Displaying publications 1 - 20 of 208 in total

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  1. Nayan NA, Ab Hamid H, Suboh MZ, Abdullah N, Jaafar R, Mhd Yusof NA, et al.
    DOI: 10.3991/ijoe.v16i07.13569
    Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, knearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.
    Keywords—CVD, ECG, machine learning, The Malaysian Cohort, RMSSD
    Study name: The Malaysian Cohort (TMC) project
    Matched MeSH terms: Electrocardiography
  2. Ghaleb FA, Kamat MB, Salleh M, Rohani MF, Abd Razak S
    PLoS One, 2018;13(11):e0207176.
    PMID: 30457996 DOI: 10.1371/journal.pone.0207176
    The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects.
    Matched MeSH terms: Electrocardiography
  3. Dharmalingam TK, Nor Azian AZ, Thiruselvi S, Abdul Aziz J
    Med J Malaysia, 2013 Apr;68(2):177-8.
    PMID: 23629572
    Left bundle branch block (LBBB) during anaesthesia is uncommon. During general anaesthesia, LBBB may be related to hypertension or tachycardia and its acute onset makes the diagnosis of acute myocardial ischemia or infarction difficult. We would like to present a case report of a healthy patient who developed LBBB intra operatively. Acute LBBB should lead to suspicion of acute coronary syndrome until proven otherwise. Inability to exclude an acute cardiac event resulted in postponement of surgery twice after general anaesthesia was administered. Cardiological investigation of our patient showed physiological left ventricular hypertrophy (LVH), "athlete's heart" which was the most likely cause of the LBBB under anaesthesia.
    Matched MeSH terms: Electrocardiography
  4. Oemar, Hamed, Abdulgani, Hafil Budianto
    Medical Health Reviews, 2008;2008(1):17-28.
    MyJurnal
    Heart failure (HF) is a major burden in almost all countries. The prevalence of symptomatic HF is still high. Despite our best understanding of its pathophysiologic mechanisms and the recent advances in pharmacologic therapy, it remains a highmortality and morbidity disease. About 30-50% of patients with HF have concurrent electrical delay in the electrocardiogram (ECG), mainly in the form of LBBB.1 This kind of conduction delay commonly occurs in patients with idiopathic dilated cardiomyopathy and ischemic cardiomyopathy as well. The abnormality of left ventricle (LV) conduction will lead to a change in LV contraction pattern resulting dyssynchronized with right ventricle) contraction. Thus, a dyssynchronous LV contractile pattern usually manifested by late activation of the LV lateral wall which in turn impairs LV systolic function, reduces cardiac output, raises filling pressure and worsens mitral regurgitation2. Cardiac resynchronization therapy (CRT) improves cardiac function and exercise capacity leading to an improved survival in patients with advanced heart failure and ventricular conduction delay.3 The underlying mechanisms of these beneficial effects are not fully understood, but they appear to be related to a restored coordination of the left (LV) and right ventricular (RV) contraction and relaxation.4 These effects may directly lead to augmented contractility and reduction of LV filling pressures.5 Echocardiography has been widely used to identify patients who are candidates for CRT and to monitor the response in LV function at follow-up after device implantation. This review addresses the applications of CRT in patients with moderate– severe heart failure and the role of echocardiography in optimizing CRT including patient selection, risk and benefit of CRT and appropriate measures.
    Matched MeSH terms: Electrocardiography
  5. Loong CW, Firdaus MAM, Said MRM, Abidin IZ
    Medeni Med J, 2020;35(3):266-270.
    PMID: 33110680 DOI: 10.5222/MMJ.2020.47374
    Takotsubo syndrome is a rare disease and remained ambiguous with its etiology. The disease manifests in various clinical characteristics and even mimicking acute coronary syndrome. We are reporting a case of an elderly lady who manifested by unsteady gaits and recurrent falls. Subacute infarct of the right corona radiata was observed on the brain CT. In addition, coronary angiography and ventriculogram were performed because of dynamic ECG changes and elevated troponin I levels. The angiogram was normal while ventriculogram showed apical ballooning. She was diagnosed and treated as Takotsubo syndrome.
    Matched MeSH terms: Electrocardiography
  6. Wong A, Abu Bakar MZ
    Am J Otolaryngol, 2021 01 04;42(2):102869.
    PMID: 33429183 DOI: 10.1016/j.amjoto.2020.102869
    PURPOSE: The nasocardiac reflex is known but not well researched. We aimed to ascertain the electrocardiographic features of the reflex and to chronologically map the heart rhythm dynamics during nasoendoscopy. We also intended to identify variables that could potentially affect the occurrence of this reflex.

