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Ectopic atrial activity underlies atrial tachycardia (AT), which, may predispose to atrial fibrillation (AF). Such abnormal excitation may be reflected as an alteration in the P-wave morphology (PWM) of the body surface potential (BSP). [ABSTRACT FROM PUBLISHER]

This study aimed to quantitatively link cardiac imaging and simultaneous physiological measurements. [ABSTRACT FROM PUBLISHER]

The fetal ECG (fECG) is one of the most valuable tools for monitoring the health of the fetus throughout pregnancy. However, its clinical use has been limited by the difficulty in analysing such non-invasive fECG recordings. [ABSTRACT FROM PUBLISHER]

Study of the mechanisms behind AF is typically based on invasive recording techniques. From a clinical point of view, it is highly desirable to have an alternative, noninvasive characterization of AF. Our aim was to investigate if the left atrium (LA) electrical activity can be seen preferentially in different sites on the surface ECG. Surface recordings of the ECG with 64 electrodes were obtained from 14 patients in atrial fibrillation (AF). After subtracting ventricular activity (QRST), power spectral density (PSD) of atrial activity was calculated using the Welch periodogram, sliding Hamming window (50% overlap). The dominant frequency (DF) was measured from the PSD in the frequency band of 4–8 Hz for each of the 64 channel surface ECGs. Recordings from the LA were obtained simultaneously from a pulmonary vein ablation catheter (PVAC). The DF of atrial activation was calculated from the PSD of the PVAC electrode number 3 (PVAC3) in the same manner as the surface ECG. Baseline recordings of 2 minute duration were analysed. DF differences were calculated between each 64 channels surface ECG and PVAC3. The Pearson's linear correlation was computed for PSD of each 64 channel surface ECG and PVAC3. The statistical difference between PSD of 32 front and 32 back channels surface ECG and PVAC3 was analyzed for both correlation and DF difference using the paired t-test. There was a highly significant difference between surface ECG and PVAC3 (p=0.00). [ABSTRACT FROM PUBLISHER]

The aim of this study was to develop a new algorithm to predict individual patient mortality with improved accuracy with respect to established methods from data collected over the first 48 hours of admission to the Intensive Care Unit. A binary classifier was developed to participate in Event 1 of the PhysioNet/Computing in Cardiology Challenge 2012. The algorithm development was undertaken using only posterior knowledge from the training dataset (Set-A), containing 41 demographic and clinical variables from 4000 ICU patients. For each variable a feature was defined as the average (across all available measurements of the given variable) likelihood of being part of the “survivors” group. To select features with highest discrimination ability (“survivors” vs. “non-survivors”), a forward sequential selection criterion with logistic cost function was adopted and repeated for cross-validation on N (=10) “leave Mout” (M=50%) random partitions of Set-A. Features that were selected in more than one partition were considered (#Feat = 32). A logistic regression model was used for classification. The score was defined as the lowest between sensitivity and positive predictive value in classification. The proposed method scored 54.9% on Set-A and 44.0% on the test set (Set-B), outperforming the established method SAPS-I (29.6% on Set-A, 31.7% on Set-B). [ABSTRACT FROM PUBLISHER]

The UP segment is the normal isoelectric reference level for ECG wave amplitude measurements but becomes obscured at high heart rates. The aim was to identify alternative reference levels suitable for use across a wide range of heart rates. 12-lead ECGs were recorded from 10 healthy subjects before and immediately following exercise. Amplitudes of the UP segment, Q wave, end of T wave (Tend) and zero voltage level, all relative to PQ level were measured from V3. The performances of beat waveform averaging (AvgBeat) and mathematical averaging of separate beat measurements (AvgVal) on reducing the influence of noise and measurement errors were compared. Due to merging of P and U waves at high heart rates, the UP segment amplitude was measureable in only approximately 71% when the heart rate was over 120 b/min. Both the UP segment amplitude and Tend amplitude tended to be overestimated at high heart rates. The standard deviations (SDs) were 0.02, 0.005, 0.021 and 0.016 mV for UP segment, Q wave, end of T wave (Tend) and zero voltage levels over the range of heart rates when using AvgVal. The SDs of amplitudes measured by AvgBeat and AvgVal methods were significantly lower than those measured from single beats (p < 0.05) for almost all features. Generally, these two methods achieved comparable performance on reducing measurement variability. [ABSTRACT FROM PUBLISHER]

