Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals

Acharya, U. Rajendra; Fujita, Hamido; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Tan, Ru San

doi:10.1007/s10489-018-1179-1

Cited by 213 publications

(105 citation statements)

References 23 publications

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“…Their system achieved and accuracy of 80.5%. Deep Convolutional Neural Network (CNN) was used extensively for ECG analysis [45,46,47,48,49]. Deep Artificial Neural Network (ANN) have be used for multiple heath care applications [50,51,52].…”

Section: Discussionmentioning

confidence: 99%

Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

Faust

Shenfield

Kareem

et al. 2018

Computers in Biology and Medicine

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245

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Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective treatment to prevent stroke. Long term cardiac monitoring improves the likelihood of diagnosing paroxysmal AF. We used a deep learning system to detect AF beats in Heart Rate (HR) signals. The data was partitioned with a sliding window of 100 beats. The resulting signal blocks were directly fed into a deep Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The system was validated and tested with data from the MIT-BIH Atrial Fibrillation Database. It achieved 98.51% accuracy with 10-fold cross-validation (20 subjects) and 99.77% with blindfold validation (3 subjects). The proposed system structure is straight forward, because there is no need for information reduction through feature extraction. All the complexity resides in the deep learning system, which gets the entire information from a signal block. This setup leads to the robust performance for unknown data, as measured with the blind fold validation. The proposed Computer-Aided Diagnosis (CAD) system can be used for long-term monitoring of the human heart. To the best of our knowledge, the proposed system is the first to incorporate deep learning for AF beat detection.

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Section: Discussionmentioning

confidence: 99%

Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

Faust

Shenfield

Kareem

et al. 2018

Computers in Biology and Medicine

Self Cite

245

131

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“…Electrocardiogram (ECG) is a key component of the clinical diagnosis and management of inpatients and outpatients that can provide important information about cardiac diseases [1]. Some cardiac diseases can be recognized only through an ECG signal as has been presented in [2][3][4][5][6]. ECG…”

Section: Introductionmentioning

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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“…Without detecting the QRS they achieved comparable performance with previous state-of-the-art methods that were based on R-peak detection and feature engineering. The same authors have also trained the previous CNN architecture for identifying shockable and non-shockable ventricular arrhythmias [104], identify CAD patients with FAN and INDB [105], classify CHF with CHFDB, NSTDB, FAN [106] and also tested its noise resistance with WT denoising [107].…”

Section: A Electrocardiogrammentioning

confidence: 99%

Deep Learning in Cardiology

Bizopoulos

Koutsouris

2019

IEEE Rev. Biomed. Eng.

142

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The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

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Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals

Cited by 213 publications

References 23 publications

Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

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

Deep Learning in Cardiology

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