Different Ventricular Fibrillation Types in Low-Dimensional Latent Spaces

The causes of ventricular fibrillation (VF) are not yet elucidated, and it has been proposed that different mechanisms might exist. Moreover, conventional analysis methods do not seem to provide time or frequency domain features that allow for recognition of different VF patterns in electrode-recorded biopotentials. The present work aims to determine whether low-dimensional latent spaces could exhibit discriminative features for different mechanisms or conditions during VF episodes. For this purpose, manifold learning using autoencoder neural networks was analyzed based on surface ECG recordings. The recordings covered the onset of the VF episode as well as the next 6 min, and comprised an experimental database based on an animal model with five situations, including control, drug intervention (amiodarone, diltiazem, and flecainide), and autonomic nervous system blockade. The results show that latent spaces from unsupervised and supervised learning schemes yielded moderate though quite noticeable separability among the different types of VF according to their type or intervention. In particular, unsupervised schemes reached a multi-class classification accuracy of 66%, while supervised schemes improved the separability of the generated latent spaces, providing a classification accuracy of up to 74%. Thus, we conclude that manifold learning schemes can provide a valuable tool for studying different types of VF while working in low-dimensional latent spaces, as the machine-learning generated features exhibit separability among different VF types. This study confirms that latent variables are better VF descriptors than conventional time or domain features, making this technique useful in current VF research on elucidation of the underlying VF mechanisms.

Distinct spectral dynamics of implanted cardiac defibrillator signals in spontaneous termination of polymorphic ventricular tachycardia and fibrillation in patients with electrical and structural diseases

AIMS

To determine the spectral dynamics of early spontaneous polymorphic ventricular tachycardia and ventricular fibrillation (PVT/VF) in humans.

METHODS AND RESULTS

Fifty-eight self-terminated and 173 shock-terminated episodes of spontaneously initiated PVT/VF recorded by Medtronic implanted cardiac defibrillators (ICDs) in 87 patients with various cardiac pathologies were analyzed by short fast Fourier transform of shifting segments to determine the dynamics of dominant frequency (DF) and regularity index (RI). The progression in the intensity of DF and RI accumulations further quantified the time course of spectral characteristics of the episodes. Episodes of self-terminated PVT/VF lasted 8.6 s [95% confidence interval (CI): 8.1-9.1] and shock-terminated lasted 13.9 s (13.6-14.3) (P < 0.001). Recordings from patients with primarily electrical pathologies displayed higher DF and RI values than those from patients with primarily structural pathologies (P < 0.05) independently of ventricular function or antiarrhythmic drug therapy. Regardless of the underlying pathology, the average DF and RI intensities were lower in self-terminated than shock-terminated episodes [DF: 3.67 (4.04-4.58) vs. 4.32 (3.46-3.93) Hz, P < 0.001; RI: 0.53 (0.48-0.56) vs. 0.63 (0.60-0.65), P < 0.001]. In a multivariate analysis controlled by the type of pathology and clinical variables, regularity remained an independent predictor of self-termination [hazard ratio: 0.954 (0.928-0.980)]. Receiver operating characteristic (ROC) curve analysis of DF and RI intensities demonstrated increased predictability for self-termination in time with 95% CI above the 0.5 cut-off limit at about t = 8.6 s and t = 6.95 s, respectively.

CONCLUSION

Consistent with the notion that fast organized sources maintain PVT/VF in humans, reduction of frequency and regularity correlates with early self-termination. Our findings might help generate ICD methods aiming to reduce inappropriate shock deliveries.

Computerized Analysis of the Ventricular Fibrillation Waveform Allows Identification of Myocardial Infarction: A Proof-of-Concept Study for Smart Defibrillator Applications in Cardiac Arrest

Background

In cardiac arrest, computerized analysis of the ventricular fibrillation (VF) waveform provides prognostic information, while its diagnostic potential is subject of study. Animal studies suggest that VF morphology is affected by prior myocardial infarction (MI), and even more by acute MI. This experimental in‐human study reports on the discriminative value of VF waveform analysis to identify a prior MI. Outcomes may provide support for in‐field studies on acute MI.

Methods and Results

We conducted a prospective registry of implantable cardioverter defibrillator recipients with defibrillation testing (2010–2014). From 12‐lead surface ECG VF recordings, we calculated 10 VF waveform characteristics. First, we studied detection of prior MI with lead II, using one key VF characteristic (amplitude spectrum area [AMSA]). Subsequently, we constructed diagnostic machine learning models: model A, lead II, all VF characteristics; model B, 12‐lead, AMSA only; and model C, 12‐lead, all VF characteristics. Prior MI was present in 58% (119/206) of patients. The approach using the AMSA of lead II demonstrated a C‐statistic of 0.61 (95% CI, 0.54–0.68). Model A performance was not significantly better: 0.66 (95% CI, 0.59–0.73), P=0.09 versus AMSA lead II. Model B yielded a higher C‐statistic: 0.75 (95% CI, 0.68–0.81), P<0.001 versus AMSA lead II. Model C did not improve this further: 0.74 (95% CI, 0.67–0.80), P=0.66 versus model B.