    MATERIAL AND METHODS: A prospective, quasi-experimental physiological study. Selected healthy subjects were observed electrocardiographically for 60 s continuously in three equal phases of 20 s each - baseline phase, nasoendoscopic phase, and recovery phase (post-nasoendoscopy). Heart rate fluctuations were charted, followed by identification of a positive nasocardiac reflex group of subjects and a negative group. Analyses against multiple variables were done.

    RESULTS: A total of 53 subjects were analysed. Heart rate during the baseline phase was 81.0 ± 9.9, nasoendoscopic phase was 72.7 ± 10.1, and recovery phase was 75.2 ± 9.6. Sixteen subjects (30.2%) had a positive nasocardiac reflex, and they remained in sinus rhythm with no occurrences of skipped beats, atrioventricular blocks or asystoles. One subject (1.9%) developed temporary ectopic premature ventricular contractions after nasoendoscopy. No variables were found affecting the incidence of a nasocardiac reflex in our study.

    CONCLUSIONS: The pattern of heart rate dynamics was consistent as heart rates drop rapidly upon endoscope insertion and recover in some measure after its withdrawal. Although all our subjects remained asymptomatic, clinicians should not overlook the risks of a severe nasocardiac reflex when performing nasoendoscopy. We recommend that electrical cardiac monitoring be part of the management of vasovagal responses during in-office endonasal procedures.

    Matched MeSH terms: Electrocardiography*
  7. Oh SL, Ng EYK, Tan RS, Acharya UR
    Comput Biol Med, 2019 Feb;105:92-101.
    PMID: 30599317 DOI: 10.1016/j.compbiomed.2018.12.012
    Abnormality of the cardiac conduction system can induce arrhythmia - abnormal heart rhythm - that can frequently lead to other cardiac diseases and complications, and are sometimes life-threatening. These conduction system perturbations can manifest as morphological changes on the surface electrocardiographic (ECG) signal. Assessment of these morphological changes can be challenging and time-consuming, as ECG signal features are often low in amplitude and subtle. The main aim of this study is to develop an automated computer aided diagnostic (CAD) system that can expedite the process of arrhythmia diagnosis, as an aid to clinicians to provide appropriate and timely intervention to patients. We propose an autoencoder of ECG signals that can diagnose normal sinus beats, atrial premature beats (APB), premature ventricular contractions (PVC), left bundle branch block (LBBB) and right bundle branch block (RBBB). Apart from the first, the rest are morphological beat-to-beat elements that characterize and constitute complex arrhythmia. The novelty of this work lies in how we modified the U-net model to perform beat-wise analysis on heterogeneously segmented ECGs of variable lengths derived from the MIT-BIH arrhythmia database. The proposed system has demonstrated self-learning ability in generating class activations maps, and these generated maps faithfully reflect the cardiac conditions in each ECG cardiac cycle. It has attained a high classification accuracy of 97.32% in diagnosing cardiac conditions, and 99.3% for R peak detection using a ten-fold cross validation strategy. Our developed model can help physicians to screen ECG accurately, potentially resulting in timely intervention of patients with arrhythmia.
    Matched MeSH terms: Electrocardiography
  8. Yildirim O, Talo M, Ay B, Baloglu UB, Aydin G, Acharya UR
    Comput Biol Med, 2019 10;113:103387.
    PMID: 31421276 DOI: 10.1016/j.compbiomed.2019.103387
    In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data. Recent progress in deep learning has contributed significantly to improvement in the quality of healthcare. In order for deep learning models to perform well, large datasets are required for training. However, a difficulty in the biomedical field is the lack of clinical data with expert annotation. A recent, commonly implemented technique to train deep learning models using small datasets is to transfer the weighting, developed from a large dataset, to the current model. This deep learning transfer strategy is generally employed for two-dimensional signals. Herein, the weighting of models pre-trained using two-dimensional large image data was applied to one-dimensional HR signals. The one-dimensional HR signals were then converted into frequency spectrum images, which were utilized for application to well-known pre-trained models, specifically: AlexNet, VggNet, ResNet, and DenseNet. The DenseNet pre-trained model yielded the highest classification average accuracy of 97.62%, and sensitivity of 100%, to detect DM subjects via HR signal recordings. In the future, we intend to further test this developed model by utilizing additional data along with cloud-based storage to diagnose DM via heart signal analysis.
    Matched MeSH terms: Electrocardiography*
  9. Viswabhargav CSS, Tripathy RK, Acharya UR
    Comput Biol Med, 2019 05;108:20-30.
    PMID: 31003176 DOI: 10.1016/j.compbiomed.2019.03.016
    Sleep is a prominent physiological activity in our daily life. Sleep apnea is the category of sleep disorder during which the breathing of the person diminishes causing the alternation in the upper airway resistance. The electrocardiogram derived respiration (EDR) and heart rate (RR-time-series) signals are normally used for the detection of sleep apnea as these two signals capture cardio-pulmonary activity information. Hence, the analysis of these two signals provides vital information about sleep apnea. In this paper, we propose the novel sparse residual entropy (SRE) features for the automated detection of sleep apnea using EDR and heart rate signals. The features required for the automated detection of sleep apnea are extracted in three steps: (i) atomic decomposition based residual estimation from both EDR and heart rate signals using orthogonal matching pursuit (OMP) with different dictionaries, (ii) estimation of probabilities from each sparse residual, and (iii) calculation of the entropy features. The proposed SRE features are fed to the combination of fuzzy K-means clustering and support vector machine (SVM) to pick the best performing classifier. The experimental results demonstrate that the proposed SRE features with radial basis function (RBF) kernel-based SVM classifier yielded higher performance with accuracy, sensitivity and specificity values of 78.07%, 78.01%, and 78.13%, respectively with Fourier dictionary and 10-fold cross-validation. For subject-specific or leave-one-out validation case, the SVM classifier has sensitivity and specificity of 85.43% and 92.60%, respectively using SRE features with Fourier dictionary (FD).
    Matched MeSH terms: Electrocardiography*
  10. Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR
    Comput Methods Programs Biomed, 2018 Jul;161:1-13.
    PMID: 29852952 DOI: 10.1016/j.cmpb.2018.04.005
    BACKGROUND AND OBJECTIVE: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017.

