Background— The identification of sites of dominant activation frequency during atrial fibrillation (AF) in humans and the effect of ablation at these sites have not been reported. Methods and Results— Thirty-two patients undergoing AF ablation (19 paroxysmal, 13 permanent) during ongoing arrhythmia were studied. Electroanatomic mapping was performed, acquiring 126±13 points per patient throughout both atria and coronary sinus. At each point, 5-second electrograms were obtained to determine the highest-amplitude frequency on spectral analysis and to construct 3D dominant frequency (DF) maps. The temporal stability of the recording interval was confirmed in a subset. Ablation was performed with the operator blinded to the DF maps. The effect of ablation at sites with or without high-frequency DF sites (maximal frequencies surrounded by a decreasing frequency gradient ≥20%) was evaluated by determining the change in AF cycle length (AFCL) and the termination and inducibility of AF. The spatial distribution ...

Spectral Analysis Identifies Sites of High-Frequency Activity Maintaining Atrial Fibrillation in Humans

There is a growing body of research focusing on automatic detection of ischemia and myocardial infarction (MI) using computer algorithms. In clinical settings, ischemia and MI are diagnosed using electrocardiogram (ECG) recordings as well as medical context including patient symptoms, medical history, and risk factors—information that is often stored in the electronic health records. The ECG signal is inspected to identify changes in the morphology such as ST-segment deviation and T-wave changes. Some of the proposed methods compute similar features automatically while others use nonconventional features such as wavelet coefficients. This review provides an overview of the methods that have been proposed in this area, focusing on their historical evolution, the publicly available datasets that they have used to evaluate their performance, and the details of their algorithms for ECG and EHR analysis. The validation strategies that have been used to evaluate the performance of the proposed methods are also presented. Finally, the paper provides recommendations for future research to address the shortcomings of the currently existing methods and practical considerations to make the proposed technical solutions applicable in clinical practice.

A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records

On auscultation of the heart. I

Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection

https://www.mdpi.com/1424-8220/21/11/3786/pdf

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

Entropy is a powerful tool for quantification of the brain function and its information processing capacity. This is evident in its broad domain of applications that range from functional interactivity between the brain regions to quantification of the state of consciousness. A number of previous reviews summarized the use of entropic measures in neuroscience. However, these studies either focused on the overall use of nonlinear analytical methodologies for quantification of the brain activity or their contents pertained to a particular area of neuroscientific research. The present study aims at complementing these previous reviews in two ways. First, by covering the literature that specifically makes use of entropy for studying the brain function. Second, by highlighting the three fields of research in which the use of entropy has yielded highly promising results: the (altered) state of consciousness, the ageing brain, and the quantification of the brain networks’ information processing. In so doing, the present overview identifies that the use of entropic measures for the study of consciousness and its (altered) states led the field to substantially advance the previous findings. Moreover, it realizes that the use of these measures for the study of the ageing brain resulted in significant insights on various ways that the process of ageing may affect the dynamics and information processing capacity of the brain. It further reveals that their utilization for analysis of the brain regional interactivity formed a bridge between the previous two research areas, thereby providing further evidence in support of their results. It concludes by highlighting some potential considerations that may help future research to refine the use of entropic measures for the study of brain complexity and its function. The present study helps realize that (despite their seemingly differing lines of inquiry) the study of consciousness, the ageing brain, and the brain networks’ information processing are highly interrelated. Specifically, it identifies that the complexity, as quantified by entropy, is a fundamental property of conscious experience, which also plays a vital role in the brain’s capacity for adaptation and therefore whose loss by ageing constitutes a basis for diseases and disorders. Interestingly, these two perspectives neatly come together through the association of entropy and the brain capacity for information processing.

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Entropy and the Brain: An Overview.

