Violeta Monasterio

A review of signals used in sleep analysis

This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.

Multilead Analysis of T-Wave Alternans in the ECG Using Principal Component Analysis

T-wave alternans (TWA) is a cardiac phenomenon associated with the mechanisms leading to sudden cardiac death. Several methods exist to automatically detect and estimate TWA in the ECG on a single-lead basis, and their main drawback is their poor sensitivity to low-amplitude TWA. In this paper, we propose a multilead analysis scheme to improve the detection and estimation of TWA. It combines principal component analysis with a single-lead method based on the generalized likelihood ratio test. The proposed scheme is evaluated and compared to a single-lead scheme by means of a simulation study, in which different types of simulated and physiological noise are considered under realistic conditions. Simulation results show that the multilead scheme can detect TWA with an SNR 30 dB lower and allows the estimation of TWA with an SNR 25 dB lower than the single-lead scheme. The two analysis schemes are also applied to stress test ECG records. Results show that the multilead scheme provides a higher detection power and that TWA detections obtained with this scheme are significantly different in healthy volunteers and ischemic patients, whereas they are not with the single-lead scheme.

A multilead scheme based on periodic component analysis for T-wave alternans analysis in the ECG.

T-wave alternans (TWA) is a cardiac phenomenon that appears in the electrocardiogram (ECG) and is associated with the mechanisms leading to sudden cardiac death (SCD). In this study, we propose the use of a multilead TWA analysis scheme that combines the Laplacian likelihood ratio (LLR) method and periodic component analysis (πCA), an eigenvalue decomposition technique whose aim is to extract the most periodic sources of the signal. The proposed scheme is evaluated in different scenarios—from synthetic signals to stress test ECGs—and is compared to other reported schemes based on the LLR method. Results demonstrate that the πCA-based scheme provides a superior ability to detect TWA than previously reported schemes, and has the potential to improve the prognostic value of testing for TWA.

Average T-wave alternans activity in ambulatory ECG records predicts sudden cardiac death in patients with chronic heart failure

BACKGROUND T-wave alternans (TWA) is a well-documented noninvasive electrocardiographic (ECG) method useful for identifying patients at risk for sudden cardiac death (SCD). OBJECTIVE The purpose of this study was to evaluate whether the long-term average TWA activity on Holter monitoring provides prognostic information in patients with chronic heart failure. METHODS Twenty-four-hour Holter ECGs from 650 ambulatory patients with mild-to-moderate chronic heart failure were analyzed in the study. Average TWA activity was measured by using a fully automated multilead technique, and 2 indices were proposed to quantify TWA: an index quantifying the average TWA activity in the whole recording (IAA), which was used to define a positive/negative TWA test, and an index quantifying the average TWA activity at heart rates between 80 and 90 beats/min (IAA(90)). RESULTS Patients were divided into TWA positive (TWA+) and TWA negative (TWA-) groups by setting a cut point of 3.7 μV for IAA, corresponding to the 75th percentile of the distribution of IAA in the population. After a median follow-up of 48 months, the survival rate was significantly higher in the TWA- group for cardiac death and SCD (p = .017 and p = .001, respectively). Multivariate Cox proportional hazards analysis revealed that both TWA+ and IAA(90) were associated with SCD with hazard rates of 2.29 (p = .004) and 1.07 per μV (p = .046), respectively. CONCLUSION The average TWA activity measured automatically from Holter ECGs predicted SCD in patients with mild-to-moderate chronic heart failure.

Comparative Evaluation of Methodologies for T-Wave Alternans Mapping in Electrograms

Electrograms (EGM) recorded from the surface of the myocardium are becoming more and more accessible. T-wave alternans (TWA) is associated with increased vulnerability to ventricular tachycardia/fibrillation and it occurs before the onset of ventricular arrhythmias. Thus, accurate methodologies for time-varying alternans estimation/detection in EGM are needed. In this paper, we perform a simulation study based on epicardial EGM recorded in vivo in humans to compare the accuracy of four methodologies: the spectral method (SM), modified moving average method, laplacian likelihood ratio method (LLR), and a novel method based on time-frequency distributions. A variety of effects are considered, which include the presence of wide band noise, respiration, and impulse artifacts. We found that 1) EGM-TWA can be detected accurately when the standard deviation of wide-band noise is equal or smaller than ten times the magnitude of EGM-TWA. 2) Respiration can be critical for EGM-TWA analysis, even at typical respiratory rates. 3) Impulse noise strongly reduces the accuracy of all methods, except LLR. 4) If depolarization time is used as a fiducial point, the localization of the T-wave is not critical for the accuracy of EGM-TWA detection. 5) According to this study, all methodologies provided accurate EGM-TWA detection/quantification in ideal conditions, while LLR was the most robust, providing better detection-rates in noisy conditions. Application on epicardial mapping of the in vivo human heart shows that EGM-TWA has heterogeneous spatio-temporal distribution.

Robust classification of neonatal apnoea-related desaturations

Respiratory signals monitored in the neonatal intensive care units are usually ignored due to the high prevalence of noise and false alarms (FA). Apneic events are generally therefore indicated by a pulse oximeter alarm reacting to the subsequent desaturation. However, the high FA rate in the photoplethysmogram may desensitize staff, reducing the reaction speed. The main reason for the high FA rates of critical care monitors is the unimodal analysis behaviour. In this work, we propose a multimodal analysis framework to reduce the FA rate in neonatal apnoea monitoring. Information about oxygen saturation, heart rate, respiratory rate and signal quality was extracted from electrocardiogram, impedance pneumogram and photoplethysmographic signals for a total of 20 features in the 5 min interval before a desaturation event. 1616 desaturation events from 27 neonatal admissions were annotated by two independent reviewers as true (physiologically relevant) or false (noise-related). Patients were divided into two independent groups for training and validation, and a support vector machine was trained to classify the events as true or false. The best classification performance was achieved on a combination of 13 features with sensitivity, specificity and accuracy of 100% in the training set, and a sensitivity of 86%, a specificity of 91% and an accuracy of 90% in the validation set.

