Effects of the location of myocardial infarction on the spectral characteristics of ventricular fibrillation

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.

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 IV^TM (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.

Detecting the harmonics of oscillations with time-variable frequencies

A method is introduced for the spectral analysis of complex noisy signals containing several frequency components. It enables components that are independent to be distinguished from the harmonics of nonsinusoidal oscillatory processes of lower frequency. The method is based on mutual information and surrogate testing combined with the wavelet transform, and it is applicable to relatively short time series containing frequencies that are time variable. Where the fundamental frequency and harmonics of a process can be identified, the characteristic shape of the corresponding oscillation can be determined, enabling adaptive filtering to remove other components and nonoscillatory noise from the signal. Thus the total bandwidth of the signal can be correctly partitioned and the power associated with each component then can be quantified more accurately. The method is first demonstrated on numerical examples. It is then used to identify the higher harmonics of oscillations in human skin blood flow, both spontaneous and associated with periodic iontophoresis of a vasodilatory agent. The method should be equally relevant to all situations where signals of comparable complexity are encountered, including applications in astrophysics, engineering, and electrical circuits, as well as in other areas of physiology and biology.