Permutation Entropy (PE) is a time series complexity measure commonly used in a variety of contexts, with medicine being the prime example. In its general form, it requires three input parameters for its calculation: time series length N, embedded dimension m, and embedded delay τ . Inappropriate choices of these parameters may potentially lead to incorrect interpretations. However, there are no specific guidelines for an optimal selection of N, m, or τ , only general recommendations such as N > > m ! , τ = 1 , or m = 3 , … , 7 . This paper deals specifically with the study of the practical implications of N > > m ! , since long time series are often not available, or non-stationary, and other preliminary results suggest that low N values do not necessarily invalidate PE usefulness. Our study analyses the PE variation as a function of the series length N and embedded dimension m in the context of a diverse experimental set, both synthetic (random, spikes, or logistic model time series) and real–world (climatology, seismic, financial, or biomedical time series), and the classification performance achieved with varying N and m. The results seem to indicate that shorter lengths than those suggested by N > > m ! are sufficient for a stable PE calculation, and even very short time series can be robustly classified based on PE measurements before the stability point is reached. This may be due to the fact that there are forbidden patterns in chaotic time series, not all the patterns are equally informative, and differences among classes are already apparent at very short lengths.

https://www.mdpi.com/1099-4300/21/4/385/pdf

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

This paper presents a dimensionality reduction study based on fuzzy rough sets with the aim of increasing the discriminant capability of the representation of normal ECG beats and those that contain ischemic events. A novel procedure is proposed to obtain the fuzzy equivalence classes based on entropy and neighborhood techniques and a modification of the Quick Reduct Algorithm is used to select the relevant features from a large feature space by a dependency function. The tests were carried out on a feature space made up by 840 wavelet features extracted from 900 ECG normal beats and 900 ECG beats with evidence of ischemia. Results of around 99% classification accuracy are obtained. This methodology provides a reduced feature space with low complexity and high representation capability. Additionally, the discriminant strength of entropy in terms of representing ischemic disorders from time-frequency information in ECG signals is highlighted.

Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection

As cardiac murmurs do not generally appear in every area of auscultation, this paper presents an effective approach for cardiac murmur detection based on stochastic analysis of acoustic features derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of phonocardiographic (PCG) signals made up by the 4-Standard Auscultation Areas (SAA). The 4-SAA PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated over EMD components. An ergodic HMM was applied on the feature space, randomly initialized and trained by expectation maximization with a convergence at 10e-6 and a maximum iteration number of 1000. Global classification results for 4-SAA were around 98.7% with satisfactory sensitivity and specificity results, using a 30-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-SAA PCG signals and stochastic analysis of acoustic features offered a high performance to detect cardiac murmurs.

http://www.cinc.org/archives/2012/pdf/0529.pdf

Stochastic analysis and classification of 4-area cardiac auscultation signals using Empirical Mode Decomposition and acoustic features

The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs.

Adaptive neuro-fuzzy inference system for acoustic analysis of 4-channel phonocardiograms using empirical mode decomposition

Resumen─ La fibrilación auricular (FA) es la arritmia cardiaca más común. La ablación con catéter se ha convertido en la principal estrategia terapéutica para el tratamiento de la FA paroxística, sin embargo, los resultados en FA permanente no son completamente satisfactorios. Se propone la ablación de los electrogramas auriculares complejos fragmentados (CFAE) para la terminación de un rotor como mecanismo de mantenimiento de FA permanente. El objetivo de este trabajo es caracterizar los CFAE mediante la implementación de entropía aproximada (ApEn) y correlacionarlos con el tip de un rotor simulado. Para esto, se desarrolló un modelo 2D de tejido de aurícula humana bajo condiciones de FA permanente; se registraron electrogramas unipolares durante la actividad del rotor y se desarrolló un algoritmo para la medida de ApEn. La ApEn permitió localizar los CFAE con una alta precisión y relacionarlos con el tip del rotor. Por lo que este índice podría ser muy eficaz en la identificación de zonas susceptibles de ablación. Palabras claves— Ablación; CFAE, Electrogramas; Entropía aproximada; Fibrilación auricular permanente. Abstract — Atrial fibrillation (AF) is the most common cardiac arrhythmia. Catheter ablation has become the main therapeutic strategy for the treatment of paroxysmal AF, however, results in permanent AF are not completely satisfactory. Ablation of complex fractionated atrial electrograms (CFAE) is proposed for the termination of a rotor as mechanism of permanent AF maintenance. The aim of this work is to characterize the CFAE by implementing approximate entropy (ApEn) and to correlate with the tip of a

/pdf/identification-of-target-sites-for-ablation-during-permanent-2fihv7952n.pdf

Diana Orrego

Papers

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection

Stochastic analysis and classification of 4-area cardiac auscultation signals using Empirical Mode Decomposition and acoustic features

Adaptive neuro-fuzzy inference system for acoustic analysis of 4-channel phonocardiograms using empirical mode decomposition

Identification of target sites for ablation during permanent atrial fibrillation implementing approximate entropy in a 2d model