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[EN] Atrial substrate alteration due to catheter ablation (CA) of atrial fibrillation (AF) is primarily assessed from P-waves. Nonetheless, how CA affects critical structures is ignored. The aim of the current study is to investigate if CA effect on CS, the principal CA reference, is related to that observed from P-waves analysis. Five-minute lead II and bipolar CS recordings of 29 paroxysmal AF patients were obtained before, during and after CA. Duration, amplitude, area and heart-rate (HR) variability (HRV) features were calculated for P-waves and local activation waves (LAWs). Normalization mitigated the effect of HR fluctuations. Linear correlations between each P-wave and LAW were tested with linear regression (LR) and Pearson correlation (PC) and nonlinear correlations with cross-quadratic sample entropy (CQSE). Correlation between the CA effect on P-waves and LAWs was investigated with PC. Negligent statistical correlations were found by PC and LR for amplitude and area (¿3.30%< corr < +18.36%, p < 0.0142). After normalization, correlation in duration increased from non-significant to significant and from low to moderate (up to 47.25%, p < 0.0001). CQSE values were from 0.6 to 1.2. The effect of CA on P-waves and LAWs duration showed a moderate/high concordance after normalization (from 50% to 89%, p < 0.0210) and a highly tuned HRV (> 90%, p < 0.0297). Apart from HRV, no significant correlations between CS LAWs and P-waves have been found. HR fluctuations mask any possible tuning and normalization should be applied prior to the analysis., Research supported by grants DPI2017¿83952¿C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2021/286 from GVA.

[EN] Despite its limited long-term success rate, catheter ablation (CA) is still the first-line treatment for patients suffering from persistent atrial fibrillation (AF). Preoperative prediction on the procedure¿s outcome is gaining clinical interest to provide optimized patient selection, limit repeated procedures, hospitalization rates, and treatment costs. To this respect, dominant frequency (DF) and amplitude of fibrillatory waves (f-waves) reflected on the electrocardiogram (ECG) recording have provided promising results. Hence, this work explores the ability of a novel set of frequency and amplitude features of the f-waves to improve CA outcome prediction. More precisely, in addition to DF and normalized f-wave amplitude (NFWA), three indices such as spectral entropy (SE), spectral flatness measure (SFM), and amplitude spectrum area (AMSA) were also analyzed. The results obtained for 204 6 s-length ECG intervals extracted from 51 persistent AF patients showed that all indices found statistically significant differences between the patients who maintained sinus rhythm and those who relapsed to AF after a follow-up of 9 months. However, they only obtained limited a discriminant ability ranging between 55 and 62%, which was overcome by 15¿23% when NFWA, SE and AMSA were combined. Consequently, this combination of frequency and amplitude features of the f-waves seems to provide new insights about the atrial substrate remodeling, which could be helpful in improving preoperative CA outcome prediction. Nonetheless, further studies are required in the future, This research has been supported by grants DPI2007-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana.

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.

[EN] In the last years, atrial fibrillation (AF) has become one of the most remarkable health problems in the developed world. This arrhythmia is associated with an increased risk of cardiovascular events, being its early detection an unresolved challenge. To palliate this issue, long-term wearable electrocardiogram (ECG) recording systems are used, because most of AF episodes are asymptomatic and very short in their initial stages. Unfortunately, portable equipments are very susceptible to be contaminated with different kind of noises, since they work in highly dynamics and ever-changing environments. Within this scenario, the correct identification of free-noise ECG segments results critical for an accurate and robust AF detection. Hence, this work presents a deep learning-based algorithm to identify high-quality intervals in single-lead ECG recordings obtained from patients with paroxysmal AF. The obtained results have provided a remarkable ability to classify between high- and low-quality ECG segments about 92%, only misclassifying around 7% of clean AF intervals as noisy segments. These outcomes have overcome most previous ECG quality assessment algorithms also dealing with AF signals by more than 20%., This research has been supported by the grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744.

