis. In some cases, it may lead to disastrous consequences when associated with other pathologies. This work introduces an approach to identify PVCs using machine learning techniques without feature extraction and cross-validation techniques. In particular, a group of six classifiers has been used: Decision Tree, Random Forest, Long-Short Term Memory (LSTM), Bidirectional LSTM, ResNet-18, MobileNetv2, and ShuffleNet. Two types of experiments have been performed on data extracted from the MIT-BIH Arrhythmia database: (i) the original dataset and (ii) the balanced dataset. MobileNetv2 came in first in both experiments with high performance and promising results for PVCs’ final diagnosis. The final results showed 99.90% of accuracy in the first experiment and 99.00% in the second one, despite no feature detection techniques were used. The approach we used, which was focused on classification without using feature extraction and cross-validation techniques, allowed us to provide excellent performance and obtain better results. Finally, this research defines as first step toward understanding the explanations for deep learning models’ incorrect classifications.

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Section: Plos Onementioning

confidence: 99%

Classification of QRS complexes to detect Premature Ventricular Contraction using machine learning techniques

et al. 2022

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“…The proposed user identification system was implemented using a shallow 1D CNN model. The 1D CNN model is a representative model for learning one-dimensional data; time-series data and is widely used to process sequential data, including one-dimensional signals such as in natural language processing, time-series analysis, speech recognition, and arrhythmia classification [30] [31].…”

Section: Implementation Of a User Identification System Using Ecg Sig...mentioning

confidence: 99%

One-Dimensional Shallow Neural Network Using Non-Fiducial Based Segmented Electrocardiogram for User Identification System

Kim,

Choi,

Choi

2023

IEEE Access

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Recent user recognition technologies have focused on biometric signals to remotely identify users in the access management, medical welfare, and public sectors. The electrocardiogram (ECG) signal is an individual's unique electrophysiological signal generated within the body and is difficult to forge or change; thus, it can be used to uniquely identify the user. Existing user identification systems based on ECG signals detect R peaks according to morphological features and use a fiducial-based segmentation process for data normalization. However, this process does not detect a distinct R peak due to motion artifacts generated by the movement of the subject, thereby degrading identification accuracy. To address the problem of decreasing peak accuracy of the fiducial-based segmentation data, this study proposes a user identification system based on one-dimensional neural networks using periodic non-fiducial-based segmentation data that do not overlap in the time domain. The proposed system comprises a preprocessing step for data denoising, a non-fiducial-based and non-overlap segmentation step, and a step for classifying users using a one-dimensional shallow neural network. In the experiment, when using self-acquired 10-second ECG signal data that were divided into non-fiducial and non-overlapping segments, the long short-term memory (LSTM), bidirectional LSTM, and one-dimensional convolutional neural network (CNN) models achieved accuracies of 92.01%, 93.13%, and 95.51%, respectively, with the 1D CNN model exhibiting the best accuracy. The proposed system demonstrated a 25.48% improvement in accuracy on non-fiducial segmented data compared to the 1D CNN model, which achieved an accuracy of 70.03% on fiducial segmented data. Moreover, when tested on publicly available ECG datasets, including MIT-BIH, ECG-ID, and PTB-XL, the proposed system exhibited a user identification accuracy of over 94%, confirming its superior performance.

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“…With the development of artificial intelligence methods such as machine learning and deep learning, it is now feasible to assist clinicians with a wide range of activities. In order to extract relevant data for digital health, these cutting-edge technologies are increasingly applied to biomedical challenges [14,15], such as proteomics [16,17], genetics and image and signal data classification [18,19], and visualization [20]. Additionally, the Internet of Medical Things (IoMT), a subset of the Internet of Things (IoT) dedicated to the connectivity of all medical equipment, expands as more medical devices are connected [21].…”

Section: Why Cad For the Melanoma Detectionmentioning

confidence: 99%

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the Clinical Images Melanoma Detection Problem

Biasi

Marco

Citarella

et al. 2022

Preprint

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Background: Melanoma is one of the deadliest tumors in the world. Early detection is critical for first-line therapy in this tumor pathology and it remains challenging due to the need for histological analysis to ensure correctness in diagnosis. Therefore, multiple computer-aided diagnosis (CAD) systems working on melanoma images were proposed to mitigate the need of a biopsy. However, although the high global accuracy is declared in literature results, the CAD systems for the health fields must focus on the lowest false negative rate (FNR) possible to qualify as a diagnosis support system. The final goal must be to avoid classification type 2 errors to prevent life-threatening situations. Another goal could be to create an easy-to-use system for both physicians and patients. Results: To achieve the minimization of type 2 error, we performed a wide exploratory analysis of the principal convolutional neural network (CNN) architectures published for the multiple image classification problem; we adapted these networks to the melanoma clinical image binary classification problem (MCIBCP). We collected and analyzed performance data to identify the best CNN architecture, in terms of FNR, usable for solving the MCIBCP problem. Then, to provide a starting point for an easy-to-use CAD system, we used a clinical image dataset (MED-NODE) because clinical images are easier to access: they can be taken by a smartphone or other hand-size devices. Despite the lower resolution than dermoscopic images, the results in the literature would suggest that it would be possible to achieve high classification performance by using clinical images. In this work, we used MED-NODE, which consists of 170 clinical images (70 images of melanoma and 100 images of naevi). We optimized the following CNNs for the MCIBCP problem: Alexnet, DenseNet, GoogleNet Inception V3, GoogleNet, MobileNet, ShuffleNet, SqueezeNet, and VGG16. Conclusions: The results suggest that a CNN built on the VGG or AlexNet structure can ensure the lowest FNR (0.07) and (0.13), respectively. In both cases, discrete global performance is ensured. 73% (accuracy), 82% (sensitivity) and 59% (specificity) for VGG; 89% (accuracy), 87% (sensitivity) and 90% (specificity) for AlexNet.

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Classification of Premature Ventricular Contraction Using Deep Learning

Cited by 15 publications

References 9 publications

Classification of QRS complexes to detect Premature Ventricular Contraction using machine learning techniques

Classification of QRS complexes to detect Premature Ventricular Contraction using machine learning techniques

One-Dimensional Shallow Neural Network Using Non-Fiducial Based Segmented Electrocardiogram for User Identification System

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the Clinical Images Melanoma Detection Problem

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