Displaying publications 61 - 80 of 212 in total

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  1. 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
  2. Syed Farid Almufazal Syed Salim, Shamsuriani Md Jamal
    MyJurnal
    FascicularVentricular Tachycardia (VT) is a uniqueclinical syndrome, rarelyencountered by physicians.It isalso known as BelhassenSyndrome, named after a physician who reported the case in 1981. The condition,accounts for 10-15% of total idiopathic VTand the rhythm is sensitive tocalcium channel blocker. First described in 1979, the diagnosis of thissyndromeremains challenging,as the electrocardiogram (ECG) changes may be incorrectly diagnosed as Supraventricular Tachycardia (SVT) with aberrant conductions. We describeda patient whopresented to Emergency Department with palpitation. The difficulty in diagnosis and management is illustrated in the reportas he was initially misdiagnosed as SVT with resistance to initial standard treatment.This case report alsodescribedwide complex tachycardia algorithms to assist physician in daily clinicalpractice. Therapeutic options inmanaging this rare syndrome werealso discussed.
    Matched MeSH terms: Electrocardiography
  3. 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
  4. Sayuti KA, Azizi MYSB
    BMJ Case Rep, 2020 Apr 22;13(4).
    PMID: 32327461 DOI: 10.1136/bcr-2019-234225
    We report a case of a 46-year-old woman who has presented to a peripheral hospital with progressive exertional dyspnoea and chest discomfort. The resting ECG showed features of left-sided ventricular hypertrophy. The initial chest radiograph was reported as cardiomegaly. Initial echocardiography revealed left atrial dilatation and 'left ventricular' hypertrophy with normal ejection fraction. She was treated as possible coronary artery disease and was subsequently referred to our centre for CT coronary angiography. Findings from the CT scan were consistent with congenitally corrected transposition of the great arteries (ccTGA). This report describes the radiological features of ccTGA, its associated cardiovascular anomalies, pathophysiology and potential complications.
    Matched MeSH terms: Electrocardiography
  5. Shamala N., Faizal, A.H.
    Medicine & Health, 2018;13(2):195-201.
    MyJurnal
    Electrocardiographic abnormalities can be associated with acute pancreatitis. However, data regarding the actual causative factor still remains elusive. Many previous cases were reported on non-specific ST and T wave abnormalities concurrent with acute pancreatitis but rarely with an increasing trend of cardiac markers. We describe the case of a 70-year-old female who presented with one such conundrum. Our patient had typical presentation of acute pancreatitis but had dynamic ECG changes with markedly increased cardiac markers. Subsequently after initiation of treatment for acute pancreatitis and observation for the course of several days, the ECG returned to the baseline as pre admission. This substantiates the fact that acute pancreatitis can mimic both biochemical and electrical manifestation of an acute coronary syndrome. Thus, Emergency Physicians should consider acute pancreatitis as a possible diagnosis in patients who present with abnormal electrocardiograms.
    Matched MeSH terms: Electrocardiography
  6. Ohn MH, Souza U, Ohn KM
    Tzu Chi Med J, 2020 08 02;32(4):392-397.
    PMID: 33163387 DOI: 10.4103/tcmj.tcmj_91_19
    Objective: Negative affect state toward learning has a substantial impact on the learning process, academic performance, and practice of a particular subject, but such attitude toward electrocardiogram (ECG) learning has still received relatively little attention in medical education research. In spite of the significant emphasis in investigating ECG teaching method, the educators would not be able to address ECG incompetency without understanding the negative perception and attitude toward ECG learning. The purpose of this study was to assess the undergraduate students' difficulties in ECG learning and hence help educators design appropriate ECG learning curriculum to instill competent skill in ECG interpretation based on this outcome.

    Materials and Methods: A total of 324 undergraduate preclinical (year 2) and clinical (year 3-5) medical students participated in this study. The research design used thematic analysis of an open-ended questionnaire to analyze the qualitative data.

    Results: The thematic analysis detected five major emergent themes: lack of remembering (18.2%), lack of understanding (28.4%), difficulty in applying (3.6%), difficulty in analysis (15.1%), and difficulty in interpretation (17.8%), of which addressing these challenges could be taken as a foundation step upon which medical educators put an emphasis on in order to improve ECG teaching and learning.