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.

[EN] Detailed analysis of the electrocardiogram (ECG) recording allows to obtain relevant information about the heart state and its behavior. Indeed, the P-wave study has proven to be helpful in diagnosing many atrial electrical conduction disorders. However, manual delineation of this wave is a very time-consuming task, and a wide variety of automatic algorithms have been proposed for that purpose. The performance of these methods has been mostly validated on the QT database, since it contains manual annotations from clinical experts and is freely available at PhysioNet. Unfortunately, in the last years several authors have shown some major limitations of this database and this work hence presents a more robust validation of a previously published P-wave delineator. Thus, two novel databases which have been very recently made available to the scienti¿c community have been analyzed. The results show that the methods ability to detect P-waves is close to 100% in stable sinus rhythm episodes, but it decreases to 94% when the heart rhythm is irregular or consecutive P-waves exhibit notable morphological variations. Regarding the error between the annotations obtained automatically and those available as a reference, no relevant differences have been observed in terms of standard deviation, thus suggesting that the algorithm achieves a consistent delineation of the P-waves. Contrarily, a notable error has been noticed in average values, thus highlighting the need of a more extensive training for the P-wave delineator than that offered by the QT database., Research supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2019/036 from GVA.

Atrial fibrillation (AF) is characterised by rapid beat interval changes. The aim of the study was to investigate the effect of such changes on ECG ventricular repolarisation characteristics. In 10 AF recordings beat averaging of lead V4 was used to generate averaged T waves where the preceding beat interval (R-R) was either short (625+/−25 ms) or long (1075+/−25 ms). The amplitudes of T wave (T amp) and T wave end, defined as the TU nadir, (TUn amp), and the intervals for R wave to T wave peak (R-T) and R wave to T wave end (R-TUn) where measured from these average beats. Difference in measured T wave characteristics between short and long beat intervals were quantified. All measurements increased significantly for long preceding beat intervals compared to short: T amp (mean±SD) 0.31±0.17 mV (short) vs 0.35±0.20 mV (long) (p = 0.04); TUn amp 0.00±0.02 mV (short) vs 0.03±0.03 mV (long) (p = 0.009); R-T 251.7±13.5 ms (short) vs 264.2±12 ms (long) (p = 0.002) and R-TUn 376.5±31 ms (short) vs 392±26.5 ms (long) (p=0.027). ECG T wave characteristics are significantly affected by preceding ventricular beat interval in AF.

Major cardiac organisations recommend U wave abnormalities should be reported during ECG interpretation. However, U waves cannot be measured in patients with atrial fibrillation (AF) due to the obscuring fibrillatory wave. The aim was to validate a U wave measurement algorithm for AF patients. Multi-beat averaging was applied to ECGs of 25 patients during paroxysms of AF and the presence of U waves compared to those from the same patients during sinus rhythm (SR). In a further database of 10 long-term AF recordings, the number of beats for effective U wave extraction by the algorithm was calculated. U waves were revealed in all AF recordings and there was no significant difference between the presence of U waves in AF and SR (p = 0.88). U wave amplitude was significantly increased in AF (mean (s.d.) amplitude 55 (39) AF vs 37 (28) μV SR, p = 0.005). The presence of U waves could easily be discerned when as few as 10 beats were used in the algorithm. The study demonstrates the validity of the algorithm to reveal U waves in AF recordings. The algorithm offers the potential to detect U wave abnormalities in patients with AF.

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.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.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).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.

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.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.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.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).AFCL recurrence and stability indices could be used in selecting patients more likely to benefit from CA.

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 (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.

