Classification of ECG Arrhythmia using Recurrent Neural Networks

Singh, Shraddha; Pandey, Saroj Kumar; Pawar, Urja; Janghel, Rekh Ram

doi:10.1016/j.procs.2018.05.045

Cited by 222 publications

(73 citation statements)

References 19 publications

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“…There are also many solutions that use RNN architectures for cardiac arrhythmia detection. Normal and abnormal beats were classified by implementing LSTM and GRU architectures [200], aiming to enable the automatic separation of regular and irregular beats on the MIT-BIH Arrhythmia Database, and comparing different RNN models in order to show their effectiveness, accuracy, and capabilities. Mixed architectures have also been proposed.…”

Section: Recent Interest In Deep Learning For Ecgmentioning

confidence: 99%

“…Whereas it is undoubtedly a probably mandatory scenario to take advantage from currently available large-data technologies, it is very likely that several words of caution should be observed. On the one hand, many (not all) the works rely on existing ECG databases, many of them available to the public thanks to the work made by the ECG signal processing community during the last several years [181,200,203]. However, the apparent diversity and large amount of data in terms of available beats in these data could be not enough for driving the training of DL based systems, due to points like the number of patients in these databases (several thousands of beats per patient is not necessarily a sign of large training sets, due to the intra-patient redundancy), or the unbalance among the actually needed classes in the clinical setting.…”

Section: Open Issues In Deep Learning For Ecgmentioning

confidence: 99%

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Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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Section: Recent Interest In Deep Learning For Ecgmentioning

confidence: 99%

Section: Open Issues In Deep Learning For Ecgmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

View full text Add to dashboard Cite

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“…The Gated Recurrent Unit (GRU) architecture consists of two gates: reset gate and update gate [31]. Basically, these are two vectors which decide what information should be passed to the output.…”

Section: Gated Recurrent Unitmentioning

confidence: 99%

Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier

et al. 2019

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The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals' morphological view show significant variation in different patients under different physical conditions. Several learning algorithms have been studied to interpret MI. However, the drawback of machine learning is the use of heuristic features with shallow feature learning architectures. To overcome this problem, a deep learning approach is used for learning features automatically, without conventional handcrafted features. This paper presents sequence modeling based on deep learning with recurrent network for ECG-rhythm signal classification. The recurrent network architecture such as a Recurrent Neural Network (RNN) is proposed to automatically interpret MI via ECG signal. The performance of the proposed method is compared to the other recurrent network classifiers such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The objective is to obtain the best sequence model for ECG signal processing. This paper also aims to study a proper data partitioning ratio for the training and testing sets of imbalanced data. The large imbalanced data are obtained from MI and healthy control of PhysioNet: The PTB Diagnostic ECG Database 15-lead ECG signals. According to the comparison result, the LSTM architecture shows better performance than standard RNN and GRU architecture with identical hyper-parameters. The LSTM architecture also shows better classification compared to standard recurrent networks and GRU with sensitivity, specificity, precision, F1-score, BACC, and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56%, and 95.32%, respectively. Apparently, deep learning with the LSTM technique is a potential method for classifying sequential data that implements time steps in the ECG signal.Algorithms 2019, 12, 118 2 of 12 records electrical signals related to heart activity and producing a voltage-chart cardiac rate and being a cardiological test that has been used in the past 100 years [7]. ECG signals have three different waveforms for each cardiac cycle: P wave, QRS complex, and T wave in normal rate [8]. In other cases, ECG form changes in the T waveform, the ST interval length, and ST elevation. Its morphology causes a cardiac abnormality, i.e., Ischemic Heart Disease (IHD) [9]. The IHD is the single largest cause of the main contributors to the disease burden in developing countries [10]. The two leading manifestations of IHD are angina and Acute Myocardial Infarction (MI) [10]. Angina is the characteristic caused by atherosclerosis leading to stenosis of one or more coronary arteries. Then, MI occurs due to a lack of oxygen demand in the cardiac muscle tissue. If cardiac muscle activity increases, oxygen demand also increases [11]. MI is the most dangerous form of IHD with the highest mortality rate [10].MI is usually diagnosed by changes in the ECG due to the increase of serum enzymes, such as creatine phosphokinase and troponin T or I [10]. ECG is the most reliable tool for interpreting ...

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“…34 Over the last few years, the ANNs also have increasingly been used to classify biosignals, such as heart disease problems. [35][36][37] An MLP is a feed-forward ANN model that can be used to map input data sets into a set of appropriate outputs. MLP passes input to output through one or more connected hidden layers.…”

Section: Classification Modulementioning

confidence: 99%

Versatile implementation of a hardware–software architecture for development and testing of brain–computer interfaces

Martinez-Ledezma

Barrón-Zambrano

Diaz-Manríquez

et al. 2020

International Journal of Advanced Robotic Systems

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Brain–computer interfaces (BCI) have been focused on improving people’s lifestyles with motor or communication disabilities. However, the utilization of this technology has found news applications, such as increasing human capacities. Nowadays, several researchers are working on probing human capabilities to control several robotic devices simultaneously. The design of BCI is an intricate work that needs a long time to its implementation. For this reason, an architecture to design and implement different types of BCIs is presented in this article. The architecture has a modular design capable of reading various electroencephalography (EEG) sensors and controlling several robotic devices similar to the plug-and-play paradigm. To test the proposed architecture, a BCI was able to manage a hexapod robot and a drone was implemented. Firstly, a mobile robotic platform was designed and implemented. The BCI is based on eye blinking, where a single blinking represents a robot command. The command orders the robot to initiate or stops their locomotion for the hexapod robot. For the drone, a blink represents the takeoff or landing order. The blinking signals are obtained from the prefrontal and frontal regions of the head by EEG sensors. The signals are then filtered using temporal filters, with cutoff frequencies based on delta, theta, alpha, and beta waves. The filtered signals were labeled and used to train a classifier based on the multilayer perceptron (MLP) model. To generate the robot command, the proposal BCI used two models of MLP to ensure the classifier prediction. So, when the two classifiers make the same prediction, within a defined time interval, send the signal to the robot to start or stop its movement. The obtained results show that it is possible to get high precision to control the hexapod robot with a precision of 91.7% and an average of 81.4%.

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Classification of ECG Arrhythmia using Recurrent Neural Networks

Cited by 222 publications

References 19 publications

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier

Versatile implementation of a hardware–software architecture for development and testing of brain–computer interfaces

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