Displaying publications 1 - 20 of 207 in total

  1. Wong KC
    Med J Malaysia, 2021 07;76(4):565.
    PMID: 34305119
    No abstract provided.
    Matched MeSH terms: Electrocardiography*
  2. Mulyadi IH, Fiedler P, Eichardt R, Haueisen J, Supriyanto E
    Med Biol Eng Comput, 2021 Feb;59(2):431-447.
    PMID: 33495984 DOI: 10.1007/s11517-021-02319-9
    Wearable electronics and sensors are increasingly popular for personal health monitoring, including smart shirts containing electrocardiography (ECG) electrodes. Optimal electrode performance requires careful selection of the electrode position. On top of the electrophysiological aspects, practical aspects must be considered due to the dynamic recording environment. We propose a new method to obtain optimal electrode placement by considering multiple dimensions. The electrophysiological aspects were represented by P-, R-, and T-peak of ECG waveform, while the shirt-skin gap, shirt movement, and regional sweat rate represented the practical aspects. This study employed a secondary data set and simulations for the electrophysiological and practical aspects, respectively. Typically, there is no ideal solution that maximizes satisfaction degrees of multiple electrophysiological and practical aspects simultaneously; a compromise is the most appropriate approach. Instead of combining both aspects-which are independent of each other-into a single-objective optimization, we used multi-objective optimization to obtain a Pareto set, which contains predominant solutions. These solutions may facilitate the decision-makers to decide the preferred electrode locations based on application-specific criteria. Our proposed approach may aid manufacturers in making decisions regarding the placement of electrodes within smart shirts.
    Matched MeSH terms: Electrocardiography*
    Med J Malaya, 1955 Mar;9(3):195-204.
    PMID: 14393209
    Matched MeSH terms: Electrocardiography/instrumentation*
  4. Mandala S, Di TC
    J Med Biol Eng, 2017;37(4):441-453.
    PMID: 28867990 DOI: 10.1007/s40846-017-0281-x
    Many studies showed electrocardiogram (ECG) parameters are useful for predicting fatal ventricular arrhythmias (VAs). However, the studies have several shortcomings. Firstly, all studies lack of effective way to present behavior of various ECG parameters prior to the occurrence of the VAs. Secondly, they also lack of discussion on how to consider the parameters as abnormal. Thirdly, the reports do not include approaches to increase the detection accuracy for the abnormal patterns. The purpose of this study is to address the aforementioned issues. It identifies ten ECG parameters from various sources and then presents a review based on the identified parameters. From the review, it has been found that the increased risk of VAs can be represented by presence and certain abnormal range of the parameters. The variation of parameters range could be influenced by either gender or age. This study also has discovered the facts that averaging, outliers elimination and morphology detection algorithms can contribute to the detection accuracy.
    Matched MeSH terms: Electrocardiography
  5. Koh KT, Law WC, Zaw WM, Foo DHP, Tan CT, Steven A, et al.
    Europace, 2021 07 18;23(7):1016-1023.
    PMID: 33782701 DOI: 10.1093/europace/euab036
    AIMS: Atrial fibrillation (AF) is a preventable cause of ischaemic stroke but it is often undiagnosed and undertreated. The utility of smartphone electrocardiogram (ECG) for the detection of AF after ischaemic stroke is unknown. The aim of this study is to determine the diagnostic yield of 30-day smartphone ECG recording compared with 24-h Holter monitoring for detecting AF ≥30 s.

    METHODS AND RESULTS: In this multicentre, open-label study, we randomly assigned 203 participants to undergo one additional 24-h Holter monitoring (control group, n = 98) vs. 30-day smartphone ECG monitoring (intervention group, n = 105) using KardiaMobile (AliveCor®, Mountain View, CA, USA). Major inclusion criteria included age ≥55 years old, without known AF, and ischaemic stroke or transient ischaemic attack (TIA) within the preceding 12 months. Baseline characteristics were similar between the two groups. The index event was ischaemic stroke in 88.5% in the intervention group and 88.8% in the control group (P = 0.852). AF lasting ≥30 s was detected in 10 of 105 patients in the intervention group and 2 of 98 patients in the control group (9.5% vs. 2.0%; absolute difference 7.5%; P = 0.024). The number needed to screen to detect one AF was 13. After the 30-day smartphone monitoring, there was a significantly higher proportion of patients on oral anticoagulation therapy at 3 months compared with baseline in the intervention group (9.5% vs. 0%, P = 0.002).

