Multi-Beat Averaging Reveals U Waves Are Ubiquitous and Standing Tall at Elevated Heart Rates Following Exercise.

The reporting of U wave abnormalities is clinically important, but the measurement of this small electrocardiographic (ECG) feature is extremely difficult, especially in challenging recording conditions, such as stress exercise, due to contaminating noise. Furthermore, it is widely stated that ECG U waves are rarely observable at heart rates greater than 90 bpm. The aims of the study were (i) to assess the ability of multi-beat averaging to reveal the presence of U waves in ECGs contaminated by noise following exercise and (ii) to quantify the effect of exercise on U wave amplitude. The multi-beat averaging algorithm was applied to recover U waves in 20 healthy subjects in pre- and post-exercise recordings. Average beats were generated from 30 beat epochs. The prevalence of U waves and their amplitudes were measured in pre- and post-exercise recordings and changes in amplitude due to exercise were quantified. U waves were present in all subjects in pre-exercise recordings. Following exercise, U waves could not be seen in standard ECG but were observable in all 20 subjects by multi-beat averaging and despite significantly increased mean (±SD) heart rate (63 ± 8 bpm vs. 100 ± 9 bpm, p < 0.0001). Furthermore, U waves were observable in all subjects with heart rates greater than 90 bpm. U waves significantly increased in amplitude following exercise (38 ± 15 µV vs. 80 ± 48 µV, p = 0.0005). Multi-beat averaging is effective at recovering U waves contaminated by noise due to exercise. U waves were measurable in all subjects, dispelling the myth that U waves are rarely seen at elevated heart rates. U waves exhibit increased amplitudes at elevated heart rates following exercise.

Heart sound classification from unsegmented phonocardiograms.

OBJECTIVE: Most algorithms for automated analysis of phonocardiograms (PCG) require segmentation of the signal into the characteristic heart sounds. The aim was to assess the feasibility for accurate classification of heart sounds on short, unsegmented recordings. APPROACH: PCG segments of 5 s duration from the PhysioNet/Computing in Cardiology Challenge database were analysed. Initially the 5 s segment at the start of each recording (seg 1) was analysed. Segments were zero-mean but otherwise had no pre-processing or segmentation. Normalised spectral amplitude was determined by fast Fourier transform and wavelet entropy by wavelet analysis. For each of these a simple single feature threshold-based classifier was implemented and the frequency/scale and thresholds for optimum classification accuracy determined. The analysis was then repeated using relatively noise free 5 s segments (seg 2) of each recording. Spectral amplitude and wavelet entropy features were then combined in a classification tree. MAIN RESULTS: There were significant differences between normal and abnormal recordings for both wavelet entropy and spectral amplitude across scales and frequency. In the wavelet domain the differences between groups were greatest at highest frequencies (wavelet scale 1, pseudo frequency 1 kHz) whereas in the frequency domain the differences were greatest at low frequencies (12 Hz). Abnormal recordings had significantly reduced high frequency wavelet entropy: (Median (interquartile range)) 6.63 (2.42) versus 8.36 (1.91), p < 0.0001, suggesting the presence of discrete high frequency components in these recordings. Abnormal recordings exhibited significantly greater low frequency (12 Hz) spectral amplitude: 0.24 (0.22) versus 0.09 (0.15), p < 0.0001. Classification accuracy (mean of specificity and sensitivity) was greatest for wavelet entropy: 76% (specificity 54%, sensitivity 98%) versus 70% (specificity 65%, sensitivity 75%) and was further improved by selecting the lowest noise segment (seg 2): 80% (specificity 65%, sensitivity 94%) versus 71% (specificity 63%, sensitivity 79%). Classification tree with combined features gave accuracy 79% (specificity 80%, sensitivity 77%). SIGNIFICANCE: The feasibility of accurate classification without segmentation of the characteristic heart sounds has been demonstrated. Classification accuracy is comparable to other algorithms but achieved without the complexity of segmentation.

Novel non-invasive algorithm to identify the origins of re-entry and ectopic foci in the atria from 64-lead ECGs: A computational study.

