Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks

Kıranyaz, Serkan; İnce, Türker; Gabbouj, Moncef

doi:10.1109/tbme.2015.2468589

Cited by 1,470 publications

(810 citation statements)

References 31 publications

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“…ECG) have just started to emerge in the literature. For example, deep neural networks have been used in ECG anomaly detection on Physionet databases [4,5]. Recently, Rajpurkar et al [6] proposed a much deeper network, which discriminated 12 types of heart conditions, normal rhythm and noisy recordings.…”

Section: Introductionmentioning

confidence: 99%

Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG

Andreotti

Carr

Pimentel

et al. 2017

Computing in Cardiology Conference (CinC)

111

113

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The diagnosis of cardiovascular diseases such as atrial fibrillation (AF) is a lengthy and expensive procedure that often requires visual inspection of ECG signals by experts. In order to improve patient management and reduce healthcare costs, automated detection of these pathologies is of utmost importance.In this study, we classify short segments of ECG into four classes (AF, normal, other rhythms or noise) as part of the Physionet/Computing in Cardiology Challenge 2017. We compare a state-of-the-art feature-based classifier with a convolutional neural network approach. Both methods were trained using the challenge data, supplemented with an additional database derived from Physionet.The feature-based classifier obtained an F 1 score of 72.0% on the training set (5-fold cross-validation), and 79% on the hidden test set. Similarly, the convolutional neural network scored 72.1% on the augmented database and 83% on the test set. The latter method resulted on a final score of 79% at the competition. Developed routines and pre-trained models are freely available under a GNU GPLv3 license.

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Section: Introductionmentioning

confidence: 99%

Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG

Andreotti

Carr

Pimentel

et al. 2017

Computing in Cardiology Conference (CinC)

111

113

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“…The proposed method does not require any form of transformation, feature extraction, and postprocessing. The feature extraction and feature-based classification phases of the bearing fault detection could be combined into a single learning body with 1D CNNs [32,33]. It can directly work over the raw data, that is, the motor current signal, to detect the anomalies.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Detection by One‐Dimensional Convolutional Neural Networks

Eren

2017

Mathematical Problems in Engineering

187

106

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Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in realtime applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy.

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“…Popular applications of these techniques are image recognition and generation, and speech and timeseries classification [78,79]. In Kiranyaz et al [10], a 1D CNN merged feature extraction and classification in one step to develop a personalized patient classifier that can be used in real time, once trained, to classify longer recordings, on wearable devices for instance. They reached an accuracy of 98.6% (95% sensitivity, 98.1% specificity) on 24 test recordings of the MIT-BIH database in classifying ventricular and supraventricular ectopic beats.…”

Section: Real-time Diagnosismentioning

confidence: 99%

“…Other studies focus on patient classification [7][8][9], based on the overall behaviour of the ECG, to diagnose specific diseases. In addition, with the development of wearable devices and the need for real-time diagnosis, other challenges such as speed or memory requirements have emerged, requiring the adaptation of these methods for quick classification [10][11][12]. Analysing the ECG with machine learning methods is a promising approach but dealing with medical data for clinical applications raises some additional challenges, such as the lack of databases available for validation and the need to interpret ECG abnormalities at the organ and cellular level.…”

Section: Introductionmentioning

confidence: 99%

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Lyon

Mincholé

Martínez

et al. 2018

J. R. Soc. Interface.

175

102

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Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.

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Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks

Abstract: Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.

Cited by 1,470 publications

References 31 publications

Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG

Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG

Bearing Fault Detection by One‐Dimensional Convolutional Neural Networks

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

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