    Conclusion: Negative attitude toward ECG learning poses a serious threat to acquire competency in ECG interpretation skill. The concept of student's memorizing ECG is not a correct approach; instead, understanding the concept and vector analysis is an elementary key for mastering ECG interpretation skill. The finding of this study sheds light into a better understanding of medical students' deficient points of ECG learning in parallel with taxonomy of cognitive domain and enables the medical teachers to come up with effective and innovative strategies for innovative ECG learning in an undergraduate medical curriculum.

    Matched MeSH terms: Electrocardiography
  7. Banu SZ
    Med J Malaysia, 1977 Mar;31(3):236-40.
    PMID: 904519
    Matched MeSH terms: Electrocardiography
  8. Qaisar SM, Mihoub A, Krichen M, Nisar H
    Sensors (Basel), 2021 Feb 22;21(4).
    PMID: 33671583 DOI: 10.3390/s21041511
    The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.
    Matched MeSH terms: Electrocardiography
  9. Mazlinda Musa, Fidelia Ferderik Anis, Hamidah Hassan, Syed Sharizman Syed Abdul Rahim, Siti Fatimah Saat
    MyJurnal
    Introduction: Airway management is one of the most important steps in emergency patient care, and it is part of the core content of emergency training programme in nursing. Besides, learning in the real clinical area on artificial air- way management is almost impossible due to the complexity of clinical conditions and non-uniform treatment algo- rithms that make the training strategies even more difficult to develop. This study was to evaluate the effectiveness of the simulation airway management training programme developed for the final year nursing students. Methods: This was a quasi-experimental with convenience sampling technique approach used. Students were exposed with the Intensive simulation of airway management technique which includes BLS, oropharyngeal measure and insertion, high flow O2 administration, interpret ECG, use of defib and understanding role of arrest team during emergency. The questionnaire on confident level was given before and after the simulation of airway management. Results: The results showed significant different in the mean score of pre-tests and post-tests (CI95% (-0.53414, -0.09586), t= -3.009, df = 19, p
    Matched MeSH terms: Electrocardiography
  10. Liew CK, Leong WS
    Med J Malaysia, 2012 Feb;67(1):108-10.
    PMID: 22582559 MyJurnal
    Coronary arteries vasospasm (CAS) is commonly seen in invasive cardiology laboratory during diagnostic catheterization or coronary intervention. Though the incidence of Printzmetal angina is uncommon, coronary vasospasm resulting in acute myocardial infarct is rare, especially if there is no significant atherosclerotic plaque within the coronary vasculature.
    Matched MeSH terms: Electrocardiography
  11. 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
  12. Ibrahim NS, Rampal S, Lee WL, Pek EW, Suhaimi A
    Cardiovasc Eng Technol, 2024 Feb;15(1):12-21.
    PMID: 37973701 DOI: 10.1007/s13239-023-00693-z
    PURPOSE: Photoplethysmography measurement of heart rate with wrist-worn trackers has been introduced in healthy individuals. However, additional consideration is necessary for patients with ischemic heart disease, and the available evidence is limited. The study aims to evaluate the validity and reliability of heart rate measures by a wrist-worn photoplethysmography (PPG) tracker compared to an electrocardiogram (ECG) during incremental treadmill exercise among patients with ischemic heart disease.

    METHODS: Fifty-one participants performed the standard incremental treadmill exercise in a controlled laboratory setting with 12-lead ECG attached to the patient's body and wearing wrist-worn PPG trackers.

    RESULTS: At each stage, the absolute percentage error of the PPG was within 10% of the standard acceptable range. Further analysis using a linear mixed model, which accounts for individual variations, revealed that PPG yielded the best performance at the baseline low-intensity exercise. As the stages progressed, heart rate validity decreased but was regained during recovery. The reliability was moderate to excellent.

    CONCLUSIONS: Low-cost trackers AMAZFIT Cor and Bip validity and reliability were within acceptable ranges, especially during low-intensity exercise among patients with ischemic heart disease recovering from cardiac procedures. Though using the tracker as part of the diagnosis tool still requires more supporting studies, it can potentially be used as a self-monitoring tool with precautions.

