Arrhythmia Detection Using Gated Recurrent Unit Network with ECG Signals

2020 ◽  
Vol 10 (3) ◽  
pp. 750-757
Author(s):  
Gang Xu ◽  
Guangxin Xing ◽  
Juanjuan Jiang ◽  
Jian Jiang ◽  
Yongsheng Ke
Keyword(s):  
Deep Learning ◽  
Visual Analysis ◽  
Classification Performance ◽  
Ecg Signal ◽  
Learning Networks ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Detection Model ◽  
High Degree ◽  
Gated Recurrent Unit

Background: Arrhythmia is a kind of heart disorder characterized by irregular heartbeats which can be detected with Electrocardiographic (ECG) signals. Accurate and early detection along with differentiation of arrhythmias is of great importance in a clinical setting. However, visual analysis of ECG signal is a challenging and timeconsuming work. We have developed an automatic arrhythmia detection model with deep learning framework to expedite the diagnosis of arrhythmia with a high degree of accuracy. Methods: We proposed a novel automatic arrhythmia detection model utilizing a combination of 1D convolutional neural network (1D-CNN) and Gated Recurrent Unit (GRU) network for the diagnosis of five different arrhythmia on ECG signals taken from the MITBIT arrhythmia physio bank database. Results: The proposed system showed a high classification performance in handling variable length ECG signal data, achieving an accuracy rate of 99.45%, sensitivity of 98.35% and specificity of 99.21% and a F1-Score of 98.95% using a five-fold cross validation strategy. Conclusions: Combining 1D-CNN and GRU networks yielded a higher degree of accuracy compared with other deep learning networks. Our proposed arrhythmia detection method may be a powerful tool to aid clinicians in accurately detecting common arrhythmias on routine ECG screening.

ECG Image Classification Using Deep Learning Approach

2021 ◽  
pp. 343-357
Author(s):  
Pratik Kanani ◽  
Mamta Chandraprakash Padole
Keyword(s):  
Deep Learning ◽  
Sampling Frequency ◽  
Heart Beat ◽  
Learning Approach ◽  
Recording Device ◽  
Noisy Signal ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Ecg Arrhythmia ◽  
Deep Learning Model

Cardiovascular diseases are a major cause of death worldwide. Cardiologists detect arrhythmias (i.e., abnormal heart beat) with the help of an ECG graph, which serves as an important tool to recognize and detect any erratic heart activity along with important insights like skipping a beat, a flutter in a wave, and a fast beat. The proposed methodology does ECG arrhythmias classification by CNN, trained on grayscale images of R-R interval of ECG signals. Outputs are strictly in the terms of a label that classify the beat as normal or abnormal with which abnormality. For training purpose, around one lakh ECG signals are plotted for different categories, and out of these signal images, noisy signal images are removed, then deep learning model is trained. An image-based classification is done which makes the ECG arrhythmia system independent of recording device types and sampling frequency. A novel idea is proposed that helps cardiologists worldwide, although a lot of improvements can be done which would foster a “wearable ECG Arrhythmia Detection device” and can be used by a common man.

scholarly journals End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2085 ◽  
Author(s):  
Rami M. Jomaa ◽  
Hassan Mathkour ◽  
Yakoub Bazi ◽  
Md Saiful Islam
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Attack Detection ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Biometric Systems ◽  
Fingerprint Biometrics ◽  
End To End ◽  
The Impact

Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

scholarly journals ECG signal classification based on Deep Learning by using Convolutional Neural Network (CNN)

2020 ◽  
Vol 3 (3) ◽  
pp. 12-23
Author(s):  
Aqeel M. Hamad alhussainy ◽  
Ammar D. Jasim
Keyword(s):  
Deep Learning ◽  
Classification System ◽  
World Health ◽  
Geometric Transformation ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Result Show ◽  
Automatic Methods ◽  
Health Organization