A Nonparametric Surrogate-Based Test of Significance for T-Wave Alternans Detection

We present a nonparametric adaptive surrogate test that allows for the differentiation of statistically significant T-wave alternans (TWA) from alternating patterns that can be solely explained by the statistics of noise. The proposed test is based on estimating the distribution of noise-induced alternating patterns in a beat sequence from a set of surrogate data derived from repeated reshuffling of the original beat sequence. Thus, in assessing the significance of the observed alternating patterns in the data, no assumptions are made about the underlying noise distribution. In addition, since the distribution of noise-induced alternans magnitudes is calculated separately for each sequence of beats within the analysis window, the method is robust to data nonstationarities in both noise and TWA. The proposed surrogate method for rejecting noise was compared to the standard noise-rejection methods used with the spectral method (SM) and the modified moving average (MMA) techniques. Using a previously described realistic multilead model of TWA and real physiological noise, we demonstrate the proposed approach that reduces false TWA detections while maintaining a lower missed TWA detection, compared with all the other methods tested. A simple averaging-based TWA estimation algorithm was coupled with the surrogate significance testing and was evaluated on three public databases: the Normal Sinus Rhythm Database, the Chronic Heart Failure Database, and the Sudden Cardiac Death Database. Differences in TWA amplitudes between each database were evaluated at matched heart rate (HR) intervals from 40 to 120 beats per minute (BPM). Using the two-sample Kolmogorov-Smirnov test, we found that significant differences in TWA levels exist between each patient group at all decades of HRs. The most-marked difference was generally found at higher HRs, and the new technique resulted in a larger margin of separability between patient populations than when the SM or MMA were applied to the same data.

Pulse Photopletismography Amplitude Decrease Detector for Sleep Apnea Evaluation in Children

A method for automatic detection of sleep apnea using pulse photoplethysmography signal (PPG) is proposed. This method is based on a detection of decreases on PPG amplitude fluctuations. The proposed detector is composed of three stages: pre-processing, envelope detection, based on root mean square series or Hilbert transform, and decision algorithm based on an adaptive threshold. The detector has been evaluated using simulated and real signals. Sensibility and positive predictive value of the detector where 76% and 73% for real signals. A clinical study to sleep apnea diagnosis in children based on this detector has been carried out. PPG attenuation events per hour ratio E h has statistical significance (p < 0.05) to classify children as normal 13.5 plusmn 6.35 Eh (mean plusmn SD) or pathologic 21.1 plusmn 8.93 Eh

Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers.

BACKGROUND Considering the rates of sudden cardiac death (SCD) and pump failure death (PFD) in chronic heart failure (CHF) patients and the cost-effectiveness of their preventing treatments, identification of CHF patients at risk is an important challenge. In this work, we studied the prognostic performance of the combination of an index potentially related to dispersion of repolarization restitution (Δα), an index quantifying T-wave alternans (IAA) and the slope of heart rate turbulence (TS) for classification of SCD and PFD. METHODS Holter ECG recordings of 597 CHF patients with sinus rhythm enrolled in the MUSIC study were analyzed and Δα, IAA and TS were obtained. A strategy was implemented using support vector machines (SVM) to classify patients in three groups: SCD victims, PFD victims and other patients (the latter including survivors and victims of non-cardiac causes). Cross-validation was used to evaluate the performance of the implemented classifier. RESULTS Δα and IAA, dichotomized at 0.035 (dimensionless) and 3.73 μV, respectively, were the ECG markers most strongly associated with SCD, while TS, dichotomized at 2.5 ms/RR, was the index most strongly related to PFD. When separating SCD victims from the rest of patients, the individual marker with best performance was Δα≥0.035, which, for a fixed specificity (Sp) of 90%, showed a sensitivity (Se) value of 10%, while the combination of Δα and IAA increased Se to 18%. For separation of PFD victims from the rest of patients, the best individual marker was TS ≤ 2.5 ms/RR, which, for Sp=90%, showed a Se of 26%, this value being lower than Se=34%, produced by the combination of Δα and TS. Furthermore, when performing SVM classification into the three reported groups, the optimal combination of risk markers led to a maximum Sp of 79% (Se=18%) for SCD and Sp of 81% (Se=14%) for PFD. CONCLUSIONS The results shown in this work suggest that it is possible to efficiently discriminate SCD and PFD in a population of CHF patients using ECG-derived risk markers like Δα, TS and IAA.

Prognostic value of average T-wave alternans and QT variability for cardiac events in MADIT-II patients.

BACKGROUND Identifying which patients might benefit the most from ICD therapy remains challenging. We hypothesize that increased T-wave alternans (TWA) and QT variability (QTV) provide complementary information for predicting appropriate ICD therapy in patients with previous myocardial infarction and reduced ejection fraction. METHODS We analyzed 10-min resting ECGs from MADIT-II patients with baseline heart rate >80 beats/min. TWA indices IAA and IAA90 were computed with the multilead Laplacian Likelihood ratio method. QTV indices QTVN and QTVI were measured using a standard approach. Cox proportional hazard models were adjusted considering appropriate ICD therapy and sudden cardiac death (SCD) as endpoints. RESULTS TWA and QTV were measured in 175 patients. Neither QTV nor TWA predicted SCD. Appropriate ICD therapy was predicted by combining IAA90 and QTVN after adjusting for relevant correlates. CONCLUSION Increased TWA and QTV are independent predictors of appropriate ICD therapy in MADIT-II patients with elevated heart rate at baseline.