Atrial tachy-arrhytmias, such as atrial fibrillation (AF), are characterised by irregular electrical activity in the atria, generally associated with erratic excitation underlain by re-entrant scroll waves, fibrillatory conduction of multiple wavelets or rapid focal activity. Epidemiological studies have shown an increase in AF prevalence in the developed world associated with an ageing society, highlighting the need for effective treatment options. Catheter ablation therapy, commonly used in the treatment of AF, requires spatial information on atrial electrical excitation. The standard 12-lead electrocardiogram (ECG) provides a method for non-invasive identification of the presence of arrhythmia, due to irregularity in the ECG signal associated with atrial activation compared to sinus rhythm, but has limitations in providing specific spatial information. There is therefore a pressing need to develop novel methods to identify and locate the origin of arrhythmic excitation. Invasive methods provide direct information on atrial activity, but may induce clinical complications. Non-invasive methods avoid such complications, but their development presents a greater challenge due to the non-direct nature of monitoring. Algorithms based on the ECG signals in multiple leads (e.g. a 64-lead vest) may provide a viable approach. In this study, we used a biophysically detailed model of the human atria and torso to investigate the correlation between the morphology of the ECG signals from a 64-lead vest and the location of the origin of rapid atrial excitation arising from rapid focal activity and/or re-entrant scroll waves. A focus-location algorithm was then constructed from this correlation. The algorithm had success rates of 93% and 76% for correctly identifying the origin of focal and re-entrant excitation with a spatial resolution of 40 mm, respectively. The general approach allows its application to any multi-lead ECG system. This represents a significant extension to our previously developed algorithms to predict the AF origins in association with focal activities.

Accuracy of pulse interval timing in ambulatory blood pressure measurement.

Blood pressure (BP) monitors rely on pulse detection. Some blood pressure monitors use pulse timings to analyse pulse interval variability for arrhythmia screening, but this assumes that the pulse interval timings detected from BP cuffs are accurate compared with RR intervals derived from ECG. In this study we compared the accuracy of pulse intervals detected using an ambulatory blood pressure monitor (ABPM) with single lead ECG. Twenty participants wore an ABPM for three hours and a data logger which synchronously measured cuff pressure and ECG. RR intervals were compared with corresponding intervals derived from the cuff pressure tracings using three different pulse landmarks. Linear mixed effects models were used to assess differences between ECG and cuff pressure timings and to investigate the effect of potential covariates. In addition, the maximum number of successive oscillometric beats detectable in a measurement was assessed. From 243 BP measurements, the landmark at the foot of the oscillometric pulse was found to be associated with fewest covariates and had a random error of 9.5 ms. 99% of the cuff pressure recordings had more than 10 successive detectable oscillometric beats. RR intervals can be accurately estimated using an ABPM.

The atrial T wave: The elusive electrocardiographic wave exposed by a case of shifting atrial pacemaker.

The atrial T wave (Ta wave) is the body surface manifestation of atrial repolarisation and, unlike the P wave (atrial depolarisation), is little recognised. We report the case of a patient with shifting pacemaker which clearly demonstrates the effect of the Ta wave on ST segment and T wave. A simple conceptual model is used to explain the observed phenomenon. The case serves as a reminder of this often forgotten ECG wave and its potential effects on other ECG features.

Opportunistic detection of atrial fibrillation using blood pressure monitors: a systematic review.

BACKGROUND: Atrial fibrillation (AF) affects around 2% of the population and early detection is beneficial, allowing patients to begin potentially life-saving anticoagulant therapies. Blood pressure (BP) monitors may offer an opportunity to screen for AF. AIM: To identify and appraise studies which report the diagnostic accuracy of automated BP monitors used for opportunistic AF detection. METHODS: A systematic search was performed of the MEDLINE, MEDLINE In-Process and EMBASE literature databases. Papers were eligible if they described primary studies of the evaluation of a BP device for AF detection, were published in a peer-reviewed journal and reported values for the sensitivity and specificity. Included studies were appraised using the QUADAS-2 tool to assess their risk of bias and applicability to opportunistic AF detection. Values for the sensitivity and specificity of AF detection were extracted from each paper and compared. RESULTS AND CONCLUSIONS: We identified seven papers evaluating six devices from two manufacturers. Only one study scored low risk in all of the QUADAS-2 domains. All studies reported specificity >85% and 6 reported sensitivity >90%. The studies showed that BP devices with embedded algorithms for detecting arrhythmias show promise as screening tools for AF, comparing favourably with manual pulse palpation. But the studies used different methodologies and many were subject to potential bias. More studies are needed to more precisely define the sensitivity and specificity of opportunistic screening for AF during BP measurement before its clinical utility in the population of interest can be assessed fully.