    METHODS: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review.

    RESULTS: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input.

    CONCLUSIONS: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.

    Matched MeSH terms: Electrocardiography
  11. Adam M, Oh SL, Sudarshan VK, Koh JE, Hagiwara Y, Tan JH, et al.
    Comput Methods Programs Biomed, 2018 Jul;161:133-143.
    PMID: 29852956 DOI: 10.1016/j.cmpb.2018.04.018
    Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.
    Matched MeSH terms: Electrocardiography*
  12. Sharma M, Agarwal S, Acharya UR
    Comput Biol Med, 2018 09 01;100:100-113.
    PMID: 29990643 DOI: 10.1016/j.compbiomed.2018.06.011
    Obstructive sleep apnea (OSA) is a sleep disorder caused due to interruption of breathing resulting in insufficient oxygen to the human body and brain. If the OSA is detected and treated at an early stage the possibility of severe health impairment can be mitigated. Therefore, an accurate automated OSA detection system is indispensable. Generally, OSA based computer-aided diagnosis (CAD) system employs multi-channel, multi-signal physiological signals. However, there is a great need for single-channel bio-signal based low-power, a portable OSA-CAD system which can be used at home. In this study, we propose single-channel electrocardiogram (ECG) based OSA-CAD system using a new class of optimal biorthogonal antisymmetric wavelet filter bank (BAWFB). In this class of filter bank, all filters are of even length. The filter bank design problem is transformed into a constrained optimization problem wherein the objective is to minimize either frequency-spread for the given time-spread or time-spread for the given frequency-spread. The optimization problem is formulated as a semi-definite programming (SDP) problem. In the SDP problem, the objective function (time-spread or frequency-spread), constraints of perfect reconstruction (PR) and zero moment (ZM) are incorporated in their time domain matrix formulations. The global solution for SDP is obtained using interior point algorithm. The newly designed BAWFB is used for the classification of OSA using ECG signals taken from the physionet's Apnea-ECG database. The ECG segments of 1 min duration are decomposed into six wavelet subbands (WSBs) by employing the proposed BAWFB. Then, the fuzzy entropy (FE) and log-energy (LE) features are computed from all six WSBs. The FE and LE features are classified into normal and OSA groups using least squares support vector machine (LS-SVM) with 35-fold cross-validation strategy. The proposed OSA detection model achieved the average classification accuracy, sensitivity, specificity and F-score of 90.11%, 90.87% 88.88% and 0.92, respectively. The performance of the model is found to be better than the existing works in detecting OSA using the same database. Thus, the proposed automated OSA detection system is accurate, cost-effective and ready to be tested with a huge database.
    Matched MeSH terms: Electrocardiography
  13. Oh SL, Ng EYK, Tan RS, Acharya UR
    Comput Biol Med, 2018 11 01;102:278-287.
    PMID: 29903630 DOI: 10.1016/j.compbiomed.2018.06.002
    Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implementing an automated system in the clinical setting can potentially help expedite diagnosis of arrhythmia, and improve the accuracies. In this paper, we propose an automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature beats (APB) and premature ventricular contraction (PVC) on ECG signals. The novelty of this work is that we used ECG segments of variable length from the MIT-BIT arrhythmia physio bank database. The proposed system demonstrated high classification performance in the handling of variable-length data, achieving an accuracy of 98.10%, sensitivity of 97.50% and specificity of 98.70% using ten-fold cross validation strategy. Our proposed model can aid clinicians to detect common arrhythmias accurately on routine screening ECG.
    Matched MeSH terms: Electrocardiography
  14. Sharma M, Tan RS, Acharya UR
    Comput Biol Med, 2018 11 01;102:341-356.
    PMID: 30049414 DOI: 10.1016/j.compbiomed.2018.07.005
    Myocardial infarction (MI), also referred to as heart attack, occurs when there is an interruption of blood flow to parts of the heart, due to the acute rupture of atherosclerotic plaque, which leads to damage of heart muscle. The heart muscle damage produces changes in the recorded surface electrocardiogram (ECG). The identification of MI by visual inspection of the ECG requires expert interpretation, and is difficult as the ECG signal changes associated with MI can be short in duration and low in magnitude. Hence, errors in diagnosis can lead to delay the initiation of appropriate medical treatment. To lessen the burden on doctors, an automated ECG based system can be installed in hospitals to help identify MI changes on ECG. In the proposed study, we develop a single-channel single lead ECG based MI diagnostic system validated using noisy and clean