Cardiovascular diseases (CVDs) are consider  the main cause  of death today According to World Health Organization (WHO),and because that ECG signal is very important tool in monitoring and diagnosis of these disease , different automatic methods were proposed based on this signal. [1]. The manual analysis of ECG signals is suffered different challenges such as differeculty of detecting and classify waveform of this signal, So, many machine learning methods  are  explored to describe  the anomalies ECG signal accurately . Deep learning (DL) can be used in ECG classification, it can improve the quality of the automatic classification system. In this paper , we have proposed a deep learning classification system by using  different layers of convolution, rectifier and pooling operations  that can be used to increase feature extraction of ECG signal.        We have proposed two models, one is used for input signal of 1-D, in which we designed model for classification csv type of data for ECG signal, while in the second proposed system, we used model for 2-D signal after convert it from its csv type .  2-D signal (ECG image) is used in order to augment the two dimensional signal with different methods to increase the accuracy of the model by training it with geometric transformation of the original input images such as rotation, shearing etc.The results are compared with AlexNet and other  models  based on the metrics, which are    used to measure the performance of the proposed work, the result show that, the proposed models improve the efficiency of the classification  in the two systems.

scholarly journals An Optimized FPGA Based System Design for the Arrhythmia Detection using ECG

2019 ◽  
Vol 9 (1) ◽  
pp. 2808-2818
Keyword(s):  
System Design ◽  
Detection System ◽  
Ecg Signal ◽  
Noise Elimination ◽  
Is Success ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Reconstructed Signal ◽  
Signal Peak

Electrocardiogram signals are highly susceptible to interferences caused due to various kinds of noises including artefacts’, disruptions in power lines attained from the human interferences and device disturbances. These noise signals tend to lower the quality of signals that result in crucial environment for detecting and diagnosing different types of arrhythmia. In order to avoid this issue, multiple filtering techniques are being incorporated out of all Gaussian filters with Haar DWT portray better outcomes in noise elimination and smoothening of signal. The process of ECG signal filtering allows performing the testing and validation of in the actual world emulation. Enhancement in PSNR ratio is observed by using the ECG signal filters along the reconstructed signal. For a given input ECG signal, the levels of the signal peak decide if the patient is suffering from arrhythmia or not. If peak is low, patient is detected with the arrhythmia disease, if high patient is normal. The results can be observed in simulation. FPGA prototyping of the design is carried out along the hardware debugging in chip scope pro tool. The design is realized using Verilog coding with the technique of morphological filtering. For the purpose of debugging the hardware device used is Artix-7. The FPGA methodology is success full in a position to detect arrhythmia. The framework based on FPGA is structured and executed in the paper which can detect a type of arrhythmia which indicates Atrio Ventricular block along with all the noises removed. The simulation results are obtained by taking ECG signals from MIT-BIH arrhythmia database. The proposed FPGA based system design is proven to be optimized as it showed very less utilization of resources when compared to previous arrhythmia detection system designs.

scholarly journals Dissecting Deep Learning Networks—Visualizing Mutual Information

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 823 ◽  
Author(s):  
Hui Fang ◽  
Victoria Wang ◽  
Motonori Yamaguchi
Keyword(s):  
Deep Learning ◽  
Mutual Information ◽  
Network Performance ◽  
Classification Performance ◽  
Knowledge Gap ◽  
Accuracy Evaluation ◽  
Learning Networks ◽  
Theoretical Understanding ◽  
Effective Network ◽  
Visualization Techniques

Deep Learning (DL) networks are recent revolutionary developments in artificial intelligence research. Typical networks are stacked by groups of layers that are further composed of many convolutional kernels or neurons. In network design, many hyper-parameters need to be defined heuristically before training in order to achieve high cross-validation accuracies. However, accuracy evaluation from the output layer alone is not sufficient to specify the roles of the hidden units in associated networks. This results in a significant knowledge gap between DL’s wider applications and its limited theoretical understanding. To narrow the knowledge gap, our study explores visualization techniques to illustrate the mutual information (MI) in DL networks. The MI is a theoretical measurement, reflecting the relationship between two sets of random variables even if their relationship is highly non-linear and hidden in high-dimensional data. Our study aims to understand the roles of DL units in classification performance of the networks. Via a series of experiments using several popular DL networks, it shows that the visualization of MI and its change patterns between the input/output with the hidden layers and basic units can facilitate a better understanding of these DL units’ roles. Our investigation on network convergence suggests a more objective manner to potentially evaluate DL networks. Furthermore, the visualization provides a useful tool to gain insights into the network performance, and thus to potentially facilitate the design of better network architectures by identifying redundancy and less-effective network units.