    CONCLUSIONS: Among patients ≥55 years of age with a recent cryptogenic stroke or TIA, 30-day smartphone ECG recording significantly improved the detection of AF when compared with the standard repeat 24-h Holter monitoring.

    Matched MeSH terms: Electrocardiography; Electrocardiography, Ambulatory
  6. Al-Busaidi AM, Khriji L, Touati F, Rasid MF, Mnaouer AB
    J Med Syst, 2017 Sep 12;41(10):166.
    PMID: 28900815 DOI: 10.1007/s10916-017-0817-1
    One of the major issues in time-critical medical applications using wireless technology is the size of the payload packet, which is generally designed to be very small to improve the transmission process. Using small packets to transmit continuous ECG data is still costly. Thus, data compression is commonly used to reduce the huge amount of ECG data transmitted through telecardiology devices. In this paper, a new ECG compression scheme is introduced to ensure that the compressed ECG segments fit into the available limited payload packets, while maintaining a fixed CR to preserve the diagnostic information. The scheme automatically divides the ECG block into segments, while maintaining other compression parameters fixed. This scheme adopts discrete wavelet transform (DWT) method to decompose the ECG data, bit-field preserving (BFP) method to preserve the quality of the DWT coefficients, and a modified running-length encoding (RLE) scheme to encode the coefficients. The proposed dynamic compression scheme showed promising results with a percentage packet reduction (PR) of about 85.39% at low percentage root-mean square difference (PRD) values, less than 1%. ECG records from MIT-BIH Arrhythmia Database were used to test the proposed method. The simulation results showed promising performance that satisfies the needs of portable telecardiology systems, like the limited payload size and low power consumption.
    Matched MeSH terms: Electrocardiography*
  7. Gul MU, Kamarul Azman MH, Kadir KA, Shah JA, Hussen S
    Comput Intell Neurosci, 2023;2023:8162325.
    PMID: 36909967 DOI: 10.1155/2023/8162325
    Atrial flutter (AFL) is a common arrhythmia with two significant mechanisms, namely, focal (FAFL) and macroreentry (MAFL). Discrimination of the AFL mechanism through noninvasive techniques can improve radiofrequency ablation efficacy. This study aims to differentiate the AFL mechanism using a 12-lead surface electrocardiogram. P-P interval series variability is hypothesized to be different in FAFL and MAFL and may be useful for discrimination. 12-lead ECG signals were collected from 46 patients with known AFL mechanisms. Features for a proposed classifier are extracted through descriptive statistics of the interval series. On the other hand, the class ratio of MAFL and FAFL was 41 : 5, respectively, which was highly imbalanced. To resolve this, different data augmentation techniques (SMOTE, modified-SMOTE, and smoothed-bootstrap) have been applied on the interval series to generate synthetic interval series and minimize imbalance. Modification is introduced in the classic SMOTE technique (modified-SMOTE) to properly produce data samples from the original distribution. The characteristics of modified-SMOTE are found closer to the original dataset than the other two techniques based on the four validation criteria. The performance of the proposed model has been evaluated by three linear classifiers, namely, linear discriminant analysis (LDA), logistic regression (LOG), and support vector machine (SVM). Filter and wrapper methods have been used for selecting relevant features. The best average performance was achieved at 400% augmentation of the FAFL interval series (90.24% sensitivity, 49.50% specificity, and 76.88% accuracy) in the LOG classifier. The variation of consecutive P-wave intervals has been shown as an effective concept that differentiates FAFL from MAFL through the 12-lead surface ECG.
    Matched MeSH terms: Electrocardiography/methods
  8. John AA, Subramanian AP, Jaganathan SK, Sethuraman B
    Indian Heart J, 2015 Nov-Dec;67(6):549-51.
    PMID: 26702684 DOI: 10.1016/j.ihj.2015.07.017
    To process the electrocardiogram (ECG) signals using MATLAB-based graphical user interface (GUI) and to classify the signals based on heart rate.
    Matched MeSH terms: Electrocardiography
  9. 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
  10. Khalid Y
    Med J Malaysia, 1994 Jun;49(2):174-5.
