Electrocardiogam Research

Based on The Indonesia Sample Registration System Survey (SRSS), among all types of Non-Communicable Disease (NCD), cardiovascular disease (CVD) was the highest cause of death at 12.9 percent at all ages (Riskesdas, 2013). Unhealthy lifestyle behavior is also a factor in the increasing prevalence of CVD. The standard of modernization refers to the lifestyle of urban communities. The thinner boundary between urban and rural areas causes a lifestyle in a rural similar to that of an urban area. So that, in handling the CVD in rural and urban, there is no significant difference. However, infrastructure development in rural areas is still slow, so access to health services is still uneven and limited (Riskesdas, 2013). Otherwise, socio-economic problems and the level of knowledge of rural communities about health are relatively lower than urban communities.

Nowadays, the use of electronic technologies for patient health medical information has produced a large amount of data from various populations, cell types, and disorders. Due to the increasing amount of data along with time and energy limitation of clinical experts in analyzing large amounts of data, a system is needed to help this process. Artificial Intelligence (AI) can be a significant element for developing a rapidly system towards CVD. AI has also been recognized as an initial step toward precision medicine. Based on AI approachment, it can be used to produce models or concepts from integration and analysis among data so that the information provided is more accurate and can help clinical experts in making decisions for CVD interpretation.

Among the patient health medical information related to CVD, electrocardiogram (ECG) is a key component of clinical diagnosis and management of inpatients and outpatients that can provide important information about the cardiac disease (Goldberger et al., 2017). Some cardiac disease can be recognized only through an ECG. ECG 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 (Khan, 2008).

To minimize the limited access to rural health care in handling cardiac abnormalities, a cheap ECG simulation system is needed as a simple test tool to measure and record the electrical activity of cardiac. A cheap ECG simulation system will be the initial detector for classifying heart disease, such as cardiomyopathy, heart failure, myocardial infarction, myocarditis, bundle branch block, dysrhythmia, etc. The interpretation of CVD from ECG is a difficult task. It takes an experienced cardiologist to be able to interpret the condition of the human cardiac. Therefore, an ECG simulation system for classifying cardiac abnormalities is made based on Artificial Intelligence (AI).

Figure 1. ECG Processing

The Intelligent Systems Research Group (ISysRG) has published some articles that implemented ECG for diagnosing some cardiac abnormalities using the Physionet dataset (https://physionet.org/). The PhysioNet Resource’s original and ongoing missions were to conduct and catalyze for biomedical research and education.

The lists of the published articles in the past two years (2019 – 2020):

  1. Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier (link: https://www.mdpi.com/1999-4893/12/6/118)
    Publisher: Algorithm, MDPI, 2019

    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.

  2. Deep classifier on the electrocardiogram interpretation system (link: https://iopscience.iop.org/article/10.1088/1742-6596/1246/1/012030)
    Publisher: IOP, 2019

    Electrocardiogram (ECG) is a primary diagnostic tool for cardiovascular diseases. A higher accuracy of heart diseases needs an automatic classification for intelligent interpretation of cardiac arrhythmia. The classification process consists of following stages: detection of QRS complex in ECG signal, feature extraction from detected QRS using R-R interval, segmentation of rhythms using extracted feature set, learning system by using Deep Neural Networks (DNNs). The performance is analyzed as a rhythm of arrhythmia classifier and MIT-BIH arrhythmia database uses to validate the method. To benchmark, the performance of DNNs algorithm is compared to, MLP and SVM algorithm in terms of accuracy. The result obtained show that the proposed method provides good accuracy about 97.7 % with less expert interaction.

  3. An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique (link: https://www.mdpi.com/2076-3417/9/14/2921)
    Publisher: Applied Science, MDPI, 2019

    An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required human interference and expertise in determining robust features, yet was time-consuming in the labeling and data processing steps. In contrast, DL enables an embedded feature extraction and feature selection in DAEs pre-training and DNNs fine-tuning process directly from raw data. Hence, DAEs is able to extract high-level of features not only from the training data but also from unseen data. The proposed model uses 10 classes of imbalanced data from ECG signals. Since it is related to the cardiac region, abnormality is usually considered for an early diagnosis of CD. In order to validate the result, the proposed model is compared with the shallow models and DL approaches. Results found that the proposed method achieved a promising performance with 99.73% accuracy, 91.20% sensitivity, 93.60% precision, 99.80% specificity, and a 91.80% F1-Score. Moreover, both the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve from the confusion matrix showed that the developed model is a good classifier. The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage.

  4. Deep Learning with Long Short-Term Memory for Enhancement Myocardial Infarction Classification (link: https://ieeexplore.ieee.org/abstract/document/8916683/)
    Publisher: IEEE, 2019 (2019 6th International Conference on Instrumentation, Control, and Automation (ICA))

    Myocardial infarction (MI) may be a minor event in a type of chronic disease, even undetectable. However, it can also be a major disaster that causes sudden death. The multivariance in ECG signals for different patients causes the interpretation of existence MI is a difficult task. The various conventional method is proposed to diagnose MI of ECG signals. The conventional classifier algorithm uses a shallow feature learning architecture based on the hand-crafted feature. This paper is only a preliminary study so that this paper contains only brief analysis and plan. However, it can present other point-of-view to process cardiac rhythm that associated in timesteps based on deep learning approach. Basically, a shallow feature learns as well as deep learning. However, the advantage and characteristics of deep learning will make classifier learn automatically without having to involve human intervention. Long short-term memory (LSTM) as deep learning classifier is proposed to the binary classification of MI and healthy control patients. The public ECG signals dataset of Physionet is used to support our research. In the evaluation of binary classification, balanced accuracy (BAcc) and Matthew’s Correlation Coefficient (MCC) metrics are used to analyze imbalance sequential data of 4.57 Imbalance Ratio (IR). The overall, 3 hidden LSTM layers as classifier show good performance in imbalanced data to classify MI with precision, sensitivity, F1 score, BAcc, and MCC is 0.91, 0.91, 0.90, 0.83 and 0.75 respectively.

  5. Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification (link: https://www.mdpi.com/2079-9292/9/1/135)
    Publisher: Electronics, MDPI, 2019

    The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due to varying types of artifacts and interference. To address this problem, some previous studies propose a computerized technique based on machine learning (ML) to distinguish between normal and abnormal heartbeats. Unfortunately, ML works on a handcrafted, feature-based approach and lacks feature representation. To overcome such drawbacks, deep learning (DL) is proposed in the pre-training and fine-tuning phases to produce an automated feature representation for multi-class classification of arrhythmia conditions. In the pre-training phase, stacked denoising autoencoders (DAEs) and autoencoders (AEs) are used for feature learning; in the fine-tuning phase, deep neural networks (DNNs) are implemented as a classifier. To the best of our knowledge, this research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL. Physionet’s well-known MIT-BIH Arrhythmia Database, as well as the MIT-BIH Noise Stress Test Database (NSTDB). Only four records are used from the NSTDB dataset: 118 24 dB, 118 −6 dB, 119 24 dB, and 119 −6 dB, with two levels of signal-to-noise ratio (SNRs) at 24 dB and −6 dB. In the validation process, six models are compared to select the best DL model. For all fine-tuned hyperparameters, the best model of ECG heartbeat classification achieves an accuracy, sensitivity, specificity, precision, and F1-score of 99.34%, 93.83%, 99.57%, 89.81%, and 91.44%, respectively. As the results demonstrate, the proposed DL model can extract high-level features not only from the training data but also from unseen data. Such a model has good application prospects in clinical practice.

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