Characteristics of the atrial repolarization phase of the ECG in paroxysmal atrial fibrillation patients and controls.

OBJECTIVE: The aim of this study is to characterize the observable segment of the atrial repolarization (Ta wave) of the standard ECG during sinus rhythm in paroxysmal atrial fibrillation (PAF) patients and controls. METHODS: Ta and P waves were measured from signal-averaged recordings of a standard 12-lead ECG in 40 patients, 20 with PAF, but in SR at the time of recording, and 20 healthy controls. Wave amplitudes and morphologies were measured. RESULTS: There were no significant differences in Ta amplitude between the PAF patients and controls. A subgroup analysis of patients on and off anti-arrhythmic drugs also showed no significant differences in Ta amplitudes. For both groups Ta wave had opposite polarity to the monophasic P wave. Biphasic P waves had Ta polarity opposite to the initial phase of the P wave. Ta wave amplitudes were largest in leads II (mean ± SD, ­25 ± 16 µV), V2 (­22 ± 10 µV), V3 (­21 ± 10 µV) and V4 (­20 ± 8 µV). A significant correlation was found between Ta and P wave amplitudes, leads recording larger P waves also had larger Ta waves (PAF group: r = 0.15 (P = 0.02) PAF vs r = 0.33 (P = 0.002) HC). CONCLUSION: No differences in the amplitude of the observable section of the atrial repolarization phase of the ECG could be observed between patients with PAF and controls. Ta wave correlates with the corresponding P wave in both amplitude and polarity.

Effect of catheter ablation on quality of life in patients with atrial fibrillation and its correlation with arrhythmia outcome.

OBJECTIVE: To assess the effect of catheter ablation on atrial fibrillation (AF) symptoms and quality of life (QoL). METHODS: Patients with AF scheduled for ablation were recruited. Pulmonary vein isolation (PVI) was performed and complex fractionated atrial electrogram (CFAE)±linear ablation undertaken in patients in AF despite PVI. QoL and AF symptoms were assessed using SF-36 V2 and Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaires before and 3 months after ablation. Change in QoL scores after ablation was correlated with clinical parameters and the extent of ablation. Magnitude of QoL change was compared between AFEQT and SF-36 physical component summary (PCS) and mental component summary (MCS) scores and correlated with arrhythmia outcome. RESULTS: 80 patients were studied. Summative and individual health scores for both AFEQT (51.5±22.0 vs 81.3±18.2; p<0.01) and SF-36 (PCS 43.3±10.5 vs 47.9±11.3; p<0.01 and MCS 45.0±11.5 vs 51.5±9.4; p<0.01) improved significantly in patients who maintained sinus rhythm after ablation, but not in those with recurrent AF. Improvement in AFEQT (25.4±19) was significantly greater than change in PCS (6.8±6.4; p<0.01) and MCS (8.5±7.9; p<0.01) scores and correlated more closely with arrhythmia outcome (AFEQT r=0.55; PCS r=0.26; MCS r=0.30). CONCLUSIONS: Patients who maintained sinus rhythm after ablation had a significant improvement in AF symptoms and QoL; however, no improvement was observed in patients with recurrent AF. QoL change after ablation did not correlate with baseline clinical parameters or ablation strategy. AF specific QoL scales are more responsive to change and correlate better with ablation outcome.

Principal component analysis of atrial fibrillation: inclusion of posterior ECG leads does not improve correlation with left atrial activity.

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.

A new algorithm to diagnose atrial ectopic origin from multi lead ECG systems--insights from 3D virtual human atria and torso.

