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Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals

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*
Diagnostic value of electrocardiogram in cardiac tamponade

Ang KP, Nordin RB, Lee SCY, Lee CY, Lu HT

Med J Malaysia, 2019 02;74(1):51-56.
PMID: 30846663

INTRODUCTION: We aim to study the diagnostic value of electrocardiogram (ECG) in cardiac tamponade.
METHODS: This study was a single centre, retrospective casecontrol study. We recruited 42 patients diagnosed with cardiac tamponade of various aetiologies confirmed by transthoracic echocardiography and 100 controls between January 2011 and December 2015. The ECG criteria of cardiac tamponade we adopted was as follows: 1) Low QRS voltage in a) the limb leads alone, b) in the precordial leads alone or, c) in all leads, 2) PR segment depression, 3) Electrical alternans, and 4) Sinus tachycardia.
RESULTS: Malignancy was the most common causes of cardiac tamponade, the two groups were of similar proportion of gender and ethnicity. We calculated the sensitivity (SN), specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) of each ECG criteria. Among the ECG abnormalities, we noted the SN of 'low voltage in all chest leads' (69%), 'low voltage in all limb leads' (67%) and 'sinus tachycardia' (69%) were higher as compared to 'PR depression' (12%) and 'electrical alternan' (5%). On the other hand, 'low voltage in all chest leads' (98%), 'low voltage in all leads' (99%), 'PR depression' (100%) and 'electrical alternans' (100%) has highest SP.
CONCLUSION: Our study reaffirmed the findings of previous studies that electrocardiography cannot be used as a screening tool for diagnosing cardiac tamponade due to its low sensitivity. However, with clinical correlation, electrocardiography is a valuable adjuvant test to 'rule in' cardiac tamponade because of its high specificity.

Matched MeSH terms: Electrocardiography*
The electrocardiogram of healthy young Chinese and Malay men

Burns-Cox CJ, Lau LC, Toh BH

J Electrocardiol, 1971;4(3):211-9.
PMID: 5126628
Matched MeSH terms: Electrocardiography*
ST. segment displacement in myocardial infarction

Fah NT

Med J Malaysia, 1977 Jun;31(4):309-15.
PMID: 927238
Matched MeSH terms: Electrocardiography*
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: Electrocardiography/methods*
Fulltext Reliability of computerised electrocardiographic reports

Ong HT, Kuah SH, Chew SP

Singapore Med J, 1993 Feb;34(1):53-4.
PMID: 8266130

The aim of this study is to assess the reliability of computerised reporting of electrocardiograms (ECG). Fifty ECG performed consecutively at the outpatient department of the Penang Adventist Hospital on the Marquette 12SL-SC were studied. Two physicians independently reviewed the ECG and the manual readings were compared with each other and to the computer reports. There was no significant difference in the measurement of rate. The PR and QT intervals measured by the two physicians were similar but each was significantly different from the computer reading. The QRS duration assessed by Physician 1 was similar to the computer reading but each was significantly different from that of Physician 2. The overall diagnosis was the same between the two physicians in 76%, between Physician 1 and the computer in 68%, and between Physician 2 and the computer in 78%. No ECG was reported as normal by the computer and said to be abnormal by either physician. Thus, the computer programme is reasonably reliable in ECG reporting with computer-physician variability being comparable to inter-physician variability.

Study site: outpatient department of the Penang Adventist Hospital

Matched MeSH terms: Electrocardiography*
Fulltext An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier

Hussein AF, Hashim SJ, Rokhani FZ, Wan Adnan WA

Sensors (Basel), 2021 Mar 26;21(7).
PMID: 33810211 DOI: 10.3390/s21072311

Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases.

Matched MeSH terms: Electrocardiography
Fulltext A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network

Wu M, Lu Y, Yang W, Wong SY

Front Comput Neurosci, 2020;14:564015.
PMID: 33469423 DOI: 10.3389/fncom.2020.564015

Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.

Matched MeSH terms: Electrocardiography
Fulltext Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor

Nizam Y, Mohd MNH, Jamil MMA

Sensors (Basel), 2018 Jul 13;18(7).
PMID: 30011823 DOI: 10.3390/s18072260

Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection.

Matched MeSH terms: Electrocardiography
Fulltext Cardiovascular disease prediction from electrocardiogram by using machine learning

Nayan NA, Ab Hamid H, Suboh MZ, Abdullah N, Jaafar R, Mhd Yusof NA, et al.

International Journal of Online and Biomedical Engineering, 2020;16(7):34-47.
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
Fulltext A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

Awais M, Badruddin N, Drieberg M

Sensors (Basel), 2017 Aug 31;17(9).
PMID: 28858220 DOI: 10.3390/s17091991

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.

Matched MeSH terms: Electrocardiography
Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III

Boon KH, Khalil-Hani M, Malarvili MB

Comput Methods Programs Biomed, 2018 Jan;153:171-184.
PMID: 29157449 DOI: 10.1016/j.cmpb.2017.10.012

This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity.

Matched MeSH terms: Electrocardiography
Fulltext Novel impedance measurement technique for soluble solid content determination of banana

Ibrahim, N.U.A., Abd Aziz, S., Nawi, N.M.