Conclusions

This proof‐of‐concept study provides the first in‐human evidence that MI detection seems feasible using VF waveform analysis. Information from multiple ECG leads rather than from multiple VF characteristics may improve diagnostic accuracy. These results require additional experimental studies and may serve as pilot data for in‐field smart defibrillator studies, to try and identify acute MI in the earliest stages of cardiac arrest.

Spectral Analysis and Mutual Information Estimation of Left and Right Intracardiac Electrograms during Ventricular Fibrillation

Ventricular fibrillation (VF) signals are characterized by highly volatile and erratic electrical impulses, the analysis of which is difficult given the complex behavior of the heart rhythms in the left (LV) and right ventricles (RV), as sometimes shown in intracardiac recorded Electrograms (EGM). However, there are few studies that analyze VF in humans according to the simultaneous behavior of heart signals in the two ventricles. The objective of this work was to perform a spectral and a non-linear analysis of the recordings of 22 patients with Congestive Heart Failure (CHF) and clinical indication for a cardiac resynchronization device, simultaneously obtained in LV and RV during induced VF in patients with a Biventricular Implantable Cardioverter Defibrillator (BICD) Contak Renewal IVTM (Boston Sci.). The Fourier Transform was used to identify the spectral content of the first six seconds of signals recorded in the RV and LV simultaneously. In addition, measurements that were based on Information Theory were scrutinized, including Entropy and Mutual Information. The results showed that in most patients the spectral envelopes of the EGM sources of RV and LV were complex, different, and with several frequency peaks. In addition, the Dominant Frequency (DF) in the LV was higher than in the RV, while the Organization Index (OI) had the opposite trend. The entropy measurements were more regular in the RV than in the LV, thus supporting the spectral findings. We can conclude that basic stochastic processing techniques should be scrutinized with caution and from basic to elaborated techniques, but they can provide us with useful information on the biosignals from both ventricles during VF.

Safety threshold of R-wave amplitudes in patients with implantable cardioverter defibrillator

OBJECTIVE

A safety threshold for baseline rhythm R-wave amplitudes during follow-up of implantable cardioverter defibrillators (ICD) has not been established. We aimed to analyse the amplitude distribution and undersensing rate during spontaneous episodes of ventricular fibrillation (VF), and define a safety amplitude threshold for baseline R-waves.

METHODS

Data were obtained from an observational multicentre registry conducted at 48 centres in Spain. Baseline R-wave amplitudes and VF events were prospectively registered by remote monitoring. Signal processing algorithms were used to compare amplitudes of baseline R-waves with VF R-waves. All undersensed R-waves after the blanking period (120 ms) were manually marked.

RESULTS

We studied 2507 patients from August 2011 to September 2014, which yielded 229 VF episodes (cycle length 189.6±29.1 ms) from 83 patients that were suitable for R-wave comparisons (follow-up 2.7±2.6 years). The majority (77.6%) of VF R-waves (n=13953) showed lower amplitudes than the reference baseline R-wave. The decrease in VF amplitude was progressively attenuated among subgroups of baseline R-wave amplitude (≥17; ≥12 to <17; ≥7 to <12; ≥2.2 to <7 mV) from the highest to the lowest: median deviations -51.2% to +22.4%, respectively (p=0.027). There were no significant differences in undersensing rates of VF R-waves among subgroups. Both the normalised histogram distribution and the undersensing risk function obtained from the ≥2.2 to <7 mV subgroup enabled the prediction that baseline R-wave amplitudes ≤2.5 mV (interquartile range: 2.3-2.8 mV) may lead to ≥25% of undersensed VF R-waves.

CONCLUSIONS

Baseline R-wave amplitudes ≤2.5 mV during follow-up of patients with ICDs may lead to high risk of delayed detection of VF.

TRIAL REGISTRATION NUMBER

NCT01561144; results.

A framework for the atrial fibrillation prediction in electrophysiological studies

BACKGROUND AND OBJECTIVE

Cardiac arrhythmias are disorders in terms of speed or rhythm in the heart's electrical system. Atrial fibrillation (AFib) is the most common sustained arrhythmia that affects a large number of persons. Electrophysiologic study (EPS) procedures are used to study fibrillation in patients; they consist of inducing a controlled fibrillation in surgical room to analyze electrical heart reactions or to decide for implanting medical devices (i.e., pacemaker). Nevertheless, the spontaneous induction may generate an undesired AFib, which may induce risk for patient and thus a critical issue for physicians. We study the unexpected AFib onset, aiming to identify signal patterns occurring in time interval preceding an event of spontaneous (i.e., not inducted) fibrillation. Profiling such signal patterns allowed to design and implement an AFib prediction algorithm able to early identify a spontaneous fibrillation. The objective is to increase the reliability of EPS procedures.