Atrial fibrillation (AF) is characterised by highly variable beat intervals. The aims of the study were to assess the accuracy of AF detection algorithms from short analysis durations and to validate prospectively the accuracy on a large community-based cohort of elderly subjects. Three algorithms for AF detection were evaluated: coefficient of variation (CV), mean successive difference (Δ) and coefficient of sample entropy (COSEn), using two databases of beat interval recordings: 167 recordings of 300 s duration for a range of rhythms acquired in a hospital setting and 2130 recordings of 10s duration acquired in the community. Using the longer recordings receiver operating characteristic (ROC) analysis was used to identify optimal algorithm thresholds and to evaluate analysis durations ranging from 5s to 60s. An ROC area of 93% was obtained at recording duration of 60s but remained above 90% for durations as low as 5s. Prospective analysis on the 2130 recordings gave AF detector sensitivities from 90.5% (CV and Δ) to 95.2% (COSEn), specificities from 89.3% (Δ) to 93.4% (COSEn) and accuracy from 89.3% (Δ) to 93.4% (COSEn), not significantly different to those obtained on the initial database. AF detection algorithms are effective for short analysis durations, offering the prospect of a simple and rapid diagnostic test based on beat intervals alone.

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. (PACE 2012;00:1–8)

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

Circadian variation in human ventricular fibrillation (VF) dominant frequency is unknown. If present this would provide evidence of physiological influence on VF. The objective was to quantify the circadian variation in human VF dominant frequency.Eight-lead Holter ECG recordings were obtained from a patient with severe myocarditis and chronic VF who was supported by a biventricular assist device. Recordings of up to 24h duration were obtained on 6 days with an average interval between recordings of 7 days. Dominant frequency and amplitude were obtained using spectral analysis and assessed for (i) circadian (ii) inter-recording and (iii) inter-lead differences.There was a significant circadian variation in amplitude (night: 0.027+/-0.004mVHz vs day: 0.044+/-0.006mVHz, p0.0001) but not dominant frequency (night: 7.85+/-0.62Hz vs day: 7.93+/-0.54Hz, p0.05). There were significant differences between recordings in dominant frequency which ranged from 6.80+/-0.29Hz to 8.36+/-0.38Hz (p0.0001) and dominant frequency spectral amplitude which ranged from 0.033+/-0.014mVHz to 0.043+/-0.017mVHz (p0.0001). Histograms of dominant frequencies in leads exhibited strikingly different distributions, particularly in V2 that was characterised by a bimodal distribution, while the other leads were characterised by predominantly unimodal distributions.VF dominant frequency spectral amplitude exhibited circadian variability. In a patient with severe myocarditis, supported with a biventricular assist device and in chronic VF, these results provide evidence for modulation of VF, probably induced by changes in posture and physical activity.

Aims The aim of this study was to assess the spatial and temporal variability of dominant fibrillation frequencies in a rare case of sustained human ventricular fibrillation (VF). Methods and results Body surface potential mapping was performed in a patient with sustained VF and who was supported by a biventricular assist device. Dominant frequencies at 54 body sites were calculated from two recordings obtained 38 days apart. Variability of dominant frequencies between recordings and across body sites was quantified. Median dominant frequencies within recordings varied between 6.1 and 7.2 Hz in recording 1 and 5.6 and 6.6 Hz in recording 2, indicating a significant reduction in dominant frequencies between the recordings ( P < 0.0001). Dominant frequencies differed across body sites by a mean (range) of 1.7 (0.4–2.8) Hz. Conclusion In this rare case of sustained VF, there was significant spatial and temporal variability of VF dominant frequencies. These findings should be considered in future ECG studies on VF where the spatial variability of dominant frequency might not otherwise have been considered.

