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Fulltext Supervised Machine Learning Based Noninvasive Prediction of Atrial Flutter Mechanism from P-to-P Interval Variability under Imbalanced Dataset Conditions

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
Fulltext An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG)

Mandala S, Rizal A, Adiwijaya, Nurmaini S, Suci Amini S, Almayda Sudarisman G, et al.

PLoS One, 2024;19(4):e0297551.
PMID: 38593145 DOI: 10.1371/journal.pone.0297551

Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.

Matched MeSH terms: Electrocardiography/methods
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: Electrocardiography/methods
Dynamic ECG features for atrial fibrillation recognition

Abdul-Kadir NA, Mat Safri N, Othman MA

Comput Methods Programs Biomed, 2016 Nov;136:143-50.
PMID: 27686711 DOI: 10.1016/j.cmpb.2016.08.021

BACKGROUND: Atrial fibrillation (AF) can cause the formation of blood clots in the heart. The clots may move to the brain and cause a stroke. Therefore, this study analyzed the ECG features of AF and normal sinus rhythm signals for AF recognition which were extracted by using a second-order dynamic system (SODS) concept.
OBJECTIVE: To find the appropriate windowing length for feature extraction based on SODS and to determine a machine learning method that could provide higher accuracy in recognizing AF.
METHOD: ECG features were extracted based on a dynamic system (DS) that uses a second-order differential equation to describe the short-term behavior of ECG signals according to the natural frequency (ω), damping coefficient, (ξ), and forcing input (u). The extracted features were windowed into 2, 3, 4, 6, 8, and 10 second episodes to find the appropriate windowing size for AF signal processing. ANOVA and t-tests were used to determine the significant features. In addition, pattern recognition machine learning methods (an artificial neural network (ANN) and a support vector machine (SVM)) with k-fold cross validation (k-CV) were used to develop the ECG recognition system.
RESULTS: Significant differences (p

Matched MeSH terms: Electrocardiography/methods*
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: Electrocardiography/methods*
Person identification in irregular cardiac conditions using electrocardiogram signals

Sidek KA, Khalil I

Annu Int Conf IEEE Eng Med Biol Soc, 2011;2011:3772-5.
PMID: 22255160 DOI: 10.1109/IEMBS.2011.6090644

This paper presents a person identification mechanism in irregular cardiac conditions using ECG signals. A total of 30 subjects were used in the study from three different public ECG databases containing various abnormal heart conditions from the Paroxysmal Atrial Fibrillation Predicition Challenge database (AFPDB), MIT-BIH Supraventricular Arrthymia database (SVDB) and T-Wave Alternans Challenge database (TWADB). Cross correlation (CC) was used as the biometric matching algorithm with defined threshold values to evaluate the performance. In order to measure the efficiency of this simple yet effective matching algorithm, two biometric performance metrics were used which are false acceptance rate (FAR) and false reject rate (FRR). Our experimentation results suggest that ECG based biometric identification with irregular cardiac condition gives a higher recognition rate of different ECG signals when tested for three different abnormal cardiac databases yielding false acceptance rate (FAR) of 2%, 3% and 2% and false reject rate (FRR) of 1%, 2% and 0% for AFPDB, SVDB and TWADB respectively. These results also indicate the existence of salient biometric characteristics in the ECG morphology within the QRS complex that tends to differentiate individuals.

Matched MeSH terms: Electrocardiography/methods*
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*
Automated identification of normal and diabetes heart rate signals using nonlinear measures

Rajendra Acharya U, Faust O, Adib Kadri N, Suri JS, Yu W

Comput Biol Med, 2013 Oct;43(10):1523-9.
PMID: 24034744 DOI: 10.1016/j.compbiomed.2013.05.024

Diabetes mellitus (DM) affects considerable number of people in the world and the number of cases is increasing every year. Due to a strong link to the genetic basis of the disease, it is extremely difficult to cure. However, it can be controlled to prevent severe consequences, such as organ damage. Therefore, diabetes diagnosis and monitoring of its treatment is very important. In this paper, we have proposed a non-invasive diagnosis support system for DM. The system determines whether or not diabetes is present by determining the cardiac health of a patient using heart rate variability (HRV) analysis. This analysis was based on nine nonlinear features namely: Approximate Entropy (ApEn), largest Lyapunov exponet (LLE), detrended fluctuation analysis (DFA) and recurrence quantification analysis (RQA). Clinically significant measures were used as input to classification algorithms, namely AdaBoost, decision tree (DT), fuzzy Sugeno classifier (FSC), k-nearest neighbor algorithm (k-NN), probabilistic neural network (PNN) and support vector machine (SVM). Ten-fold stratified cross-validation was used to select the best classifier. AdaBoost, with least squares (LS) as weak learner, performed better than the other classifiers, yielding an average accuracy of 90%, sensitivity of 92.5% and specificity of 88.7%.