Rapid atrial arrhythmias such as atrial fibrillation (AF) predispose to ventricular arrhythmias, sudden cardiac death and stroke. Identifying the origin of atrial ectopic activity from the electrocardiogram (ECG) can help to diagnose the early onset of AF in a cost-effective manner. The complex and rapid atrial electrical activity during AF makes it difficult to obtain detailed information on atrial activation using the standard 12-lead ECG alone. Compared to conventional 12-lead ECG, more detailed ECG lead configurations may provide further information about spatio-temporal dynamics of the body surface potential (BSP) during atrial excitation. We apply a recently developed 3D human atrial model to simulate electrical activity during normal sinus rhythm and ectopic pacing. The atrial model is placed into a newly developed torso model which considers the presence of the lungs, liver and spinal cord. A boundary element method is used to compute the BSP resulting from atrial excitation. Elements of the torso mesh corresponding to the locations of the placement of the electrodes in the standard 12-lead and a more detailed 64-lead ECG configuration were selected. The ectopic focal activity was simulated at various origins across all the different regions of the atria. Simulated BSP maps during normal atrial excitation (i.e. sinoatrial node excitation) were compared to those observed experimentally (obtained from the 64-lead ECG system), showing a strong agreement between the evolution in time of the simulated and experimental data in the P-wave morphology of the ECG and dipole evolution. An algorithm to obtain the location of the stimulus from a 64-lead ECG system was developed. The algorithm presented had a success rate of 93%, meaning that it correctly identified the origin of atrial focus in 75/80 simulations, and involved a general approach relevant to any multi-lead ECG system. This represents a significant improvement over previously developed algorithms.

Recurring patterns of atrial fibrillation in surface ECG predict restoration of sinus rhythm by catheter ablation.

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±10 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. non-terminating 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.

Extracting fetal heart beats from maternal abdominal recordings: selection of the optimal principal components.

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.

Recurring patterns in stationary intervals of abdominal uterine electromyograms during gestation.

Abdominal uterine electromyograms (uEMG) studies have focused on uterine contractions to describe the evolution of uterine activity and preterm birth (PTB) prediction. Stationary, non-contracting uEMG has not been studied. The aim of the study was to investigate the recurring patterns in stationary uEMG, their relationship with gestation age and PTB, and PTB predictivity. A public database of 300 (38 PTB) three-channel (S1-S3) uEMG recordings of 30 min, collected between 22 and 35 weeks' gestation, was used. Motion and labour contraction-free intervals in uEMG were identified as 5-min weak-sense stationarity intervals in 268 (34 PTB) recordings. Sample entropy (SampEn), percentage recurrence (PR), percentage determinism (PD), entropy (ER), and maximum length (L MAX) of recurrence were calculated and analysed according to the time to delivery and PTB. Random time series were generated by random shuffle (RS) of actual data. Recurrence was present in actual data (p<0.001) but not RS. In S3, PR (p<0.005), PD (p<0.01), ER (p<0.005), and L MAX (p<0.05) were higher, and SampEn lower (p<0.005) in PTB. Recurrence indices increased (all p<0.001) and SampEn decreased (p<0.01) with decreasing time to delivery, suggesting increasingly regular and recurring patterns with gestation progression. All indices predicted PTB with AUC≥0.62 (p<0.05). Recurring patterns in stationary non-contracting uEMG were associated with time to delivery but were relatively poor predictors of PTB.

Electrocardiographic reference values for a population of older adults in sub-Saharan Africa.

BACKGROUND/OBJECTIVES: Older adults in sub-Saharan Africa (SSA) are at greatest risk of an impending noncommunicable diseases epidemic, of which cardiac disease is the most prevalent contributor. Thus, it is essential to establish electrocardiographic reference values for a population that is likely to differ genetically and environmentally from others where reference values are established. METHODS: Two thousand two hundred thirty-two apparently healthy community-based participants without known cardiac disease aged 70+ in rural Tanzania underwent 12-lead electrocardiography. Electrocardiograms were digitally analyzed and gender-specific reference values for P duration (PD), P amplitude (PAMP), P area (PAREA), P terminal negative force (V1) (PTNF), PR interval, QRS duration (QRSD), QT/QTc, R amplitude (II, V5) (RAMP) LVH index (LVHI), R axis and R/S ratio (V1) reported, following univariate analysis of covariance using a multiple linear regression model, adjusting for age, systolic blood pressure (SBP), body mass index (BMI), and RR interval. RESULTS: Data from 1824 subjects were suitable for analysis. Adjusted mean values for men/women were: PD 115/110 ms, PAMP (avg) 123/114 µV, PAMP (II) 203/190 µV, PAREA (avg) 5.3/4.6 mV*s, PAREA (II) 9.3/8.1 mV*s, PTNF 1.7/1.4 mV*s, PR 158/152 ms, QRSD 89/84 ms, QT 370/375 ms, QTc 421/427 ms, RAMP (II) 805/854 µV, (V5) 2022/1742 µV, LVHI 3.0/2.8 mV (Sokolow-Lyon), 1.293/1.146 mV (Cornell), R axis 51/49°, R/S 0.2/0.2. Excluding PTNF , R axis and R/S ratio, all gender differences were significant (P < 0.001 apart from LVHI [Sokolow-Lyon; P < 0.005)] and RAMP (II) [P < 0.05]) following adjustment for age, SBP, BMI, and RR interval. CONCLUSIONS: Our description of comprehensive electrocardiographic parameters establishes reference values in this genetically and environmentally diverse SSA population thereby allowing identification of "outliers" with potential cardiac disease.

