Heartbeat Classification Using 1D Convolutional Neural Networks

Shaker, Abdelrahman; Tantawi, Manal M.; Shedeed, Howida A.; Tolba, Mohamed F.

doi:10.1007/978-3-030-31129-2_46

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…In a deep hierarchical structure, the learned features tend to become more abstract as the network gets deeper [38]. Convolutional neural networks (CNNs), including hidden layers of convolutional filters with pretrained weights can be run in real-time, thus being feasible for different ECG monitoring applications, such as denoising [39,40], QRS detection [41,42], ECG segmentation [43], heartbeat classification [44][45][46][47][48], and arrhythmia classification with different output diagnosis labels (normal rhythm, atrial fibrillation, other rhythm, noise [49][50][51][52]; normal rhythm, atrial fibrillation, atrial flutter, ventricular fibrillation [53,54]). While the above studies use DNN architectures with 3 to 11 hidden layers, a recent study of Hannun et al (2019) [55] has demonstrated that an end-to-end 34-layer DNN can classify a broad range of 12 distinct arrhythmias with high diagnostic performance similar to that of cardiologists.…”

Section: Introductionmentioning

confidence: 99%

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Krasteva

Ménétré

Didon

et al. 2020

Sensors

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Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocardiogram (ECG) and can generally provide high-output diagnostic accuracy if subjected to robust training and optimization on large datasets at high computational cost. So far, limited research and optimization of DNNs in shock advisory systems is found on large ECG arrhythmia databases from out-of-hospital cardiac arrests (OHCA). The objective of this study is to optimize the hyperparameters (HPs) of deep convolutional neural networks (CNN) for detection of shockable (Sh) and nonshockable (NSh) rhythms, and to validate the best HP settings for short and long analysis durations (2–10 s). Large numbers of (Sh + NSh) ECG samples were used for training (720 + 3170) and validation (739 + 5921) from Holters and defibrillators in OHCA. An end-to-end deep CNN architecture was implemented with one-lead raw ECG input layer (5 s, 125 Hz, 2.5 uV/LSB), configurable number of 5 to 23 hidden layers and output layer with diagnostic probability p ∈ [0: Sh,1: NSh]. The hidden layers contain N convolutional blocks × 3 layers (Conv1D (filters = Fi, kernel size = Ki), max-pooling (pool size = 2), dropout (rate = 0.3)), one global max-pooling and one dense layer. Random search optimization of HPs = {N, Fi, Ki}, i = 1, … N in a large grid of N = [1, 2, … 7], Fi = [5;50], Ki = [5;100] was performed. During training, the model with maximal balanced accuracy BAC = (Sensitivity + Specificity)/2 over 400 epochs was stored. The optimization principle is based on finding the common HPs space of a few top-ranked models and prediction of a robust HP setting by their median value. The optimal models for 1–7 CNN layers were trained with different learning rates LR = [10−5; 10−2] and the best model was finally validated on 2–10 s analysis durations. A number of 4216 random search models were trained. The optimal models with more than three convolutional layers did not exhibit substantial differences in performance BAC = (99.31–99.5%). Among them, the best model was found with {N = 5, Fi = {20, 15, 15, 10, 5}, Ki = {10, 10, 10, 10, 10}, 7521 trainable parameters} with maximal validation performance for 5-s analysis (BAC = 99.5%, Se = 99.6%, Sp = 99.4%) and tolerable drop in performance (<2% points) for very short 2-s analysis (BAC = 98.2%, Se = 97.6%, Sp = 98.7%). DNN application in future-generation shock advisory systems can improve the detection performance of Sh and NSh rhythms and can considerably shorten the analysis duration complying with resuscitation guidelines for minimal hands-off pauses.

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Section: Introductionmentioning

confidence: 99%

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Krasteva

Ménétré

Didon

et al. 2020

Sensors

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“…Since the experience and knowledge of each doctor is different, differences can be shown in deciding the disease. For this purpose, it is important to develop decision support systems in terms of helping doctors in the diagnosis and treatment of the disease (Shaker et al, 2019;Virgeniya et al, 2020;Sanderson et al, 2020;Adem et al, 2019;Adem, 2018). In this study, it was aimed to classify the COVID-19 disease with CNN, which is one of the deep learning methods, by using the data set consisting of blood analysis values.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Diagnosis Prediction in Emergency Care Patients using the Convolutional Neural Network

Adem

Kılıçarslan

2021

Afyon Kocatepe University Journal of Sciences and Engineering

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The sudden increase in cases of Coronavirus disease (COVID-19) puts a high pressure on health care providers in many countries across the world. In the present case, an early and correct diagnosis of the disease, and starting the treatment is of vital importance. Most of the developing countries have insufficient RT-PCR tests, the most verified diagnostic test for COVID-19. This increases the number of infected patients and delays preventive measures. In this study, the risk of a positive COVID-19 diagnosis is estimated by applying Convolutional Neural Network (CNN) method, which is a deep learning model, to the dataset obtained from routine blood tests of all patients who admitted to the emergency service. The dataset used in the experiments consists of the data from patients admitted to the Israelita Albert Einstein Hospital in São Paulo, Brazil, between March 28th and April 3rd, 2020. In addition to the J48, ANN, Random Forest, and Random Committee data mining algorithms, the CNN deep learning algorithm were applied to the dataset. The 5 and 7 fold cross validation model was applied to the data set and the average of the two models was used as an evaluation criterion in order to ensure objectivity. The best prediction performance was obtained by the CNN method by 92.52% accuracy. Experimental results revealed that the proposed approach is in line with the results of the tests with general validity.

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Deep Convolutional Neural Networks for ECG Heartbeat Classification Using Two-Stage Hierarchical Method

Shaker

Tantawi

Shedeed

et al. 2020

Advances in Intelligent Systems and Computing

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Heartbeat Classification Using 1D Convolutional Neural Networks

Cited by 7 publications

References 22 publications

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

COVID-19 Diagnosis Prediction in Emergency Care Patients using the Convolutional Neural Network

Deep Convolutional Neural Networks for ECG Heartbeat Classification Using Two-Stage Hierarchical Method

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