Identification of pancreatic adenocarcinoma molecular subtypes on histology slides using deep learning models.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 4141-4141
Author(s):  
Charlie Saillard ◽  
Flore Delecourt ◽  
Benoit Schmauch ◽  
Olivier Moindrot ◽  
Magali Svrcek ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Pancreatic Adenocarcinoma ◽  
Validation Cohort ◽  
Molecular Subtypes ◽  
Routine Care ◽  
Classification Performance ◽  
Molecular Heterogeneity ◽  
Learning Models ◽  
Detection Model

4141 Background: Pancreatic adenocarcinoma (PAC) is predicted to be the second cause of death by cancer in 2030 and its prognosis has seen little improvement in the last decades. PAC is a very heterogeneous tumor with preeminent stroma and multiple histological aspects. Omic studies confirmed its molecular heterogeneity, possibly one of the main factors explaining the failure of most clinical trials. Two and three transcriptomic subtypes of tumor cells and stroma respectively, were described with major prognostic and predictive implications. The tumor subtypes, Basal-like and Classical, have been shown by several groups to be predictive of the response to first line chemotherapy. As of today, these subtypes can only be defined by RNA profiling which is limited by the quantity and quality of the samples (formalin fixation and low cellularity) as well as by the analytical delay that may restrict its application in routine care. In addition, tumors may harbor a mixture of several subtypes limiting their interpretation using bulk transcriptomic approaches and thereby their clinical use. Here, we propose a multistep approach using deep learning models to predict tumor components and their molecular subtypes on routine histological preparations. Methods: 728 whole-slide digitized histological slides corresponding to 350 consecutive resected PAC from four centers with clinical and transcriptomic data were assembled and used as a discovery set. PAC from TCGA (n = 134) was used as a validation set. Tumor regions from slides of the discovery set were annotated to train a multistep deep learning model that first recognizes tumor tissue and then predicts tumor and stroma cells molecular subtypes assessed by the published PurIST algorithm. Results: The tumor detection model was very efficient (AUC = 0.98 in the TCGA validation cohort). In the discovery set, the Basal-like/Classical classification performance of the model by cross validation was 0.79 (AUC) and reached 0.86 when restricted to samples with a high-confidence RNA-defined molecular subtype.Subtypes defined by the model were independently associated with overall survival in multivariate analysis (HR = 2.56 [1.87 - 3.49], pval < 0.001), and association was higher relatively to PurIST RNA subtypes (HR = 1.60 [1.17 - 2.19] pval < 0.001). In the validation cohort, the model had an overall AUC of 0.82, and 0.89 in the subset of “subtype-pure” tumors. In addition to demonstrating the value of histology-based deep learning models for tumor subtyping in PAC, these results also show the limit of molecular-based subtyping in highly heterogeneous samples. Conclusions: This study provides the first PAC subtyping tool usable worldwide in clinical practice, finally opening the possibility of patient molecular stratification in routine care and clinical trials.

Facemask Detection Based on Double Convolutional Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Guoqiang Chen ◽  
Bingxin Bai ◽  
Hongpeng Zhou ◽  
Mengchao Liu ◽  
Huailong Yi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Contextual Information ◽  
Detection Accuracy ◽  
Learning Networks ◽  
Position Information ◽  
Detection Model ◽  
Multi Stage

