Óscar Barquero-Pérez

Heart Rate Turbulence Analysis Based on Photoplethysmography

The goal of this paper is to determine whether the photoplethysmography (PPG) can replace the ECG-based detection of heart rate turbulence. Using the PPG, classification of ventricular premature beats (VPBs) is accomplished with a linear classifier. The two conventional parameters turbulence onset and slope are studied together with a recently introduced parameter characterizing turbulence shape. Performance is studied on a dataset with 4131 VPBs, recorded from a total of 27 patients in different clinical contexts (hemodialysis treatment, intensive care monitoring, and electrophysiological study). The sensitivity/specificity of VPB classification was found to be 90.5/99.9%, with an accuracy of 99.3%, suggesting that classification of VPBs can be reliable made from the PPG. The main difference between the two types of turbulence analysis stems from the fact that the pulse transit time varies largely immediately after the VPB. Out of the 22 patients which had a sufficient number of VPBs, the outcome of the ECG- and PPG-based analysis was identical in 21. It is concluded that the PPG may serve as a surrogate technique for the ECG in turbulence analysis.

Heart Rate Turbulence Denoising Using Support Vector Machines

Heart rate turbulence (HRT) is a transient acceleration and subsequent deceleration of the heart rate after a premature ventricular complex (PVC), and it has been shown to be a strong risk stratification criterion in patients with cardiac disease. In order to reduce the noise level of the HRT signal, conventional measurements of HRT use a patient-averaged template of post-PVC tachogram (PPT), hence providing with long-term HRT indexes. We hypothesize that the reduction of the noise level at each isolated PPT, using signal processing techniques, will allow us to estimate short-term HRT indexes. Accordingly, its application could be extended to patients with reduced number of available PPT. In this paper, several HRT denoising procedures are proposed and tested, with special attention to support vector machine (SVM) estimation, as this is a robust algorithm that allows us to deal with few available time samples in the PPT. Pacing-stimulated HRT during electrophysiological study are used as a low-noise gold standard. Measurements in a 24-h Holter patient database reveal a significant reduction in the bias and the variance of HRT measurements. We conclude that SVM denoising yields short-term HRT measurements and improves the signal-to-noise level of long-term HRT measurements.

Heart Rate Variability on 7-Day Holter Monitoring Using a Bootstrap Rhythmometric Procedure

Heart rate variability (HRV) markers have been widely used to characterize the autonomous regulation state of the heart from 24-h Holter monitoring, but long-term evolution of HRV indexes is mostly unknown. A dataset of 7-day Holter recordings of 22 patients with congestive heart failure was studied. A rhythmometric procedure was designed to characterize the infradian, circadian, and ultradian components for each patient, as well as circadian and ultradian fluctuations. Furthermore, a bootstrap test yielded automatically the rhythmometric model for each patient. We analyzed the temporal evolution of relevant time-domain (AVNN, SDNN, and NN50), frequency-domain (LF, HF, HFn, and LF/HF), and nonlinear (α1 and SampEn) HRV indexes. Circadian components were the most significant for all HRV indexes, but the infradian ones were also strongly present in NN50, HFn, LF/HF, α1, and SampEn indexes. Among ultradian components that one corresponding to 12 h, was the most relevant. Long-term monitoring of HRV conveys new potentially relevant rhythmometric information, which can be analyzed by using the proposed automatic procedure.

Fundamental Frequency and Regularity of Cardiac Electrograms With Fourier Organization Analysis

Dominant frequency analysis (DFA) and organization analysis (OA) of cardiac electrograms (EGMs) aims to establish clinical targets for cardiac arrhythmia ablation. However, these previous spectral descriptions of the EGM have often discarded relevant information in the spectrum, such as the harmonic structure or the spectral envelope. We propose a fully automated algorithm for estimating the spectral features in EGM recordings This approach, called Fourier OA (FOA), accounts jointly for the organization and periodicity in the EGM, in terms of the fundamental frequency instead of dominant frequency. In order to compare the performance of FOA and DFA-OA approaches, we analyzed simulated EGM, obtained in a computer model, as well as two databases of implantable defibrillator-stored EGM. FOA parameters improved the organization measurements with respect to OA, and averaged cycle length and regularity indexes were more accurate when related to the fundamental (instead of dominant) frequency, as estimated by the algorithm (p <; 0.05 comparing f0 estimated by DFA and by FOA). FOA yields a more detailed and robust spectral description of EGM compared to DFA and OA parameters.