    Matched MeSH terms: Electrocardiography
  13. Jin C, Dai Q, Li P, Lam P, Cha YM
    J Cardiovasc Electrophysiol, 2023 Sep;34(9):1933-1943.
    PMID: 37548113 DOI: 10.1111/jce.16013
    INTRODUCTION: Left bundle branch area pacing (LBBP) is a novel conduction system pacing method to achieve effective physiological pacing and an alternative to cardiac resynchronization therapy (CRT) with biventricular pacing (BVP) for patients with heart failure with reduced ejection fraction (HFrEF). We conduted this meta-analysis and systemic review to review current data comparing BVP and LBBP in patients with HFrEF and indications for CRT.

    METHODS: We searched PubMed/Medline, Web of Science, and Cochrane Library from the inception of the database to November 2022. All studies that compared LBBP with BVP in patients with HFrEF and indications for CRT were included. Two reviewers performed study selection, data abstraction, and risk of bias assessment. We calculated risk ratios (RRs) with the Mantel-Haenszel method and mean difference (MD) with inverse variance using random effect models. We assessed heterogeneity using the I2 index, with I2  > 50% indicating significant heterogeneity.

    RESULTS: Ten studies (9 observational studies and 1 randomized controlled trial; 616 patients; 15 centers) published between 2020 and 2022 were included. We observed a shorter fluoroscopy time (MD: 9.68, 95% confidence interval [CI]: 4.49-14.87, I2  = 95%, p 

    Matched MeSH terms: Electrocardiography
  14. Sze TL, Abdul Aziz YF, Abu Bakar N, Mohd Sani F, Oemar H
    Iran J Radiol, 2015 Jan;12(1):e6878.
    PMID: 25793089 DOI: 10.5812/iranjradiol.6878
    Coronary artery fistula (CAF) is a rare anomaly of the coronary artery. Patients with this condition are usually asymptomatic. However, cardiac failure may occur later in life due to progressive enlargement of the fistula. Diagnosis is traditionally made by echocardiogram and conventional angiogram. However with the advantage of new technologies such as computed tomography (CT) coronary angiography, the course and communications of these fistulae can be delineated non-invasively and with greater accuracy. We report a case of a left circumflex artery fistula to the coronary sinus which was suspected on echocardiogram and the diagnosis was clinched on ECG-gated CT.
    Matched MeSH terms: Electrocardiography
  15. Acharya UR, Faust O, Sree V, Swapna G, Martis RJ, Kadri NA, et al.
    Comput Methods Programs Biomed, 2014;113(1):55-68.
    PMID: 24119391 DOI: 10.1016/j.cmpb.2013.08.017
    Coronary artery disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the heart rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.
    Matched MeSH terms: Electrocardiography
  16. Siti Nurul Azimah MJ, Laily Noor Zuliaty A, Sharifah Shafinaz SA, Huzairi S
    Med J Malaysia, 2025 Jan;80(1):96-101.
    PMID: 39812435
    INTRODUCTION: The coronavirus disease of 2019 (COVID-19) predominantly impacts the pulmonary system; however, it also has harmful consequences for the cardiovascular system through the occurrence of myocardial injury.

    MATERIALS AND METHODS: This retrospective study analysed 119 COVID-19 patients admitted to Al-Sultan Abdullah Hospital (HASA) from March until December 2020. Demographics, medical histories, admission laboratory results, electrocardiogram (ECG), echocardiogram (echo), were captured from the hospitals' health records. Myocardial injury is an injury to the myocardium that can be diagnosed by elevated cardiac troponin T or I level above the 99th percentile upper reference limit (URL), an abnormal ECG, and an abnormal echo. Data were analysed using Statistical Package for Social Sciences (SPSS) Version 27.

    RESULTS: From this study, it was founded that the prevalence of myocardial injury is 36.1% (43 subjects out of 119). The risk factors are older age (odds ratio, 2.347; p=0.028), males (odds ratio, 2.019; p=0.125), Indians (odds ratio, 3.659; p=0.296), hypertension (odds ratio, 2.776; p=0.009), diabetes mellitus (odds ratio, 1.732; p=0.155) and category 4 and 5 COVID-19 patients (odds ratio, 2.325; p=0.038).