The genesis of the U wave in the ECG is still in doubt. This study aimed to investigate the relationship between the occurrence of the U wave and both the electrical and mechanical timing sequence in heart beats. The M-mode echocardiography and 12-lead ECG were simultaneously recorded from 4 healthy subjects, with four repeat measurements in each. The ECG for both aortic valve (AV) and mitral valve (MV) were recorded from all four subjects. The timings of QQ interval, the peak and end of T wave, the peak and end of U wave, the AV and MV opening and closing were measured. The relationships between the QQ interval and the other timings were analysed. The MV opening and AV closing preceded the end of T wave (also considered as the start of U wave for healthy subjects) by 9 ± 11ms and 47 ± 15ms, respectively. When compared with the mechanical timings, the timing of the U wave had better correlation with the QQ interval, with the R square increasing from 0.76 to 0.95. Our finding suggests that the characteristics of the U wave may be more related to electrical activity than the mechanical events. [ABSTRACT FROM PUBLISHER]

For the application of acquiring ECGs from mobile telephones by unskilled users it would be beneficial if the mobile device could assess ECG quality and inform the user if the quality was acceptable. Using the PhysioNet/Computing in Cardiology Challenge 2011 dataset we identified several ECG features that were commonly observed in the training set ‘unacceptable’ category for algorithmic development: flat baseline (FB), saturation (SA), baseline drift (BD), low amplitude (LA), high amplitude (HA) and steep slope (SS). For the training set with each feature detection applied separately the following scores were achieved: FB 76.2%, SA 80.9%, BD 61.3%, LA 75.6%, HA 74.1% and SS 77.5%. With all features combined a score of 91.4% was achieved. For the test set the algorithm classified 181 records as unacceptable and 319 records as acceptable and the score was 85.7%. [ABSTRACT FROM PUBLISHER]

To separate the atrial (AA) from the ventricular (VA) electrical activity in surface ECG recordings of atrial fibrillation (AF), various methods have been proposed, such as QRS-T cancellation by beat-averaged template subtraction, and blind source separation (BSS). Although QRS-T cancellation is computationally more efficient than BSS, and allows the preservation of spatial information, it is sensitive to morphology changes, which produce large residuals in AA, biasing the frequency analysis. Aim of this study was: (i) to propose an improved approach to VA cancellation based on k-means morphology clustering (MC); (ii) to validate its ability to estimate AF dominant frequency (DF) on a standard database with intra-cardiac and surface ECG recordings (IAFDB, Physionet.org); (iii) to compare the temporal evolution of the spectral content of MC-estimated AA (MC-AA) with the one obtained from a reference BSS method based on Independent component analysis (ICA) and second-order blind identification (SOBI), in 14 body surface potential map (BSPM) recordings. QRS-T amplitude in MC-AA was significantly lower (p<0.001) than in ECG (in closest BSPM channel to V1). The validation on IAFDB showed no significant difference in DF estimation (p=0.546) in 17 recordings. Also no significant difference in DF estimation (p=0.208) with respect to the reference BSS method was observed. The proposed QRS-T cancellation method effectively suppresses VA and accurately estimates DF compared to an established BSS method. [ABSTRACT FROM PUBLISHER]

Atrial fibrillation (AF) dominant frequency (DF) is thought to reflect the degree of organisation of underlying atrial activity (AA). Our aim was to assess the changes in DF stage by stage throughout AF ablation. 64-lead body surface ECG was undertaken in 4 patients during AF ablation procedures. After QRST subtraction, DF was obtained from spectral analysis of the lead exhibiting the largest AF component (A22). 28 recordings of 2 minute duration obtained between each ablation were analysed for within and between recording changes in DF. Mean (SD) within recording DF difference was −0.023 (0.12) Hz. Mean (SD) between recording DF difference was −0.036(0.11) Hz. Within- and between-recording DF differences were not significant (Kruskal-Wallis ANOVA, p>0.05). This study shows that the changes in DF due to ablation could not be distinguished from the natural variability of the DF as measured by the within recording variability. [ABSTRACT FROM PUBLISHER]