METHODS

We gathered data signals collected by a General Electric Healthcare's CardioLab electrophysiology recording system (i.e., a polygraph). We extracted superficial and intracavitary cardiac signals regarding 50 different patients studied at the University Magna Graecia Cardiology Department. By studying waveform (i.e., amplitude and energy) of intracavitary signals before the onset of the arrhythmia, we were able to define patterns related to AFib onsets that are side effects of an inducted fibrillation.

RESULTS

A framework for atrial fibrillation prediction during electrophysiological studies has been developed. It includes a prediction algorithm to alert an upcoming AFib onset. Tests have been performed on an intracavitary cardiac signals data set, related to patients studied in electrophysiological room. Also, results have been validated by the clinicians, proving that the framework can be useful in case of integration with the polygraph, helping physicians in managing and controlling of patient status during EPS.

Cycle length characteristics differentiating non-sustained from self-terminating ventricular fibrillation in Brugada syndrome

AIMS

Limited information is available on self-terminating (ST) ventricular fibrillation (VF). Understanding spontaneous fluctuations in VF cycle length (CL) is required to identify arrhythmia that will stop before shock. Using Brugada syndrome (BS) as a model, the purpose of the study was to compare ST-VF and VF terminated by electrical shock and to look for spontaneous fluctuations in ventricular CL.

METHODS AND RESULTS

Occurrence of ST-VF and VF was studied in 53 patients with 46 VF episodes: (i) spontaneously, (ii) during defibrillation threshold testing, (iii) during programmed ventricular stimulation (PVS). Fifteen presented ST-VF (average duration 25 s): 11 during PVS, 1 during defibrillation threshold testing, and 3 spontaneously (at device interrogation). Self-terminating ventricular fibrillation was compared with 31 VFs terminated by electrical shock. Mean ventricular CL was longer (192.5 ± 22 vs. 149 ± 19 ms) (P < 0.0001) and CL became longer or did not change in ST-VF (187 ± 28 vs. 200 ± 25 ms) (first vs. last CL)(NS) in contrast with progressively shorter CL in electrical shock-terminated VF (177 ± 14.5 vs. 139 ± 12 ms) (first vs. last CL before electrical shock) (P < 0.0001). Ventricular fibrillation had more CL variability (average 16.4 ± 6.5 ms) for the first 50 beats than ST-VF (average 4.08 ± 2) (P < 0.0001). Cycle length range for the first 50 beats was 9.6 ± 1 ms for ST-VF and 44 ± 15 for VF (P < 0.002).

CONCLUSION

Self-terminating ventricular fibrillation in BS was not rare (28%). Ventricular CL was longer and progressively increased or did not change in ST-VF compared with electrical shock-terminating VF. Cycle length variability and CL range could differentiate VF and ST-VF within the first 50 beats. These parameters should be considered in the algorithms for VF detection and termination.

Spectral methods to distinguish ventricular fibrillation from artefact in implantable cardioverter-defibrillators

BACKGROUND

Despite the proven benefit of implantable cardioverter-defibrillators (ICDs), inappropriate shocks remain a significant problem. Recent trends have shown an increased incidence of lead failure and an increased exposure of devices to extreme electromagnetic interference environments.

AIMS

The goal of the current study is to evaluate the spectral characteristics of ventricular fibrillation (VF) detected in an ICD at time of defibrillation threshold testing and use of the findings to predict event types from a population of clinical VF and artefact events.

METHODS AND RESULTS

A modelling group was created from induced VF and artefact events at time of ICD implantation and DFT testing. Power spectral density evaluation was performed on each event and used to calculate an energy ratio (ER; the ratio of energy under the first three harmonics to the entire spectrum). The model was then applied to a database of clinical VF and artefact events to determine its sensitivity and specificity. The far-field ER of the modelling group was significantly larger for VF (0.888 ± 0.110) than artefact (0.265 ± 0.156, P < 0.0001). In the test group, the far-field ER of VF (0.882 ± 0.088) was also significantly larger than artefact (0.344 ± 0.128, P < 0.0001). At a cut-off of >0.526, the far-field ER had a sensitivity of 100% [confidence interval (CI) 100-100%] and a specificity of 92.4% (CI 84.9-98.5%) to distinguish clinical VF from clinical artefact.

CONCLUSION

Far-field signal during VF detected by an ICD has a distinct spectral pattern that can distinguish VF from artefact.