Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network

Acharya, U. Rajendra; Fujita, Hamido; Lih, Oh Shu; Adam, Muhammad; Tan, Jen Hong; Chua, Chua Kuang

doi:10.1016/j.knosys.2017.06.003

Cited by 307 publications

(121 citation statements)

References 24 publications

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“…Moreover, conventional techniques require features to be extracted manually. In deep learning, both the feature extraction and classification processes are conducted automatically [28,29] unlike the traditional machine learning techniques. Amongst others, CNN is the most prevalent type of deep learning network that has been exploited by researchers to identify abnormal EEG signals [30] and to study these signals to diagnose disorders such as depression [31], seizure [32], attention deficit hyperactivity disorder [33] and autism [34].…”

Section: Deep Learningmentioning

confidence: 99%

Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

et al. 2019

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A computerized detection system for the diagnosis of Schizophrenia (SZ) using a convolutional neural system is described in this study. Schizophrenia is an anomaly in the brain characterized by behavioral symptoms such as hallucinations and disorganized speech. Electroencephalograms (EEG) indicate brain disorders and are prominently used to study brain diseases. We collected EEG signals from 14 healthy subjects and 14 SZ patients and developed an eleven-layered convolutional neural network (CNN) model to analyze the signals. Conventional machine learning techniques are often laborious and subject to intra-observer variability. Deep learning algorithms that have the ability to automatically extract significant features and classify them are thus employed in this study. Features are extracted automatically at the convolution stage, with the most significant features extracted at the max-pooling stage, and the fully connected layer is utilized to classify the signals. The proposed model generated classification accuracies of 98.07% and 81.26% for non-subject based testing and subject based testing, respectively. The developed model can likely aid clinicians as a diagnostic tool to detect early stages of SZ.

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Section: Deep Learningmentioning

confidence: 99%

Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

et al. 2019

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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.

140

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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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“…Their report indicated that the machine-learned algorithm showed a high sensitivity of 92% (95% CI: 74%-100%). Electrocardiogram (ECG) is an important method that can be used for detection of CAD [23,24,25]. Giri et al [23] used Discrete Wavelet Transform (DWT) approach in order to decompose the heart rate signals into frequency sub-bands.…”

Section: Related Workmentioning

confidence: 99%

Performance Improvement of Decision Trees for Diagnosis of Coronary Artery Disease Using Multi Filtering Approach

Abdar

Nasarian

Zhou

et al. 2019

2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS)

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The heart is one of the strongest muscular organs in the human body. Every year, this disease can kill many people in the world. Coronary artery disease (CAD) is named as the most common type of heart disease. Four well-known decision trees (DTs) are applied on the Z-Alizadeh Sani CAD dataset, which consists of J48, BF tree, REP tree, and NB tree. A multi filtering approach, named MFA, was used to modify the weight of attributes to improve the performance of DTs in this study. The model was applied on three main coronary arteries including the Left Anterior Descending (LAD), Left Circumflex (LCX), and Right Coronary Artery (RCA). The obtained results show that data balancing has a valuable impact on the performance of DTs. The comparison results show that this study provides the best results applied on the Z-Alizadeh Sani dataset compared to previous studies. The proposed MFA could improve the performance of the classic DTs algorithms significantly, with the highest accuracies obtained by NB tree for LAD, LCX, and RCA are 94.90%, 92.97% and 93.43%, respectively.

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Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network

Cited by 307 publications

References 24 publications

Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

Deep Learning in Cardiology

Performance Improvement of Decision Trees for Diagnosis of Coronary Artery Disease Using Multi Filtering Approach

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