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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: Atrial Fibrillation/physiopathology*
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: Atrial Fibrillation/physiopathology
Paroxysmal atrial fibrillation prediction method with shorter HRV sequences

Boon KH, Khalil-Hani M, Malarvili MB, Sia CW

Comput Methods Programs Biomed, 2016 Oct;134:187-96.
PMID: 27480743 DOI: 10.1016/j.cmpb.2016.07.016

This paper proposes a method that predicts the onset of paroxysmal atrial fibrillation (PAF), using heart rate variability (HRV) segments that are shorter than those applied in existing methods, while maintaining 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 stabilize (electrically) and prevent the onset of atrial arrhythmias with different pacing techniques. We investigate the effect of HRV features extracted from different lengths of HRV segments prior to PAF onset with the proposed PAF prediction method. The pre-processing stage of the predictor includes QRS detection, HRV quantification and ectopic beat correction. Time-domain, frequency-domain, non-linear and bispectrum features are then extracted from the quantified HRV. In the feature selection, the HRV feature set and classifier parameters are optimized simultaneously using an optimization procedure based on genetic algorithm (GA). Both full feature set and statistically significant feature subset are optimized by GA respectively. For the statistically significant feature subset, Mann-Whitney U test is used to filter non-statistical significance features that cannot pass the statistical test at 20% significant level. The final stage of our predictor is the classifier that is based on support vector machine (SVM). A 10-fold cross-validation is applied in performance evaluation, and the proposed method achieves 79.3% prediction accuracy using 15-minutes HRV segment. This accuracy is comparable to that achieved by existing methods that use 30-minutes HRV segments, most of which achieves accuracy of around 80%. More importantly, our method significantly outperforms those that applied segments shorter than 30 minutes.

Matched MeSH terms: Atrial Fibrillation/physiopathology*
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: Atrial Fibrillation/physiopathology*
Flickering of cardiac state before the onset and termination of atrial fibrillation

Lan BL, Liew YW, Toda M, Kamsani SH

Chaos, 2020 May;30(5):053137.
PMID: 32491883 DOI: 10.1063/1.5130524

Complex dynamical systems can shift abruptly from a stable state to an alternative stable state at a tipping point. Before the critical transition, the system either slows down in its recovery rate or flickers between the basins of attraction of the alternative stable states. Whether the heart critically slows down or flickers before it transitions into and out of paroxysmal atrial fibrillation (PAF) is still an open question. To address this issue, we propose a novel definition of cardiac states based on beat-to-beat (RR) interval fluctuations derived from electrocardiogram data. Our results show the cardiac state flickers before PAF onset and termination. Prior to onset, flickering is due to a "tug-of-war" between the sinus node (the natural pacemaker) and atrial ectopic focus/foci (abnormal pacemakers), or the pacing by the latter interspersed among the pacing by the former. It may also be due to an abnormal autonomic modulation of the sinus node. This abnormal modulation may be the sole cause of flickering prior to termination since atrial ectopic beats are absent. Flickering of the cardiac state could potentially be used as part of an early warning or screening system for PAF and guide the development of new methods to prevent or terminate PAF. The method we have developed to define system states and use them to detect flickering can be adapted to study critical transition in other complex systems.

Matched MeSH terms: Atrial Fibrillation/physiopathology*
Atrial fibrillation classification and association between the natural frequency and the autonomic nervous system

Abdul-Kadir NA, Mat Safri N, Othman MA

Int J Cardiol, 2016 Nov 01;222:504-8.
PMID: 27505342 DOI: 10.1016/j.ijcard.2016.07.196

BACKGROUND: The feasibility study of the natural frequency (ω) obtained from a second-order dynamic system applied to an ECG signal was discovered recently. The heart rate for different ECG signals generates different ω values. The heart rate variability (HRV) and autonomic nervous system (ANS) have an association to represent cardiovascular variations for each individual. This study further analyzed the ω for different ECG signals with HRV for atrial fibrillation classification.
METHODS: This study used the MIT-BIH Normal Sinus Rhythm (nsrdb) and MIT-BIH Atrial Fibrillation (afdb) databases for healthy human (NSR) and atrial fibrillation patient (N and AF) ECG signals, respectively. The extraction of features was based on the dynamic system concept to determine the ω of the ECG signals. There were 35,031 samples used for classification.
RESULTS: There were significant differences between the N & NSR, N & AF, and NSR & AF groups as determined by the statistical t-test (p<0.0001). There was a linear separation at 0.4s(-1) for ω of both databases upon using the thresholding method. The feature ω for afdb and nsrdb falls within the high frequency (HF) and above the HF band, respectively. The feature classification between the nsrdb and afdb ECG signals was 96.53% accurate.
CONCLUSIONS: This study found that features of the ω of atrial fibrillation patients and healthy humans were associated with the frequency analysis of the ANS during parasympathetic activity. The feature ω is significant for different databases, and the classification between afdb and nsrdb was determined.