    PMID: 8090099
    Pulsus alternans, the alternating strong and week pulses which occur in patients with severe heart failure, was first described by Traube in 1872. Since then various methods, both invasive1,2 and non-invasive3,4, have been used to study this phenomenon. This study demonstrates the utility of using simultaneous electrocardiography (ECG) and Doppler echocardiography to document pulsus alternans, and to differentiate it from other causes of alternating pulses.
    Matched MeSH terms: Electrocardiography*
  11. Izan NF, Salleh SH, Ting CM, Noman F, Sh-Hussain H, Poznanski RR, et al.
    J Integr Neurosci, 2020 Sep 30;19(3):479-487.
    PMID: 33070527 DOI: 10.31083/j.jin.2020.03.222
    The purpose is to estimate the effectiveness of electrocardiograms during resting and active participation by the differentiation between the electrical activity of the heart while standing and sitting in a resting state. The concern is to identify the electrocardiogram parameters that did not show significant changes within these positions. The electrocardiogram parameters can be considered to be a standard marker for medically compromised patients. The electrocardiogram is recorded in the standing and sitting positions focusing on healthy participants using standard electrode placement of lead-I. Combined lead-I patterns (camel-hump or ST-segment prolongation) are usually seen in neurologic injury or hypothermia patients. The pairwise comparisons of a year data are about 454,400 cycles of sitting and 493,470 cycles of standing data. Thus, it is essential to quantify the nature and magnitude of changes seen in the electrocardiogram with a change of posture from sitting to standing in a healthy individual. This makes the findings of electrocardiogram analysis in this paper interesting in which some parameters (i.e., camel-hump patterns in lead-I) are helpful for clinical interpretations and could be suggestive of neurologic injury.
    Matched MeSH terms: Electrocardiography*
  12. Mutlag AA, Ghani MKA, Mohammed MA, Lakhan A, Mohd O, Abdulkareem KH, et al.
    Sensors (Basel), 2021 Oct 19;21(20).
    PMID: 34696135 DOI: 10.3390/s21206923
    In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.
    Matched MeSH terms: Electrocardiography*
  13. Chew KT, Raman V, Then PHH
    Sensors (Basel), 2021 Dec 08;21(24).
    PMID: 34960291 DOI: 10.3390/s21248197
    Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. As the leading cause of deaths worldwide, further improvements to the early detection and prevention of these cardiovascular diseases is of the utmost importance for reducing the death toll. In particular, the remote and continuous monitoring of vital signs such as electrocardiograms are critical for improving the detection rates and speed of abnormalities while improving accessibility for elderly individuals. In this paper, we consider the design and deployment characteristics of a remote patient monitoring system for arrhythmia detection in elderly individuals. Thus, we developed a scalable system architecture to support remote streaming of ECG signals at near real-time. Additionally, a two-phase classification scheme is proposed to improve the performance of existing ECG classification algorithms. A prototype of the system was deployed at the Sarawak General Hospital, remotely collecting data from 27 unique patients. Evaluations indicate that the two-phase classification scheme improves algorithm performance when applied to the MIT-BIH Arrhythmia Database and the remotely collected single-lead ECG recordings.
    Matched MeSH terms: Electrocardiography*
  14. Liao CM, Soo CS
    Singapore Med J, 1996 Feb;37(1):101, 122-3.
    PMID: 8783924
    Matched MeSH terms: Electrocardiography*
  15. 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: Electrocardiography/methods
  16. Meau YP, Ibrahim F, Narainasamy SA, Omar R
    Comput Methods Programs Biomed, 2006 May;82(2):157-68.
    PMID: 16638620
    This study presents the development of a hybrid system consisting of an ensemble of Extended Kalman Filter (EKF) based Multi Layer Perceptron Network (MLPN) and a one-pass learning Fuzzy Inference System using Look-up Table Scheme for the recognition of electrocardiogram (ECG) signals. This system can distinguish various types of abnormal ECG signals such as Ventricular Premature Cycle (VPC), T wave inversion (TINV), ST segment depression (STDP), and Supraventricular Tachycardia (SVT) from normal sinus rhythm (NSR) ECG signal.
    Matched MeSH terms: Electrocardiography/classification*
  17. Loh KY, Mohamed AL
    N Engl J Med, 2005 Sep 1;353(9):933.
    PMID: 16135838 DOI: 10.1056/NEJMicm040398
    Matched MeSH terms: Electrocardiography*
  18. 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: Electrocardiography*
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