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Background: Lead V1 is routinely analysed due to its large amplitude AF waveform. V1 correlates strongly with right atrial activity but only moderately with left atrial activity. Posterior lead V9 correlates strongest with left atrial activity., Aims: (1) To establish whether surface dominant AF frequency (DAF) calculated using principal component analysis (PCA) of a modified 12-lead ECG (including posterior leads) has a stronger correlation with left atrial activity compared to the standard ECG. (2) To assess the contribution of individual ECG leads to the AF principal component in both ECG configurations., Methods: Patients were assigned to modified or standard ECG groups. In the modified ECG, posterior leads V8 and V9 replaced V4 and V6. AF waveform was extracted from one-minute surface ECG recordings using PCA. Surface DAF was correlated with intracardiac DAF from the high right atrium (HRA), coronary sinus (CS) and pulmonary veins (PVs)., Results: 96 patients were studied. Surface DAF from the modified ECG did not have a stronger correlation with left atrial activity compared to the standard ECG. Both ECG configurations correlated strongly with HRA, CS and right PVs but only moderately with left PVs. V1 contributed most to the AF principal component in both ECG configurations., (Copyright © 2015. Published by Elsevier Ltd.)

This study presents a systematic comparison of different approaches to the automated selection of the principal components (PC) which optimise the detection of maternal and fetal heart beats from non-invasive maternal abdominal recordings.A public database of 75 4-channel non-invasive maternal abdominal recordings was used for training the algorithm. Four methods were developed and assessed to determine the optimal PC: (1) power spectral distribution, (2) root mean square, (3) sample entropy, and (4) QRS template. The sensitivity of the performance of the algorithm to large-amplitude noise removal (by wavelet de-noising) and maternal beat cancellation methods were also assessed. The accuracy of maternal and fetal beat detection was assessed against reference annotations and quantified using the detection accuracy score F1 [2*PPV*Se / (PPV + Se)], sensitivity (Se), and positive predictive value (PPV). The best performing implementation was assessed on a test dataset of 100 recordings and the agreement between the computed and the reference fetal heart rate (fHR) and fetal RR (fRR) time series quantified.The best performance for detecting maternal beats (F1 99.3%, Se 99.0%, PPV 99.7%) was obtained when using the QRS template method to select the optimal maternal PC and applying wavelet de-noising. The best performance for detecting fetal beats (F1 89.8%, Se 89.3%, PPV 90.5%) was obtained when the optimal fetal PC was selected using the sample entropy method and utilising a fixed-length time window for the cancellation of the maternal beats. The performance on the test dataset was 142.7 beats(2)/min(2) for fHR and 19.9 ms for fRR, ranking respectively 14 and 17 (out of 29) when compared to the other algorithms presented at the Physionet Challenge 2013.

An algorithm for analyzing changes in ECG morphology based on principal component analysis (PCA) is presented and applied to the derivation of surrogate respiratory signals from single-lead ECGs. The respiratory-induced variability of ECG features, P waves, QRS complexes, and T waves are described by the PCA. We assessed which ECG features and which principal components yielded the best surrogate for the respiratory signal. Twenty subjects performed controlled breathing for 180 s at 4, 6, 8, 10, 12, and 14 breaths per minute and normal breathing. ECG and breathing signals were recorded. Respiration was derived from the ECG by three algorithms: the PCA-based algorithm and two established algorithms, based on RR intervals and QRS amplitudes. ECG-derived respiration was compared to the recorded breathing signal by magnitude squared coherence and cross-correlation. The top ranking algorithm for both coherence and correlation was the PCA algorithm applied to QRS complexes. Coherence and correlation were significantly larger for this algorithm than the RR algorithm(p < 0.05 and p < 0.0001, respectively) but were not significantly different from the amplitude algorithm. PCA provides a novel algorithm for analysis of both respiratory and nonrespiratory related beat-to-beat changes in different ECG features.

Analysis of atrial rhythm is important in the treatment and management of patients with atrial fibrillation. Several algorithms exist for extracting the atrial signal from the electrocardiogram (ECG) in atrial fibrillation, but there are few reports on how well these techniques are able to recover the atrial signal. We assessed and compared three algorithms for extracting the atrial signal from the 12-lead ECG. The 12-lead ECGs of 30 patients in atrial fibrillation were analyzed. Atrial activity was extracted by three algorithms, Spatiotemporal QRST cancellation (STC), principal component analysis (PCA), and independent component analysis (ICA). The amplitude and frequency characteristics of the extracted atrial signals were compared between algorithms and against reference data. Mean (standard deviation) amplitude of QRST segments of VI was 0.99 (0.54) mV, compared to 0.18 (0.11) mV (STC), 0.19 (0.13) mV (PCA), and 0.29 (0.22) mV (ICA). Hence, for all algorithms there were significant reductions in the amplitude of the ventricular activity compared with that in V1. Reference atrial signal amplitude in V1 was 0.18 (0.11) mV, compared to 0.17 (0.10) mV (STC), 0.12 (0.09) mV (PCA), and 0.18 (0.13) mV (ICA) in the extracted atrial signals. PCA tended to attenuate the atrial signal in these segments. There were no significant differences for any of the algorithms when comparing the amplitude of the reference atrial signal with that of the extracted atrial signals in segments in which ventricular activity had been removed. There were no significant differences between algorithms in the frequency characteristics of the extracted atrial signals. There were discrepancies in amplitude and frequency characteristics of the atrial signal in only a few cases resulting from notable residual ventricular activity for PCA and ICA algorithms. In conclusion, the extracted atrial signals from these algorithms exhibit very similar amplitude and frequency characteristics. Users of these algorithms should be observant of residual ventricular activities which can affect the analysis of the fibrillatory waveform in clinical practice. [ABSTRACT FROM AUTHOR]