Comparison of Detection of Arrhythmias in Patients With Chronic Heart Failure Secondary to Non-Ischemic Versus Ischemic Cardiomyopathy by 1 Versus 7-Day Holter Monitoring

The purpose of this study was to compare the diagnostic sensitivity of 1-day Holter monitoring versus 7-day Holter monitoring (7DH) to detect atrial and ventricular arrhythmias in a population of stable patients with chronic heart failure and left ventricular dysfunction. Sixty-three consecutive stable patients with chronic heart failure with left ventricular ejection fractions < or =50% were included. Blood samples were obtained, the Minnesota Living With Heart Failure Questionnaire was administered, and echocardiography, 6-minute walk tests, and 7DH were performed at enrollment. The mean ejection fraction was 35.8 +/- 9.8%, and the mean age was 55.5 +/- 13.9 years. Seven-day Holter monitoring did not significantly increase the detection of nonsustained atrial tachycardia or atrial fibrillation. In contrast, the incidence of nonsustained ventricular tachycardia increased in nonischemic patients from 35.1% on day 1 to 54.1% on day 7 (p = 0.01). In ischemic patients, the sensitivity increased from 11.5% to 46.2% (p = 0.004). Two patients without nonsustained ventricular tachycardia on day 1 had episodes of 13 and 16 beats on days 3 and 6 of monitoring. In patients with left ventricular ejection fractions >35% and N-terminal-pro-brain natriuretic peptide levels <1,000 pg/ml, no episodes of nonsustained ventricular tachycardia were detected on day 1 in nonischemic and ischemic patients, but 7DH detected 3 new patients in each group. In conclusion, 7DH clearly improves the detection and allows a better characterization of ventricular arrhythmic episodes but seems to be less useful for supraventricular events.

Quality estimation of the electrocardiogram using cross-correlation among leads

BackgroundFast and accurate quality estimation of the electrocardiogram (ECG) signal is a relevant research topic that has attracted considerable interest in the scientific community, particularly due to its impact on tele-medicine monitoring systems, where the ECG is collected by untrained technicians. In recent years, a number of studies have addressed this topic, showing poor performance in discriminating between clinically acceptable and unacceptable ECG records.Methods This paper presents a novel, simple and accurate algorithm to estimate the quality of the 12-lead ECG by exploiting the structure of the cross-covariance matrix among different leads. Ideally, ECG signals from different leads should be highly correlated since they capture the same electrical activation process of the heart. However, in the presence of noise or artifacts the covariance among these signals will be affected. Eigenvalues of the ECG signals covariance matrix are fed into three different supervised binary classifiers.Results and conclusion The performance of these classifiers were evaluated using PhysioNet/CinC Challenge 2011 data. Our best quality classifier achieved an accuracy of 0.898 in the test set, while having a complexity well below the results of contestants who participated in the Challenge, thus making it suitable for implementation in current cellular devices.

Sudden Cardiac Risk Stratification with Electrocardiographic Indices - A Review on Computational Processing, Technology Transfer, and Scientific Evidence

Great effort has been devoted in recent years to the development of sudden cardiac risk predictors as a function of electric cardiac signals, mainly obtained from the electrocardiogram (ECG) analysis. But these prediction techniques are still seldom used in clinical practice, partly due to its limited diagnostic accuracy and to the lack of consensus about the appropriate computational signal processing implementation. This paper addresses a three-fold approach, based on ECG indices, to structure this review on sudden cardiac risk stratification. First, throughout the computational techniques that had been widely proposed for obtaining these indices in technical literature. Second, over the scientific evidence, that although is supported by observational clinical studies, they are not always representative enough. And third, via the limited technology transfer of academy-accepted algorithms, requiring further meditation for future systems. We focus on three families of ECG derived indices which are tackled from the aforementioned viewpoints, namely, heart rate turbulence (HRT), heart rate variability (HRV), and T-wave alternans. In terms of computational algorithms, we still need clearer scientific evidence, standardizing, and benchmarking, siting on advanced algorithms applied over large and representative datasets. New scenarios like electronic health recordings, big data, long-term monitoring, and cloud databases, will eventually open new frameworks to foresee suitable new paradigms in the near future.

Ontology for Heart Rate Turbulence Domain From The Conceptual Model of SNOMED-CT

Electronic health record (EHR) automates the clinician workflow, allowing evidence-based decision support and quality management. We aimed to start a framework for domain standardization of cardiovascular risk stratification into the EHR, including risk indices whose calculation involves ECG signal processing. We propose the use of biomedical ontologies completely based on the conceptual model of SNOMED-CT, which allows us to implement our domain in the EHR. In this setting, the present study focused on the heart rate turbulence (HRT) domain, according to its concise guidelines and clear procedures for parameter calculations. We used 289 concepts from SNOMED-CT, and generated 19 local extensions (new concepts) for the HRT specific concepts not present in the current version of SNOMED-CT. New concepts included averaged and individual ventricular premature complex tachograms, initial sinus acceleration for turbulence onset, or sinusal oscillation for turbulence slope. Two representative use studies were implemented: first, a prototype was inserted in the hospital information system for supporting HRT recordings and their simple follow up by medical societies; second, an advanced support for a prospective scientific research, involving standard and emergent signal processing algorithms in the HRT indices, was generated and then tested in an example database of 27 Holter patients. Concepts of the proposed HRT ontology are publicly available through a terminology server, hence their use in any information system will be straightforward due to the interoperability provided by SNOMED-CT.

Long-term characterization of persistent atrial fibrillation: wave morphology, frequency, and irregularity analysis

Abstract Short-term properties of atrial fibrillation (AF) frequency, f-wave morphology, and irregularity parameters have been thoroughly studied, but not long-term properties. In the present work, f-wave morphology is characterized by principal component analysis, introducing a novel temporal parameter defined by the cumulative normalized variance of the three largest principal components