    CONCLUSION: Myocardial injury is prevalent among patients affected by COVID-19 and is associated with older age, hypertension and category 4 and 5 COVID-19. The researchers suggested conducting a more thorough investigation of the sizable population in multiple settings and conducting a prospective study where all infected COVID-19 patients have to undergo several tests, such as ECG, troponin T, and echocardiogram.

    Matched MeSH terms: Electrocardiography
  17. Ahmad S, Valli H, Salvage SC, Grace AA, Jeevaratnam K, Huang CL
    Clin Exp Pharmacol Physiol, 2018 02;45(2):174-186.
    PMID: 28949414 DOI: 10.1111/1440-1681.12863
    Increasing evidence implicates chronic energetic dysfunction in human cardiac arrhythmias. Mitochondrial impairment through Pgc-1β knockout is known to produce a murine arrhythmic phenotype. However, the cumulative effect of this with advancing age and its electrocardiographic basis have not been previously studied. Young (12-16 weeks) and aged (>52 weeks), wild type (WT) (n = 5 and 8) and Pgc-1β-/- (n = 9 and 6), mice were anaesthetised and used for electrocardiographic (ECG) recordings. Time intervals separating successive ECG deflections were analysed for differences between groups before and after β1-adrenergic (intraperitoneal dobutamine 3 mg/kg) challenge. Heart rates before dobutamine challenge were indistinguishable between groups. The Pgc-1β-/- genotype however displayed compromised nodal function in response to adrenergic challenge. This manifested as an impaired heart rate response suggesting a functional defect at the level of the sino-atrial node, and a negative dromotropic response suggesting an atrioventricular conduction defect. Incidences of the latter were most pronounced in the aged Pgc-1β-/- mice. Moreover, Pgc-1β-/- mice displayed electrocardiographic features consistent with the existence of a pro-arrhythmic substrate. Firstly, ventricular activation was prolonged in these mice consistent with slowed action potential conduction and is reported here for the first time. Additionally, Pgc-1β-/- mice had shorter repolarisation intervals. These were likely attributable to altered K+ conductance properties, ultimately resulting in a shortened QTc interval, which is also known to be associated with increased arrhythmic risk. ECG analysis thus yielded electrophysiological findings bearing on potential arrhythmogenicity in intact Pgc-1β-/- systems in widespread cardiac regions.
    Matched MeSH terms: Electrocardiography*
  18. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, et al.
    Comput Biol Med, 2017 10 01;89:389-396.
    PMID: 28869899 DOI: 10.1016/j.compbiomed.2017.08.022
    The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats.
    Matched MeSH terms: Electrocardiography*
  19. Hussein AF, Hashim SJ, Aziz AFA, Rokhani FZ, Adnan WAW
    J Med Syst, 2017 Nov 29;42(1):15.
    PMID: 29188389 DOI: 10.1007/s10916-017-0871-8
    The non-stationary and multi-frequency nature of biomedical signal activities makes the use of time-frequency distributions (TFDs) for analysis inevitable. Time-frequency analysis provides simultaneous interpretations in both time and frequency domain enabling comprehensive explanation, presentation and interpretation of electrocardiogram (ECG) signals. The diversity of TFDs and specific properties for each type show the need to determine the best TFD for ECG analysis. In this study, a performance evaluation of five TFDs in term of ECG abnormality detection is presented. The detection criteria based on extracted features from most important ECG signal components (QRS) to detect normal and abnormal cases. This is achieved by estimating its energy concentration magnitude using the TFDs. The TFDs analyse ECG signals in one-minute interval instead of conventional time domain approach that analyses based on beat or frame containing several beats. The MIT-BIH normal sinus rhythm ECG database total records of 18 long-term ECG sampled at 128 Hz have been analysed. The tested TFDs include Dual-Tree Wavelet Transform, Spectrogram, Pseudo Wigner-Ville, Choi-Williams, and Born-Jordan. Each record is divided into one-minute slots, which is not considered previously, and analysed. The sample periods (slots) are randomly selected ten minutes interval for each record. This result with 99.44% detection accuracy for 15,735 ECG beats shows that Choi-Williams distribution is most reliable to be used for heart problem detection especially in automated systems that provide continuous monitoring for long time duration.
    Matched MeSH terms: Electrocardiography/methods*
  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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