Background: The study on facemask detection is of great significance because facemask detection is difficult, and the workload is heavy in places with a large number of people during the COVID-19 outbreak. Objective: The study aims to explore new deep learning networks that can accurately detect facemasks and improve the network's ability to extract multi-level features and contextual information. In addition, the proposed network effectively avoids the interference of objects like masks. The new network could eventually detect masks wearers in the crowd. Method: A Multi-stage Feature Fusion Block (MFFB) and a Detector Cascade Block (DCB) are proposed and connected to the deep learning network for facemask detection. The network's ability to obtain information improves. The network proposed in the study is Double Convolutional Neural Networks (CNN) called DCNN, which can fuse mask features and face position information. During facemask detection, the network extracts the featural information of the object and then inputs it into the data fusion layer. Results: The experiment results show that the proposed network can detect masks and faces in a complex environment and dense crowd. The detection accuracy of the network improves effectively. At the same time, the real-time performance of the detection model is excellent. Conclusion: The two branch networks of the DCNN can effectively obtain the feature and position information of facemasks. The network overcomes the disadvantage that a single CNN is susceptible to the interference of the suspected mask objects. The verification shows that the MFFB and the DCB can improve the network's ability to obtain object information, and the proposed DCNN can achieve excellent detection performance.

scholarly journals A Deep Learning Approach for a Source Code Detection Model Using Self-Attention

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yao Meng ◽  
Long Liu
Keyword(s):  
Deep Learning ◽  
Source Code ◽  
Classification Performance ◽  
Detection Model ◽  
Learning Framework ◽  
Code Clone ◽  
Representation Model ◽  
Discriminative Model ◽  
Bidirectional Lstm ◽  
Lstm Network

With the development of deep learning, many approaches based on neural networks are proposed for code clone. In this paper, we propose a novel source code detection model At-biLSTM based on a bidirectional LSTM network with a self-attention layer. At-biLSTM is composed of a representation model and a discriminative model. The representation model firstly transforms the source code into an abstract syntactic tree and splits it into a sequence of statement trees; then, it encodes each of the statement trees with a deep-first traversal algorithm. Finally, the representation model encodes the sequence of statement vectors via a bidirectional LSTM network, which is a classical deep learning framework, with a self-attention layer and outputs a vector representing the given source code. The discriminative model identifies the code clone depending on the vectors generated by the presentation model. Our proposed model retains both the syntactics and semantics of the source code in the process of encoding, and the self-attention algorithm makes the classifier concentrate on the effect of key statements and improves the classification performance. The contrast experiments on the benchmarks OJClone and BigCloneBench indicate that At-LSTM is effective and outperforms the state-of-art approaches in source code clone detection.

Comparative Study of ECG Classification Performance Using Decision Tree Algorithms

2012 ◽  
Vol 3 (4) ◽  
pp. 102-120 ◽  
Author(s):  
Faiza Charfi ◽  
Ali Kraiem
Keyword(s):  
Decision Tree ◽  
Heart Diseases ◽  
Classification Performance ◽  
Primary Diagnosis ◽  
Health Condition ◽  
Ecg Signal ◽  
Chi Square ◽  
Ecg Signals ◽  
Tree Algorithms ◽  
And Control

The electrocardiogram (ECG) signal has often been reported to play an important role in the primary diagnosis, prognosis, and survival analysis of heart diseases. Electrocardiography has brought several valuable impacts on the practice of medicine. This paper deals with the feature extraction and automatic analysis of different ECG signal waves using derivative based/ Pan-Tompkins based algorithms. The ECG signal contains an important amount of information that can be exploited in different way. It allows for the analysis of cardiac health condition. The discrimination of ECG signals using the Data Mining Decision Tree techniques is of crucial importance in the cardiac disease therapy and control of cardiac arrhythmias. Different ECG signals from MIT/BIH Arrhythmia data base are used for ECG features extraction and analysis. Two pathologies are considered: atrial fibrillation and right bundle branch block. Some decision tree classification algorithms currently in use, including C4.5, Improved C4.5, CHAID (Chi square Automatic Interaction Detector) and Improved CHAID are performed for performance analysis. Promising results have been achieved using the C4.5 classifier, with an overall accuracy of 96.87%.

Sign in / Sign up

Export Citation Format

Share Document