Matched MeSH terms: Electrocardiography/methods
Fulltext Classification of emotional states from electrocardiogram signals: a non-linear approach based on Hurst

Selvaraj J, Murugappan M, Wan K, Yaacob S

Biomed Eng Online, 2013;12:44.
PMID: 23680041 DOI: 10.1186/1475-925X-12-44

Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.

Matched MeSH terms: Electrocardiography/methods*
Newborn seizure detection based on heart rate variability

Malarvili MB, Mesbah M

IEEE Trans Biomed Eng, 2009 Nov;56(11):2594-603.
PMID: 19628449 DOI: 10.1109/TBME.2009.2026908

In this paper, we investigate the use of heart rate variability (HRV) for automatic newborn seizure detection. The proposed method consists of a sequence of processing steps, namely, obtaining HRV from the ECG, extracting a discriminating HRV feature set, selecting an optimal subset from the full feature set, and, finally, classifying the HRV into seizure/nonseizure using a supervised statistical classifier. Due to the fact that HRV signals are nonstationary, a set of time-frequency features from the newborn HRV is proposed and extracted. In order to achieve efficient HRV-based automatic newborn seizure detection, a two-phase wrapper-based feature selection technique is used to select the feature subset with minimum redundancy and maximum class discriminability. Tested on ECG recordings obtained from eight newborns with identified EEG seizure, the proposed HRV-based neonatal seizure detection algorithm achieved 85.7% sensitivity and 84.6% specificity. These results suggest that the HRV is sensitive to changes in the cardioregulatory system induced by the seizure, and therefore, can be used as a basis for an automatic seizure detection.

Matched MeSH terms: Electrocardiography/methods
Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things

Iqbal U, Wah TY, Habib Ur Rehman M, Mujtaba G, Imran M, Shoaib M

J Med Syst, 2018 Nov 05;42(12):252.
PMID: 30397730 DOI: 10.1007/s10916-018-1107-2

Electrocardiography (ECG) sensors play a vital role in the Internet of Medical Things, and these sensors help in monitoring the electrical activity of the heart. ECG signal analysis can improve human life in many ways, from diagnosing diseases among cardiac patients to managing the lifestyles of diabetic patients. Abnormalities in heart activities lead to different cardiac diseases and arrhythmia. However, some cardiac diseases, such as myocardial infarction (MI) and atrial fibrillation (Af), require special attention due to their direct impact on human life. The classification of flattened T wave cases of MI in ECG signals and how much of these cases are similar to ST-T changes in MI remain an open issue for researchers. This article presents a novel contribution to classify MI and Af. To this end, we propose a new approach called deep deterministic learning (DDL), which works by combining predefined heart activities with fused datasets. In this research, we used two datasets. The first dataset, Massachusetts Institute of Technology-Beth Israel Hospital, is publicly available, and we exclusively obtained the second dataset from the University of Malaya Medical Center, Kuala Lumpur Malaysia. We first initiated predefined activities on each individual dataset to recognize patterns between the ST-T change and flattened T wave cases and then used the data fusion approach to merge both datasets in a manner that delivers the most accurate pattern recognition results. The proposed DDL approach is a systematic stage-wise methodology that relies on accurate detection of R peaks in ECG signals, time domain features of ECG signals, and fine tune-up of artificial neural networks. The empirical evaluation shows high accuracy (i.e., ≤99.97%) in pattern matching ST-T changes and flattened T waves using the proposed DDL approach. The proposed pattern recognition approach is a significant contribution to the diagnosis of special cases of MI.

Matched MeSH terms: Electrocardiography/methods*
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: Electrocardiography/methods*
CT coronary angiogram in children with Kawasaki patients: experience in 52 patients

Borhanuddin BK, Abdul Latiff H, Mohamed Yusof AK

Cardiol Young, 2022 Dec;32(12):1994-1998.
PMID: 35707919 DOI: 10.1017/S1047951122000154

BACKGROUND: CT is an accepted non-invasive imaging tool to assess the coronary arteries in adults; however, its utilisation in children is limited by high heart rate and lack of standardised protocol. We sought to assess diagnostic quality and factors that affect image quality of CT in assessing coronary artery lesions in Kawasaki patients less than 18 years of age.
METHODOLOGY: CT coronary angiography was performed on patients with Kawasaki disease diagnosed with coronary aneurysm or suspected to have coronary stenosis. Studies were performed using electrocardiogram-gated protocols. General anaesthesia was used in patients who were not cooperative for breathing control. Heart rate, image quality, and effective radiation dose were documented.
RESULTS: Fifty-two Kawasaki patients underwent CT coronary angiography to assess coronary artery lesions. Median heart rate was 88 beats per minute (range 50-165 beats/minute). Image quality was graded as excellent in 34 (65%) patients, good in 17 (32%), satisfactory in 1, and poor in 1 patient. Coronary artery aneurysm was found in 25 (bilateral = 6, unilateral = 19, multiple = 11). Thrombus was found in 11 patients resulting in partial and total occlusion in 8 and 3 patients, respectively. Coronary stenosis was noted in 2 patients. The effective radiation dose was 1.296 millisievert (median 0.81 millisievert). Better diagnostic imaging quality was significantly related to lower heart rate (p = 0.007).
CONCLUSION: Electrocardiogram-triggered CT coronary angiography provides a good diagnostic assessment of coronary artery lesions in children with Kawasaki disease.