Pertanika Journal of Science & Technology, 2017;25(2):519-526.
MyJurnal

Soluble solid content (SSC) is one of the important traits that indicate the ripeness of banana fruits.
Determination of SSC for banana often requires destructive laboratory analysis on the fruit. An impedance measurement technique was investigated as a non-destructive approach for SSC determination of bananas. A pair of electrocardiogram (ECG) electrode connected to an impedance analyser board was used to measure the impedance value of bananas over the frequency of 19.5 to 20.5 KHz. The SSC measurement was conducted using a pocket refractometer and data was analysed to correlate SSC with impedance values. It was found that the mean of impedance, Z decreased from 10.01 to 99.93 KΩ at the frequency of 20 KHz, while the mean value of SSC increased from 0.58 to 4.93 % Brix from day 1 to day 8. The best correlation between impedance and SSC was found at 20 KHz, with the coefficient of determination, R2 of 0.87. This result indicates the potential of impedance measurement in predicting SSC of banana fruits.

Matched MeSH terms: Electrocardiography
Fulltext Anaesthetic challenges in a patient with Wolff-Parkinson-White (WPW) syndrome for orchidectomy

Yeap TB, Teah MK, Thevarajah S, Azerai S

BMJ Case Rep, 2021 Mar 25;14(3).
PMID: 33766970 DOI: 10.1136/bcr-2020-241176

Wolff-Parkinson-White (WPW) syndrome is an extremely rare congenital cardiac conduction disorder. It is due to an aberrant pathway between the atrium and ventricle. This manuscript entails a man with an underlying WPW who was posted for an elective orchidectomy. We discussed the important perioperative precautions to prevent the precipitation of acute cardiac events.

Matched MeSH terms: Electrocardiography
Fulltext Atrial septal defect with Crochetage sign presenting with pulmonary artery thrombosis

Kaisbain N, Lim WJ, Kim HS

BMJ Case Rep, 2021 Jul 27;14(7).
PMID: 34315750 DOI: 10.1136/bcr-2021-244180

Atrial septal defect (ASD) is the most common congenital heart disease observed in adult. Several ECG findings are considered sensitive for the diagnosis of ASD. We describe a 50 years old man who displayed Crochetage sign, incomplete right bundle branch block (IRBBB) and right ventricular strain pattern on ECG. Crochetage sign is highly specific for ASD and it correlates with shunt severity. The diagnostic specificity for ASD increases if the R waves have both Crochetage patterns and IRBBB. It is important not to confuse Crochetage signs with IRBBB abnormalities on ECG. Our patient was ultimately diagnosed with a large ASD measuring 3 cm with bidirectional shunt and concomitant pulmonary thrombosis. This illustrates that high suspicion of the ASD with the use of good-old ECG signs remains relevant in this modern era. This also reminds us that patients with Eisenmenger syndrome are at higher risk for pulmonary thrombosis.

Matched MeSH terms: Electrocardiography
Fulltext Deep neural network technique for automated detection of ADHD and CD using ECG signal

Loh HW, Ooi CP, Oh SL, Barua PD, Tan YR, Molinari F, et al.

Comput Methods Programs Biomed, 2023 Nov;241:107775.
PMID: 37651817 DOI: 10.1016/j.cmpb.2023.107775

BACKGROUND AND OBJECTIVE: Attention Deficit Hyperactivity problem (ADHD) is a common neurodevelopment problem in children and adolescents that can lead to long-term challenges in life outcomes if left untreated. Also, ADHD is frequently associated with Conduct Disorder (CD), and multiple research have found similarities in clinical signs and behavioral symptoms between both diseases, making differentiation between ADHD, ADHD comorbid with CD (ADHD+CD), and CD a subjective diagnosis. Therefore, the goal of this pilot study is to create the first explainable deep learning (DL) model for objective ECG-based ADHD/CD diagnosis as having an objective biomarker may improve diagnostic accuracy.
METHODS: The dataset used in this study consist of ECG data collected from 45 ADHD, 62 ADHD+CD, and 16 CD patients at the Child Guidance Clinic in Singapore. The ECG data were segmented into 2 s epochs and directly used to train our 1-dimensional (1D) convolutional neural network (CNN) model.
RESULTS: The proposed model yielded 96.04% classification accuracy, 96.26% precision, 95.99% sensitivity, and 96.11% F1-score. The Gradient-weighted class activation mapping (Grad-CAM) function was also used to highlight the important ECG characteristics at specific time points that most impact the classification score.
CONCLUSION: In addition to achieving model performance results with our suggested DL method, Grad-CAM's implementation also offers vital temporal data that clinicians and other mental healthcare professionals can use to make wise medical judgments. We hope that by conducting this pilot study, we will be able to encourage larger-scale research with a larger biosignal dataset. Hence allowing biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings, as ECG can be easily obtained via wearable devices such as smartwatches.

Matched MeSH terms: Electrocardiography
Coronary CT angiography with single-source and dual-source CT: comparison of image quality and radiation dose between prospective ECG-triggered and retrospective ECG-gated protocols

Sabarudin A, Sun Z, Yusof AK

Int J Cardiol, 2013 Sep 30;168(2):746-53.
PMID: 23098849 DOI: 10.1016/j.ijcard.2012.09.217

This study is conducted to investigate and compare image quality and radiation dose between prospective ECG-triggered and retrospective ECG-gated coronary CT angiography (CCTA) with the use of single-source CT (SSCT) and dual-source CT (DSCT).

Matched MeSH terms: Electrocardiography/methods; Electrocardiography/standards*
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: Electrocardiography/methods*; Electrocardiography, Ambulatory
Linear and nonlinear analysis of normal and CAD-affected heart rate signals

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
Fulltext Left Bundle Branch Block Under General Anaesthesia in an Athlete's Heart

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

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