datasets. The raw ECG signals are taken from the Physikalisch-Technische Bundesanstalt database. We design a novel two-band optimal biorthogonal filter bank (FB) for analysis of the ECG signals. We present a method to design a novel class of two-band optimal biorthogonal FB in which not only the product filter but the analysis lowpass filter is also a halfband filter. The filter design problem has been composed as a constrained convex optimization problem in which the objective function is a convex combination of multiple quadratic functions and the regularity and perfect reconstruction conditions are imposed in the form linear equalities. ECG signals are decomposed into six subbands (SBs) using the newly designed wavelet FB. Following to this, discriminating features namely, fuzzy entropy (FE), signal-fractal-dimensions (SFD), and renyi entropy (RE) are computed from all the six SBs. The features are fed to the k-nearest neighbor (KNN). The proposed system yields an accuracy of 99.62% for the noisy dataset and an accuracy of 99.74% for the clean dataset, using 10-fold cross validation (CV) technique. Our MI identification system is robust and highly accurate. It can thus be installed in clinics for detecting MI.
    Matched MeSH terms: Electrocardiography
  15. Yıldırım Ö, Pławiak P, Tan RS, Acharya UR
    Comput Biol Med, 2018 11 01;102:411-420.
    PMID: 30245122 DOI: 10.1016/j.compbiomed.2018.09.009
    This article presents a new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis. Cardiovascular disease prevention is one of the most important tasks of any health care system as about 50 million people are at risk of heart disease in the world. Although automatic analysis of ECG signal is very popular, current methods are not satisfactory. The goal of our research was to design a new method based on deep learning to efficiently and quickly classify cardiac arrhythmias. Described research are based on 1000 ECG signal fragments from the MIT - BIH Arrhythmia database for one lead (MLII) from 45 persons. Approach based on the analysis of 10-s ECG signal fragments (not a single QRS complex) is applied (on average, 13 times less classifications/analysis). A complete end-to-end structure was designed instead of the hand-crafted feature extraction and selection used in traditional methods. Our main contribution is to design a new 1D-Convolutional Neural Network model (1D-CNN). The proposed method is 1) efficient, 2) fast (real-time classification) 3) non-complex and 4) simple to use (combined feature extraction and selection, and classification in one stage). Deep 1D-CNN achieved a recognition overall accuracy of 17 cardiac arrhythmia disorders (classes) at a level of 91.33% and classification time per single sample of 0.015 s. Compared to the current research, our results are one of the best results to date, and our solution can be implemented in mobile devices and cloud computing.
    Matched MeSH terms: Electrocardiography
  16. Jahmunah V, Oh SL, Wei JKE, Ciaccio EJ, Chua K, San TR, et al.
    Phys Med, 2019 Jun;62:95-104.
    PMID: 31153403 DOI: 10.1016/j.ejmp.2019.05.004
    The heart muscle pumps blood to vital organs, which is indispensable for human life. Congestive heart failure (CHF) is characterized by the inability of the heart to pump blood adequately throughout the body without an increase in intracardiac pressure. The symptoms include lung and peripheral congestion, leading to breathing difficulty and swollen limbs, dizziness from reduced delivery of blood to the brain, as well as arrhythmia. Coronary artery disease, myocardial infarction, and medical co-morbidities such as kidney disease, diabetes, and high blood pressure all take a toll on the heart and can impair myocardial function. CHF prevalence is growing worldwide. It afflicts millions of people globally, and is a leading cause of death. Hence, proper diagnosis, monitoring and management are imperative. The importance of an objective CHF diagnostic tool cannot be overemphasized. Standard diagnostic tests for CHF include chest X-ray, magnetic resonance imaging (MRI), nuclear imaging, echocardiography, and invasive angiography. However, these methods are costly, time-consuming, and they can be operator-dependent. Electrocardiography (ECG) is inexpensive and widely accessible, but ECG changes are typically not specific for CHF diagnosis. A properly designed computer-aided detection (CAD) system for CHF, based on the ECG, would potentially reduce subjectivity and provide quantitative assessment for informed decision-making. Herein, we review existing CAD for automatic CHF diagnosis, and highlight the development of an ECG-based CAD diagnostic system that employs deep learning algorithms to automatically detect CHF.
    Matched MeSH terms: Electrocardiography*
  17. Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR
    Comput Methods Programs Biomed, 2019 Jul;176:81-91.
    PMID: 31200914 DOI: 10.1016/j.cmpb.2019.04.032
    BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal.

    METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.

    RESULTS: Our review shows that all of these signals contain information for sleep stage scoring.

    CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.

    Matched MeSH terms: Electrocardiography
  18. Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR
    Comput Methods Programs Biomed, 2019 Jul;176:121-133.
    PMID: 31200900 DOI: 10.1016/j.cmpb.2019.05.004
    BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.

    METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.

    RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.

    CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.

    Matched MeSH terms: Electrocardiography/methods*; Electrocardiography, Ambulatory
  19. Alizadehsani R, Abdar M, Roshanzamir M, Khosravi A, Kebria PM, Khozeimeh F, et al.
    Comput Biol Med, 2019 08;111:103346.
    PMID: 31288140 DOI: 10.1016/j.compbiomed.2019.103346
    Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.
    Matched MeSH terms: Electrocardiography
  20. Tan JH, Hagiwara Y, Pang W, Lim I, Oh SL, Adam M, et al.
    Comput Biol Med, 2018 03 01;94:19-26.
    PMID: 29358103 DOI: 10.1016/j.compbiomed.2017.12.023
    Coronary artery disease (CAD) is the most common cause of heart disease globally. This is because there is no symptom exhibited in its initial phase until the disease progresses to an advanced stage. The electrocardiogram (ECG) is a widely accessible diagnostic tool to diagnose CAD that captures abnormal activity of the heart. However, it lacks diagnostic sensitivity. One reason is that, it is very challenging to visually interpret the ECG signal due to its very low amplitude. Hence, identification of abnormal ECG morphology by clinicians may be prone to error. Thus, it is essential to develop a software which can provide an automated and objective interpretation of the ECG signal. This paper proposes the implementation of long short-term memory (LSTM) network with convolutional neural network (CNN) to automatically diagnose CAD ECG signals accurately. Our proposed deep learning model is able to detect CAD ECG signals with a diagnostic accuracy of 99.85% with blindfold strategy. The developed prototype model is ready to be tested with an appropriate huge database before the clinical usage.
    Matched MeSH terms: Electrocardiography*
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