Matched MeSH terms: Atrial Fibrillation/physiopathology*
Patient selection, pacing indications, and subsequent outcomes with de novo leadless single-chamber VVI pacing

Piccini JP, Stromberg K, Jackson KP, Kowal RC, Duray GZ, El-Chami MF, et al.

Europace, 2019 Nov 01;21(11):1686-1693.
PMID: 31681964 DOI: 10.1093/europace/euz230

AIMS: Patient selection is a key component of securing optimal patient outcomes with leadless pacing. We sought to describe and compare patient characteristics and outcomes of Micra patients with and without a primary pacing indication associated with atrial fibrillation (AF) in the Micra IDE trial.
METHODS AND RESULTS: The primary outcome (risk of cardiac failure, pacemaker syndrome, or syncope related to the Micra system or procedure) was compared between successfully implanted patients from the Micra IDE trial with a primary pacing indication associated with AF or history of AF (AF group) and those without (non-AF group). Among 720 patients successfully implanted with Micra, 228 (31.7%) were in the non-AF group. Reasons for selecting VVI pacing in non-AF patients included an expectation for infrequent pacing (66.2%) and advanced age (27.2%). More patients in the non-AF group had a condition that precluded the use of a transvenous pacemaker (9.6% vs. 4.7%, P = 0.013). Atrial fibrillation patients programmed to VVI received significantly more ventricular pacing compared to non-AF patients (median 67.8% vs. 12.6%; P

Matched MeSH terms: Atrial Fibrillation/physiopathology
Fulltext Age-dependent atrial arrhythmic phenotype secondary to mitochondrial dysfunction in Pgc-1β deficient murine hearts

Valli H, Ahmad S, Chadda KR, Al-Hadithi ABAK, Grace AA, Jeevaratnam K, et al.

Mech Ageing Dev, 2017 Oct;167:30-45.
PMID: 28919427 DOI: 10.1016/j.mad.2017.09.002

INTRODUCTION: Ageing and several age-related chronic conditions including obesity, insulin resistance and hypertension are associated with mitochondrial dysfunction and represent independent risk factors for atrial fibrillation (AF).
MATERIALS AND METHODS: Atrial arrhythmogenesis was investigated in Langendorff-perfused young (3-4 month) and aged (>12 month), wild type (WT) and peroxisome proliferator activated receptor-γ coactivator-1β deficient (Pgc-1β-/-) murine hearts modeling age-dependent chronic mitochondrial dysfunction during regular pacing and programmed electrical stimulation (PES).
RESULTS AND DISCUSSION: The Pgc-1β-/- genotype was associated with a pro-arrhythmic phenotype progressing with age. Young and aged Pgc-1β-/- hearts showed compromised maximum action potential (AP) depolarization rates, (dV/dt)max, prolonged AP latencies reflecting slowed action potential (AP) conduction, similar effective refractory periods and baseline action potential durations (APD90) but shortened APD90 in APs in response to extrasystolic stimuli at short stimulation intervals. Electrical properties of APs triggering arrhythmia were similar in WT and Pgc-1β-/- hearts. Pgc-1β-/- hearts showed accelerated age-dependent fibrotic change relative to WT, with young Pgc-1β-/- hearts displaying similar fibrotic change as aged WT, and aged Pgc-1β-/- hearts the greatest fibrotic change. Mitochondrial deficits thus result in an arrhythmic substrate, through slowed AP conduction and altered repolarisation characteristics, arising from alterations in electrophysiological properties and accelerated structural change.

Matched MeSH terms: Atrial Fibrillation/physiopathology*
Fulltext Biatrial thrombi resembling myxoma regressed after prolonged anticoagulation in a patient with mitral stenosis: a case report

Lu HT, Nordin R, Othman N, Choy CN, Kam JY, Leo BC, et al.