Non-Invasive Estimation Of Left Atrial Dominant Frequency In Atrial Fibrillation From Different Electrode Sites: Insight From Body Surface Potential Mapping.

The dominant driving sources of atrial fibrillation are often found in the left atrium, but the expression of left atrial activation on the body surface is poorly understood. Using body surface potential mapping and simultaneous invasive measurements of left atrial activation our aim was to describe the expression of the left atrial dominant fibrillation frequency across the body surface. 20 patients in atrial fibrillation were studied. The spatial distributions of the dominant atrial fibrillation frequency across anterior and posterior sites on the body surface were quantified. Their relationship with invasive left atrial dominant fibrillation frequency was assessed by linear regression analysis, and the coefficient of determination was calculated for each body surface site. The correlation between intracardiac and body surface dominant frequency was significantly higher with posterior compared with anterior sites (coefficient of determination 67±8% vs 48±2%, p<0.001). The site with largest coefficient of determination was 79.6% (p<0.001) and was a posterior site. In comparison with the site closest to lead V1 it had a coefficient of determination of 23.0% (p=0.033), and with the posterior body surface site closest to lead V9 had a coefficient of determination of 70.3% (p<0.001). Left atrial dominant fibrillation frequency was more closely represented at posterior body surface sites.

Characteristics of atrial fibrillation cycle length predict restoration of sinus rhythm by catheter ablation.

BACKGROUND: Successful termination of atrial fibrillation (AF) during catheter ablation (CA) is associated with arrhythmia-free follow-up. Preablation factors such as mean atrial fibrillation cycle length (AFCL) predict the likelihood of AF termination during ablation but recurring patterns and AFCL stability have not been evaluated. OBJECTIVE: To investigate novel predictors of acute and postoperative ablation outcomes from intracardiac electrograms: (1) recurring AFCL patterns and (2) localization index (LI) of the instantaneous fibrillatory rate distribution. METHODS: Sixty-two patients with AF (32 paroxysmal AF; 45 men; age 57 ± 10 years) referred for CA were enrolled. One-minute electrogram was recorded from coronary sinus (CS; 5 bipoles) and right atrial appendage (HRA; 2 bipoles). Atrial activations were detected automatically to derive the AFCL and instantaneous fibrillatory rate (inverse of AFCL) time series. Recurring AFCL patterns were quantified by using recurrence plot indices (RPIs): percentage determinism, entropy of determinism, and maximum diagonal length. AFCL stability was determined by using the LI. The CA outcome predictivity of individual indices was assessed. RESULTS: Patients with terminated atrial fibrillation (T-AF) had higher RPI (P < .05 in CS7-8) and LI than did those with nonterminated atrial fibrillation (P < .005 in CS3-4; P < .05 in CS5-6, CS7-8, and HRA). Patients free of arrhythmia after 3-month follow-up had higher RPI and LI (all P < .05 in CS7-8). All indices except percentage determinism predicted T-AF in CS7-8 (area under the curve [AUC] ≥ 0.71; odds ratio [OR] ≥ 4.50; P < .05). The median AFCL and LI predicted T-AF in HRAD (AUC ≥ 0.75; OR ≥ 7.76; P < .05). The RPI and LI predicted 3-month follow-up in CS7-8 (AUC ≥ 0.68; OR ≥ 4.17; P < .05). CONCLUSIONS: AFCL recurrence and stability indices could be used in selecting patients more likely to benefit from CA.

