Displaying all 14 publications

Abstract:
Sort:
  1. ECG lead misplacement in the frontal and horizontal plane mimicking A myocardial infarction
    Kaisbain N, Khoo KKL, Lim WJ
    Am J Emerg Med, 2023 Dec;74:196.e1-196.e4.
    PMID: 37863804 DOI: 10.1016/j.ajem.2023.10.009
    BACKGROUND/AIMS: Electrocardiogram (ECG) is an inexpensive, fundamental screening tool used in daily clinical practice. It is essential in the diagnosis of life-threatening conditions, such as acute myocardial infarctions, ventricular arrhythmias etc. However, ECG lead misplacement is a common technical error, which may translate into wrong interpretations, unnecessary investigations, and improper treatments.

    METHODS/RESULTS: We report a case of a multiple ECG lead misplacement made across two different planes of the heart, resulting in a bizarre series of ECG, mimicking an acute high lateral myocardial infarction. Multiple ECGs were done as there were abrupt changes compared to previous ECGS. Patient was pain free and administration of potentially harmful procedures and treatments were prevented.

    CONCLUSION: Our case demonstrated the importance of high clinical suspicion in diagnosing ECG lead misplacement. It is the responsibility of both the healthcare workers who are performing and interpreting the ECG to be alert of a possible lead malposition, to prevent untoward consequences to the patient.