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., Author summary Atrial tachy-arrhythmias are associated with irregular excitation waves arising from re-entrant excitation, multiple wavelets or rapid focal activity. Identifying the origin of the irregular activity may be vital for diagnosis and treatment of the disorder. Where invasive and non-invasive methods provide approaches for such identification, both have associated disadvantages. In this study, we used a biophysically detailed model of the human atria and torso to develop an algorithm based on the correlation between the electrocardiogram (ECG) signal from a 64-lead vest and the location of rapid focal and re-entrant excitation. Using the properties of the atrial activation and the ECG signals, we developed a focus-location algorithm which is able to distinguish rapid focal activity from re-entrant scroll waves centred in the same location. Based on simulated data, the algorithm had success rates of 93% and 76% for correctly identifying the origin of focal and re-entrant excitation, respectively, and 88% for distinguish focal and re-entrant excitation, with no false positives. Inherited from our previous algorithm, it is also easily generalizable to any multi-lead ECG system.

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

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 V1 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.

Recent guideline drafts of the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) underline the necessity to test nonantiarrhythmic drugs for their potential to prolong the QT or the corrected QT (QTc) interval. The implementation of these guidelines requires a large amount of ECG measurements on animals and humans in preclinical and clinical phases of the drug development process. We propose the use of magnetocardiography (MCG) as a complementary method with particular advantages in high-throughput studies, where signal quality and reliability are key factors. Our proposal is based on a review of recent MCG studies investigating the repolarization phase and results of methodological work assessing QT interval parameters from the MCG. The applicability of MCG for pre-clinical in-vivo studies is demonstrated by the ease of measurement in unrestrained non-anesthetized rabbits, guinea pigs, and hamsters..

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.

Our group has described previously the identification of arrhythmogenic pulmonary veins by rapid local electrical activations during atrial fibrillation. We have now investigated an algorithm for automated computer detection of this phenomenon from catheter electrodes in the upper pulmonary veins and assessed its performance in identifying arrhymogenic veins. Ten patients with persistent atrial fibrillation scheduled for pulmonary vein isolation at this hospital were studied. Electrogram recordings in the upper pulmonary veins were recorded and analyzed. Arrhythmogenic veins were identified by focal activity during sinus rhythm at electrophysiological studies. Recordings were visually assessed by a cardiologist for the presence of rapid repetitive electrical activations during atrial fibrillation. An index of rapid repetitive electrical activity (RREA index), the ratio of the number of activations with cycle lengths in the range 50 ms to 100 ms to the number of activations with cycle lengths in the range 100 ms to 200 ms, was devised to describe the extent of such activity automatically. The index was assessed as a predictor of arrhythmogenic veins. Electrograms from 19 upper pulmonary veins were recorded. Rapid activity was evident in 15 veins by visual manual assessment. The mean (range) automatic RREA index was 0.07 (0 to 0.16) for those identified as having no such activity manually, and 0.83 (0.22 to 1.68) for those identified with rapid activity (p

Multichannel magnetocardiography measures the magnetic field distribution of the human heart noninvasively from many sites over the body surface. Multichannel magnetocardiogram (MCG) analysis enables regional temporal differences in the distribution of cardiac magnetic field strength during depolarization and repolarization to be identified, allowing estimation of the global and local inhomogeneity of the cardiac activation process. The aim of this study was to compare the spatial distribution of cardiac magnetic field strength during ventricular depolarization and repolarization in both normal subjects and patients with cardiac abnormalities, obtaining amplitude measurements by magnetocardiography. MCGs were recorded at 49 sites over the heart from three normal subjects and two patients with inverted T-wave conditions. The magnetic field intensity during depolarization and repolarization was measured automatically for each channel and displayed spatially as contour maps. A Pearson correlation was used to determine the spatial relationship between the variables. For normal subjects, magnetic field strength maps during depolarization (R-wave) showed two asymmetric regions of magnetic field strength with a high positive value in the lower half of the chest and a high negative value above this. The regions of high R-wave amplitude corresponded spatially to concentrated asymmetric regions of high magnetic field strength during repolarization (T-wave). Pearson-r correlation coefficients of 0.7 (p The measurement of cardiac magnetic field distribution during depolarization and repolarization of normal subjects and patients with clinical abnormalities should enable the improvement of theoretical models for the explanation of the cardiac depolarization and repolarization processes.