Evaluation of an algorithm based on single-condition decision rules for binary classification of 12-lead ambulatory ECG recording quality.

A new algorithm for classifying ECG recording quality based on the detection of commonly observed ECG contaminants which often render the ECG unusable for diagnostic purposes was evaluated. Contaminants (baseline drift, flat line, QRS-artefact, spurious spikes, amplitude stepwise changes, noise) were detected on individual leads from joint time-frequency analysis and QRS amplitude. Classification was based on cascaded single-condition decision rules (SCDR) that tested levels of contaminants against classification thresholds. A supervised learning classifier (SLC) was implemented for comparison. The SCDR and SLC algorithms were trained on an annotated database (Set A, PhysioNet Challenge 2011) of 'acceptable' versus 'unacceptable' quality recordings using the 'leave M out' approach with repeated random partitioning and cross-validation. Two training approaches were considered: (i) balanced, in which training records had equal numbers of 'acceptable' and 'unacceptable' recordings, (ii) unbalanced, in which the ratio of 'acceptable' to 'unacceptable' recordings from Set A was preserved. For each training approach, thresholds were calculated, and classification accuracy of the algorithm compared to other rule based algorithms and the SLC using a database for which classifications were unknown (Set B PhysioNet Challenge 2011). The SCDR algorithm achieved the highest accuracy (91.40%) compared to the SLC (90.40%) in spite of its simple logic. It also offers the advantage that it facilitates reporting of meaningful causes of poor signal quality to users.

Spatial pattern of P waves in paroxysmal atrial fibrillation patients in sinus rhythm and controls.

BACKGROUND: Measuring body surface potentials in the assessment of the electrical activity of the heart is the most commonly used noninvasive method for diagnosing cardiac arrhythmias. Paroxysmal atrial fibrillation (PAF) patients have disturbed cardiac electrophysiology but the detailed characteristics of atrial activation on the body surface are unknown. METHODS: P waves from 60 sites on the body surface were analyzed from 10 PAF patients in sinus rhythm (PAF group) and 10 healthy controls (HC group). Evolution of atrial depolarization was described qualitatively by maps of P-wave amplitudes. P-wave dipole evolution was described quantitatively by measuring the changing location (body site) and amplitude of the dipole positive and negative pole peaks. RESULTS: Both groups exhibited similar dipolar structure with an area of positive and an area of negative potentials. Over the depolarization cycle, there were significant changes in the location of the dipole with the positive pole rotating anteriorly right to left by two electrode sites (10 cm) (P = 0.001). There were significant differences between groups with the positive pole in PAF offset to the right of the chest by 0.43 (0.38) strips compared to HC (P < 0.007). Compared to controls, the PAF group positive poles reached peak amplitude sooner (49 [11] ms vs 65 [14] ms, P = 0.012) and negative poles reached peak amplitude later (74 [13] ms vs 62 [8] ms, P = 0.019). CONCLUSION: Atrial depolarization is characterized by a single dipole with time-varying amplitude and orientation with significant differences in dipole trajectory between patients with PAF and HCs.

Spatial complexity and spectral distribution variability of atrial activity in surface ECG recordings of atrial fibrillation.

Considerable research effort has been devoted to the estimation of the degree of organisation of atrial fibrillation (AF), to potentially support clinical decision making. The aims of this study were to: (1) analyse the temporal variability of spatial organisation (complexity) and spectral distribution of AF in body surface potential maps (BSPM), proposing an automated implementation of the analysis and (2) assess the applicability to reduced lead-sets. Twenty-one persistent AF recordings of 3 min each (64 BSPM: 32 anterior, 32 posterior) were analysed. The relationship between spatial organisation (C) and its variability (CV) was quantified on automatically delineated TQ segments. The relationship between spectral concentration (SC) and spectral variability (SV) was quantified on the atrial activity (AA) extracted using principal component analysis. Three different lead-sets: 64, 32 anterior and 10 anterior channels were considered. Significant (p < 0.001) correlation (ρ) was found: ρ(CV, C) ≥ 0.80, ρ(SC, SV) ≤-0.83 for all lead-sets. The results suggest that a higher degree of spatial organisation is associated with reduced variability of spatial organisation over time, and lower spectral variability associated with more prominent spectral peak in the AF frequency band (4-10 Hz).