    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  2. Fulltext A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal
    Ullah A, Rehman SU, Tu S, Mehmood RM, Fawad, Ehatisham-Ul-Haq M
    Sensors (Basel), 2021 Feb 01;21(3).
    PMID: 33535397 DOI: 10.3390/s21030951
    Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model's classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models' effectiveness.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  3. HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
    Islam MS, Hasan KF, Sultana S, Uddin S, Lio' P, Quinn JMW, et al.
    Neural Netw, 2023 May;162:271-287.
    PMID: 36921434 DOI: 10.1016/j.neunet.2023.03.004
    Deep learning-based models have achieved significant success in detecting cardiac arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the performance of such models, we have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved. By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60%, F1 score of 98.21%, a precision of 97.66%, and recall of 99.60% using MIT-BIH generated ECG. In addition, this approach significantly reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  4. Current methods in electrocardiogram characterization
    Martis RJ, Acharya UR, Adeli H
    Comput Biol Med, 2014 May;48:133-49.
    PMID: 24681634 DOI: 10.1016/j.compbiomed.2014.02.012
    The Electrocardiogram (ECG) is the P-QRS-T wave depicting the cardiac activity of the heart. The subtle changes in the electric potential patterns of repolarization and depolarization are indicative of the disease afflicting the patient. These clinical time domain features of the ECG waveform can be used in cardiac health diagnosis. Due to the presence of noise and minute morphological parameter values, it is very difficult to identify the ECG classes accurately by the naked eye. Various computer aided cardiac diagnosis (CACD) systems, analysis methods, challenges addressed and the future of cardiovascular disease screening are reviewed in this paper. Methods developed for time domain, frequency transform domain, and time-frequency domain analysis, such as the wavelet transform, cannot by themselves represent the inherent distinguishing features accurately. Hence, nonlinear methods which can capture the small variations in the ECG signal and provide improved accuracy in the presence of noise are discussed in greater detail in this review. A CACD system exploiting these nonlinear features can help clinicians to diagnose cardiovascular disease more accurately.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  5. Fulltext Cardiac arrhythmias and echocardiographic features in Wolff-Parkinson-White syndrome
    Ng WH, Kew ST
    Med J Malaysia, 1980 Sep;35(1):41-5.
    PMID: 7253998
    Electrocardiographic features of the Woljf-Parkinson-White syndrome may be seen in normal individuals and in those with congenital or acquired heart disease. Predisposition to tachyarrhythmias and its misinterpretation are common. In this report a case of Wolff-Parkinson-White syndrome in a 25 year old Malay male who presented with cardiac arrhythmias is described. Echocardiographic findings and the role of echocardiography are discussed.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis*
  6. Fulltext Evaluation of conventional and computerised monitoring of cardiac function in the coronary care unit
    Ng KH
    Med J Malaysia, 1983 Dec;38(4):289-93.
    PMID: 6599984
    One of the important functions of the Coronary Care Unit (CCU) is the continuous and intensive monitoring of cardiac function. To date, many monitoring techniques have been developed and tested. In this paper, both the conventional and computerised monitoring techniques are reviewed and evaluated. It is shown that a computerised system has several defirute advantages over the conventional system, e.g. lower false alarm rate, accurate and fast data processing, retrospective studies. However one also ought to be aware of the limitations,
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  7. Cardiac arrhythmias in acute myocardial infarction
    Ng WH, Goh TH, Ishak E, Ahmad Z
    Med J Malaysia, 1979 Dec;34(2):131-5.
    PMID: 548713
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis*
  8. Fulltext ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree
    Mandala S, Cai Di T, Sunar MS, Adiwijaya
    PLoS One, 2020;15(5):e0231635.
    PMID: 32407335 DOI: 10.1371/journal.pone.0231635
    Spontaneous prediction of malignant ventricular arrhythmia (MVA) is useful to avoid delay in rescue operations. Recently, researchers have developed several algorithms to predict MVA using various features derived from electrocardiogram (ECG). However, there are several unresolved issues regarding MVA prediction such as the effect of number of ECG features on a prediction remaining unclear, possibility that an alert for occurring MVA may arrive very late and uncertainty in the performance of the algorithm predicting MVA minutes before onset. To overcome the aforementioned problems, this research conducts an in-depth study on the number and types of ECG features that are implemented in a decision tree classifier. In addition, this research also investigates an algorithm's execution time before the occurrence of MVA to minimize delays in warnings for MVA. Lastly, this research aims to study both the sensitivity and specificity of an algorithm to reveal the performance of MVA prediction algorithms from time to time. To strengthen the results of analysis, several classifiers such as support vector machine and naive Bayes are also examined for the purpose of comparison study. There are three phases required to achieve the objectives. The first phase is literature review on existing relevant studies. The second phase deals with design and development of four modules for predicting MVA. Rigorous experiments are performed in the feature selection and classification modules. The results show that eight ECG features with decision tree classifier achieved good prediction performance in terms of execution time and sensitivity. In addition, the results show that the highest percentage for sensitivity and specificity is 95% and 90% respectively, in the fourth 5-minute interval (15.1 minutes-20 minutes) that preceded the onset of an arrhythmia event. Such results imply that the fourth 5-minute interval would be the best time to perform prediction.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis*
  9. [Possibilities for predicting of cardiac arrhythmias and conductivity disorders in former athletes.]
    Krysiuk OB, Obrezan AG, Zadvorev SF, Yakovlev AA
    Adv Gerontol, 2020;33(1):131-136.
    PMID: 32362096
    In order to analyze the relationship between the athletic qualification and syndrome of cardiac rhythm and conductivity disturbances in former athletes, a retrospective analysis of medical records of 39 male former athletes with cardiovascular complaints (mean age 61,6±11,3 years, mean duration of career in sports 23,9±17,3 years, mean duration of post-athletic period 20,1±9,9 years) was carried out. The patients were screened for cardiac arrhythmias and underwent echocardiography. The overall prevalence of sustained paroxysms of atrial fibrillation was 42%, increasing with the athletic qualification. Ryan grade 4b-5 premature ventricular contractions were found in 14% of patients. 3 parameters were found to be the independent predictors of arrhythmias in former athletes, i. e. athletic qualification, multifocal atherosclerosis (as an anti-risk factor), and age. The coefficient of determinance for the created prognostic model reached 43%. Further prospective studies are needed to validate an algorithm.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  10. Fulltext The rationale and design of the Micra Transcatheter Pacing Study: safety and efficacy of a novel miniaturized pacemaker
    Ritter P, Duray GZ, Zhang S, Narasimhan C, Soejima K, Omar R, et al.
    Europace, 2015 May;17(5):807-13.
    PMID: 25855677 DOI: 10.1093/europace/euv026
    Recent advances in miniaturization technologies and battery chemistries have made it possible to develop a pacemaker small enough to implant within the heart while still aiming to provide similar battery longevity to conventional pacemakers. The Micra Transcatheter Pacing System is a miniaturized single-chamber pacemaker system that is delivered via catheter through the femoral vein. The pacemaker is implanted directly inside the right ventricle of the heart, eliminating the need for a device pocket and insertion of a pacing lead, thereby potentially avoiding some of the complications associated with traditional pacing systems.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  11. A deep convolutional neural network model to classify heartbeats
    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: Arrhythmias, Cardiac/diagnosis
  12. Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis
    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: Arrhythmias, Cardiac/diagnosis*
  13. Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals
    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: Arrhythmias, Cardiac/diagnosis
  14. A new approach for arrhythmia classification using deep coded features and LSTM networks
    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: Arrhythmias, Cardiac/diagnosis*
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links