Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

Yıldırım, Özal; Talo, Muhammed; Ciaccio, Edward J.; Tan, Ru San; Acharya, U. Rajendra

doi:10.1016/j.cmpb.2020.105740

Cited by 94 publications

(56 citation statements)

References 78 publications

(144 reference statements)

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“…In order to make a fair benchmarking, we compare our proposed model with several previous studies. These studies focus on the ECG signal classification using DL architecture, especially the use of 1D-CNN architecture ( Yıldırım, Pławiak & Rajendra Acharya, 2018 ; Rajkumar, Ganesan & Lavanya, 2019 ; Nannavecchia et al, 2021 ), LSTM architecture ( Yildirim et al, 2019 ; Gao et al, 2019 ), and combination architecture of 1D-CNN as a feature extraction and LSTM as a classifier ( Lui & Chow, 2018 ; Oh et al, 2018 ; Yildirim et al, 2020 ; Chen et al, 2020 ; Luo et al, 2021 ). However, all the classification methodologies are developed by treating beat and rhythm separately.…”

Section: Resultsmentioning

confidence: 99%

“…From these two studies, the classification performance is improved; however, the number of classes are reduced from 17 to 5-class. Other study for ECG signal classification based on beat feature utilize combination between 1D convolutional layers and LSTM architecture with 10.000 subject and seven-class abnormalities ( Yildirim et al, 2020 ). By using the proposed model, the classification accuracy around 92.24%, unfortunately, the sensitivity only reaches 80.15%.…”

Section: Resultsmentioning

confidence: 99%

“…Combination of 1D Convolutional layers with other DL architecture can actually produce quite impressive results ( Lui & Chow, 2018 ; Oh et al, 2018 ; Chen et al, 2020 ; Yildirim et al, 2020 ; Luo et al, 2021 ) rivalling a model which uses individual 1D-CNN and LSTM architecture ( Yıldırım, Pławiak & Rajendra Acharya, 2018 ; Gao et al, 2019 ; Rajkumar, Ganesan & Lavanya, 2019 ; Nannavecchia et al, 2021 ). Even in other study, in order to obtain satisfactory results in ECG signal classification, three different DL architecture, 1D-CNN, LSTM and GRU are combined ( Luo et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

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Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification

Darmawahyuni

Nurmaini

Rachmatullah

et al. 2022

PeerJ Computer Science

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Background Electrocardiogram (ECG) signal classification plays a critical role in the automatic diagnosis of heart abnormalities. While most ECG signal patterns cannot be recognized by a human interpreter, they can be detected with precision using artificial intelligence approaches, making the ECG a powerful non-invasive biomarker. However, performing rapid and accurate ECG signal classification is difficult due to the low amplitude, complexity, and non-linearity. The widely-available deep learning (DL) method we propose has presented an opportunity to substantially improve the accuracy of automated ECG classification analysis using rhythm or beat features. Unfortunately, a comprehensive and general evaluation of the specific DL architecture for ECG analysis across a wide variety of rhythm and beat features has not been previously reported. Some previous studies have been concerned with detecting ECG class abnormalities only through rhythm or beat features separately. Methods This study proposes a single architecture based on the DL method with one-dimensional convolutional neural network (1D-CNN) architecture, to automatically classify 24 patterns of ECG signals through both rhythm and beat. To validate the proposed model, five databases which consisted of nine-class of ECG-base rhythm and 15-class of ECG-based beat were used in this study. The proposed DL network was applied and studied with varying datasets with different frequency samplings in intra and inter-patient scheme. Results Using a 10-fold cross-validation scheme, the performance results had an accuracy of 99.98%, a sensitivity of 99.90%, a specificity of 99.89%, a precision of 99.90%, and an F1-score of 99.99% for ECG rhythm classification. Additionally, for ECG beat classification, the model obtained an accuracy of 99.87%, a sensitivity of 96.97%, a specificity of 99.89%, a precision of 92.23%, and an F1-score of 94.39%. In conclusion, this study provides clinicians with an advanced methodology for detecting and discriminating heart abnormalities between different ECG rhythm and beat assessments by using one outstanding proposed DL architecture.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification

Darmawahyuni

Nurmaini

Rachmatullah

et al. 2022

PeerJ Computer Science

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“…In this work, we have used 20 channel EEG signals with 7339 EEG signals of 10 s duration in each channel. For this big dataset, many deep learning models have been used [36][37][38][39].…”

Section: Motivation and Our Modelmentioning

confidence: 99%

Automated major depressive disorder detection using melamine pattern with EEG signals

et al. 2021

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Major depressive disorder (MDD) is one of the most common modern ailments affected huge population throughout the world. The electroencephalogram (EEG) signal is widely used to screen the MDD. The manual diagnosis of MDD using EEG is time consuming, subjective and may cause human errors. Therefore, nowadays various automated systems have been developed to diagnose MDD accurately and rapidly. In this work, we have proposed a novel automated MDD detection system using EEG signals. Our proposed model has three steps: (i) Melamine pattern and discrete wavelet transform (DWT)-based multileveled feature generation, (ii) selection of most relevant features using neighborhood component analysis (NCA) and (iii) classification using support vector machine (SVM) and k nearest neighbor (kNN) classifiers. The novelty of this work is the application of melamine pattern. The molecular structure of melamine (also named chemistry spider-ChemSpider) is used to generate 1536 features. Also, various statistical features are extracted from DWT coefficients. The NCA is used to select the most relevant features and these selected features are classified using SVM and kNN classifiers. The presented model attained greater than 95% accuracies using all channels with quadratic SVM classifier. Our results obtained highest classification accuracy of 99.11% and 99.05% using Weighted kNN and Quadratic SVM respectively using A2A1 EEG channel. We have developed the automated depression model using a big dataset and yielded high classification accuracies. These results indicate that our presented model can be used in mental health clinics to confirm the manual diagnosis of psychiatrists.

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“…The ECG waveforms record a combination of electrical activity from various cardiac cells; a typical waveform consists of three phases: P-wave, QRS-complex, and T-wave. Much attention has been paid to the classification of diseases and localization of cardiac sources with ECG waveform (e.g., [ 2 , 3 , 4 , 5 ]). By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease can be noninvasively identified.…”

Section: Introductionmentioning

confidence: 99%

ECG Localization Method Based on Volume Conductor Model and Kalman Filtering

Nakano

Rashed

Nakane

et al. 2021

Sensors

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The 12-lead electrocardiogram was invented more than 100 years ago and is still used as an essential tool in the early detection of heart disease. By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease can be noninvasively identified. However, most previous studies are based on signal processing, and thus an approach that includes physics modeling would be helpful for source localization problems. This study proposes a localization method for cardiac sources by combining an electrical analysis with a volume conductor model of the human body as a forward problem and a sparse reconstruction method as an inverse problem. Our formulation estimates not only the current source location but also the current direction. For a 12-lead electrocardiogram system, a sensitivity analysis of the localization to cardiac volume, tilted angle, and model inhomogeneity was evaluated. Finally, the estimated source location is corrected by Kalman filter, considering the estimated electrocardiogram source as time-sequence data. For a high signal-to-noise ratio (greater than 20 dB), the dominant error sources were the model inhomogeneity, which is mainly attributable to the high conductivity of the blood in the heart. The average localization error of the electric dipole sources in the heart was 12.6 mm, which is comparable to that in previous studies, where a less detailed anatomical structure was considered. A time-series source localization with Kalman filtering indicated that source mislocalization could be compensated, suggesting the effectiveness of the source estimation using the current direction and location simultaneously. For the electrocardiogram R-wave, the mean distance error was reduced to less than 7.3 mm using the proposed method. Considering the physical properties of the human body with Kalman filtering enables highly accurate estimation of the cardiac electric signal source location and direction. This proposal is also applicable to electrode configuration, such as ECG sensing systems.

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Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

Cited by 94 publications

References 78 publications

Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification

Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification

Automated major depressive disorder detection using melamine pattern with EEG signals

ECG Localization Method Based on Volume Conductor Model and Kalman Filtering

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