Atrial Fibrillation Frequency Analysis. Introduction: Our group has shown previously that measurements of atrial frequency can be obtained from surface 12-lead ECG recordings of patients during atrial fibrillation (AF), using a combination of principal component and Fourier transform algorithms. Such measurements are reproducible over time and change with drug manipulation of the arrhythmia. Aims: To determine whether linear left atrial ablation, using a combination of “roof” and “mitral isthmus” lines results in changes in surface atrial frequency during AF and to assess the contribution of each individual line when sited sequentially. Methods and Results: Computerized recordings from 26 patients, who had undergone linear ablation procedures for AF, were reviewed. The atrial signal was extracted from the 12-lead ECG data by principal component analysis and the main frequency component identified using Fourier analysis. Atrial frequency before and after these two standard ablation lines was compared. Atrial frequency decreased significantly after the combination of roof and mitral isthmus lines (5.66 vs 5.15 Hz) and when either roof (5.61 vs 5.13 Hz) or mitral isthmus (5.89 vs 5.75 Hz) lines were sited first. However, only the roof line led to a significant reduction in atrial frequency when sited second (5.64 vs 5.49 Hz). Conclusions: Measurements of atrial frequency can be obtained from surface 12-lead ECG recordings during AF and change as predicted in response to linear left atrial ablation. This technique may be useful in assessing antiarrhythmic treatments for AF. (J Cardiovasc Electrophysiol, Vol. 16, pp. 1-7, August 2005) [ABSTRACT FROM AUTHOR]

Atrial Fibrillation Frequency Analysis.. Introduction:The aims of this study were to evaluate (1) principal component analysis as a technique for extracting the atrial signal waveform from the standard 12-lead ECG and (2) its ability to distinguish changes in atrial fibrillation (AF) frequency parameters over time and in response to pharmacologic manipulation using drugs with different effects on atrial electrophysiology.Methods and Results:Twenty patients with persistent AF were studied. Continuous 12-lead Holter ECGs were recorded for 60 minutes, first, in the drug-free state. Mean and variability of atrial waveform frequency were measured using an automated computer technique. This extracted the atrial signal by principal component analysis and identified the main frequency component using Fourier analysis. Patients were then allotted sequentially to receive 1 of 4 drugs intravenously (amiodarone, flecainide, sotalol, or metoprolol), and changes induced in mean and variability of atrial waveform frequency measured. Mean and variability of atrial waveform frequency did not differ within patients between the two 30-minute sections of the drug-free state. As hypothesized, significant changes in mean and variability of atrial waveform frequency were detected after manipulation with amiodarone (mean: 5.77 vs 4.86 Hz; variability: 0.55 vs 0.31 Hz), flecainide (mean: 5.33 vs 4.72 Hz; variability: 0.71 vs 0.31 Hz), and sotalol (mean: 5.94 vs 4.90 Hz; variability: 0.73 vs 0.40 Hz) but not with metoprolol (mean: 5.41 vs 5.17 Hz; variability: 0.81 vs 0.82 Hz).Conclusion:A technique for continuously analyzing atrial frequency characteristics of AF from the surface ECG has been developed and validated.(J Cardiovasc Electrophysiol, Vol. 15, pp. 1-6, September 2004) [ABSTRACT FROM AUTHOR]

Background: Non-invasive tools to help identify patients likely to benefit from catheter ablation (CA) of atrial fibrillation (AF) would facilitate personalised treatment planning. Aim: To investigate atrial waveform organisation through recurrence plot indices (RPI) and their ability to predict CA outcome. Methods: One minute 12-lead ECG was recorded before CA from 62 patients with AF (32 paroxysmal AF; 45 men; age 57 + 1 0 years). Organisation of atrial waveforms from i) TQ intervals in V1 and ii) QRST suppressed continuous AF waveforms (CAFW), were quantified using RPI: percentage recurrence (PR), percentage determinism (PD), entropy of recurrence (ER). Ability to predict acute (terminating vs. nonterminating AF), 3-month and 6-month postoperative outcome (AF vs. AF free) were assessed. Results: RPI either by TQ or CAFW analysis did not change significantly with acute outcome. Patients arrhythmia-free at 6-month follow-up had higher organisation in TQ intervals by PD (p < 0.05) and ER (p < 0.005) and both were significant predictors of 6-month outcome (PD (AUC=0.67, p < 0.05) and ER (AUC=0.72, p < 0.005)). For paroxysmal AF cases, all RPI predicted 3-month (AUC(ER) = 0.78, p < 0.05; AUC(PD) = 0.79, p < 0.05; AUC(PR)=0.80, p < 0.01) and 6-month (AUC(ER)=0.81, p< 0.005; AUC(PD) = 0.75, p < 0.05; AUC(PR)=0.71, p < 0.05) outcome. CAFW-derived RPIs did not predict acute or postoperative outcomes. Higher values of any RPI from TQ (values greater than 25th percentile of preoperative distribution) were associated with decreased risk of AF relapse at follow-up (hazard ratio ≤ 0.52, all p < 0.05). Conclusions: Recurring patterns from preprocedural 1-minute recordings of ECG TQ intervals were significant predictors of CA 6-month outcome. [ABSTRACT FROM AUTHOR]