Matched MeSH terms: Electrocardiography/methods
The clinical benefit of cardiac resynchronization therapy for narrow QRS compared to broad QRS complex patients

Yap LB, Qadir F, Nguyen ST, Ma SK, Koh KW, Muhammad Z, et al.

Int J Cardiol, 2015 Mar 15;183:178-9.
PMID: 25666128 DOI: 10.1016/j.ijcard.2015.01.042
Matched MeSH terms: Electrocardiography/methods
Drowsiness detection during different times of day using multiple features

Sahayadhas A, Sundaraj K, Murugappan M

Australas Phys Eng Sci Med, 2013 Jun;36(2):243-50.
PMID: 23719977 DOI: 10.1007/s13246-013-0200-6

Driver drowsiness has been one of the major causes of road accidents that lead to severe trauma, such as physical injury, death, and economic loss, which highlights the need to develop a system that can alert drivers of their drowsy state prior to accidents. Researchers have therefore attempted to develop systems that can determine driver drowsiness using the following four measures: (1) subjective ratings from drivers, (2) vehicle-based measures, (3) behavioral measures and (4) physiological measures. In this study, we analyzed the various factors that contribute towards drowsiness. A total of 15 male subjects were asked to drive for 2 h at three different times of the day (00:00-02:00, 03:00-05:00 and 15:00-17:00 h) when the circadian rhythm is low. The less intrusive physiological signal measurements, ECG and EMG, are analyzed during this driving task. Statistically significant differences in the features of ECG and sEMG signals were observed between the alert and drowsy states of the drivers during different times of day. In the future, these physiological measures can be fused with vision-based measures for the development of an efficient drowsiness detection system.

Matched MeSH terms: Electrocardiography/methods*
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
Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals

Sudarshan VK, Acharya UR, Oh SL, Adam M, Tan JH, Chua CK, et al.

Comput Biol Med, 2017 04 01;83:48-58.
PMID: 28231511 DOI: 10.1016/j.compbiomed.2017.01.019

Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments.

Matched MeSH terms: Electrocardiography/methods*
Diastolic dysfunction grading, echocardiographic and electrocardiogram findings in 50 patients with apical hypertrophic cardiomyopathy

Aslannif R, Suraya K, Koh HB, Tey YS, Tan KL, Tham CH, et al.

Med J Malaysia, 2019 12;74(6):521-526.
PMID: 31929479

INTRODUCTION: Apical Hypertrophic Cardiomyopathy (Apical HCM) is an uncommon variant of hypertrophic cardiomyopathy, but it is relatively more common in Asian countries. This is a retrospective, non-randomised, single centre study of patients with Apical HCM focusing on their diastolic dysfunction grading, echocardiographic parameters and electrocardiograms (ECG).
METHODS: All Apical HCM patients coming for clinic visits at the Institut Jantung Negara from September 2017 to September 2018 were included. We assessed their echocardiography images, grade their diastolic function and reviewed their ECG on presentation.
RESULTS: Fifty patient were included, 82% (n=41) were males and 18% (n=9) females. The diastolic function grading of 37 (74%) patients were able to be determined using the updated 2016 American Society of Echocardiography (ASE) diastolic guidelines. Fifty percent (n=25) had the typical ace-ofspades shape left ventricle (LV) appearance in diastole and 12% (n=6) had apical pouch. All patients had T inversion in the anterior leads of their ECG, and only 52% (n=26) fulfilled the ECG left ventricular hypertrophy (LVH) criteria. Majority of our patients presented with symptoms of chest pain (52%, n=26) and dyspnoea (42%, n=21).
CONCLUSION: The updated 2016 ASE guideline makes it easier to evaluate LV diastolic function in most patients with Apical HCM. It also helps in elucidating the aetiology of dyspnoea, based on left atrial pressure. Clinicians should have a high index of suspicion for Apical HCM when faced with deep T inversion on ECG, in addition to a thick LV apex with an aceof- spades appearance during diastole.

Matched MeSH terms: Electrocardiography/methods*
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*
Fulltext A comparison of long-term outcomes between narrow and broad QRS complex patients treated with cardiac resynchronization therapy

Yap LB, Nguyen ST, Qadir F, Ma SK, Muhammad Z, Koh KW, et al.

Acta Cardiol, 2016 Jun;71(3):323-30.
PMID: 27594128 DOI: 10.2143/AC.71.3.3152093
Matched MeSH terms: Electrocardiography/methods*

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