J Med Case Rep, 2016 Aug 10;10(1):221.
PMID: 27510438 DOI: 10.1186/s13256-016-1018-0

BACKGROUND: Many cases of cardiac masses have been reported in the literature, but in this case report we described a rare case of biatrial cardiac mass that represented a challenge for diagnosis and therapy. The differentiation between cardiac masses such as thrombi, vegetations, myxomas and other tumors is not always straightforward and an exact diagnosis is important because of its distinct treatment strategy. Transthoracic/esophageal echocardiography and cardiac magnetic resonance play an important role in establishing the diagnosis of cardiac masses. However, no current noninvasive diagnostic tool has the ability to absolutely diagnose cardiac masses; obtaining a pathological specimen by surgical resection of cardiac masses is the only reliable method to diagnose cardiac masses accurately. Our case report is an exception in that the final diagnosis was affirmed by empirical anticoagulation therapy based on clinical judgment and noninvasive characterization of biatrial mass.
CASE PRESENTATION: We described a 54-year-old Malay man with severe mitral stenosis and atrial fibrillation who presented with a biatrial mass. Transthoracic/esophageal echocardiography and cardiac magnetic resonance detected a large, homogeneous right atrial mass typical of a thrombus, and a left atrial mass adhering to interatrial septum that mimicked atrial myxoma. The risk factors, morphology, location, and characteristics of the biatrial cardiac mass indicated a diagnosis of thrombi. However, our patient declined surgery. As a result, the nature of his cardiac masses was not specified by histology. Of note, his left atrial mass was completely regressed by long-term warfarin, leaving a residual right atrial mass. Thus, we affirmed the most probable diagnosis of cardiac thrombi. During the course of treatment, he had an episode of non-fatal ischemic stroke most probably because of a thromboembolism.
CONCLUSIONS: Noninvasive characterization of cardiac mass is essential in clarifying the diagnosis and directing treatment strategy. Anticoagulation is a feasible treatment when the clinical assessment, risk factors, and imaging findings indicate a diagnosis of thrombi. After prolonged anticoagulation therapy, complete resolution of biatrial thrombi was achievable in our case.

Matched MeSH terms: Atrial Fibrillation/physiopathology
Fulltext Joint Associations Between Body Mass Index and Waist Circumference With Atrial Fibrillation in Men and Women

Poorthuis MHF, Sherliker P, de Borst GJ, Carter JL, Lam KBH, Jones NR, et al.

J Am Heart Assoc, 2021 04 20;10(8):e019025.
PMID: 33853362 DOI: 10.1161/JAHA.120.019025

Background Associations between adiposity and atrial fibrillation (AF) might differ between sexes. We aimed to determine precise estimates of the risk of AF by body mass index (BMI) and waist circumference (WC) in men and women. Methods and Results Between 2008 and 2013, over 3.2 million adults attended commercial screening clinics. Participants completed health questionnaires and underwent physical examination along with cardiovascular investigations, including an ECG. We excluded those with cardiovascular and cardiac disease. We used multivariable logistic regression and determined joint associations of BMI and WC and the risk of AF in men and women by comparing likelihood ratio χ2 statistics. Among 2.1 million included participants 12 067 (0.6%) had AF. A positive association between BMI per 5 kg/m2 increment and AF was observed, with an odds ratio of 1.65 (95% CI, 1.57-1.73) for men and 1.36 (95% CI, 1.30-1.42) for women among those with a BMI above 20 kg/m2. We found a positive association between AF and WC per 10 cm increment, with an odds ratio of 1.47 (95% CI, 1.36-1.60) for men and 1.37 (95% CI, 1.26-1.49) for women. Improvement of likelihood ratio χ2 was equal after adding BMI and WC to models with all participants. In men, WC showed stronger improvement of likelihood ratio χ2 than BMI (30% versus 23%). In women, BMI showed stronger improvement of likelihood ratio χ2 than WC (23% versus 12%). Conclusions We found a positive association between BMI (above 20 kg/m2) and AF and between WC and AF in both men and women. BMI seems a more informative measure about risk of AF in women and WC seems more informative in men.

Matched MeSH terms: Atrial Fibrillation/physiopathology

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