Risk prediction for myocardial infarction via generalized functional regression models

Abstract

In this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose pre-hospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a multivariate functional principal component analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally, the robustness of the procedure is checked via leave-j-out techniques.

Predicting plant disease epidemics from functionally represented weather series

“…However, the new post-anthesis lr models (19,20) were no better (mean AIC ¼ 1038) than the original post-anthesis lr models (mean AIC ¼ 1032). The greatest improvement in model fit, from the lr perspective, came from the two new lr models (25,26) in which the weather-based predictors summarized pre-and post-anthesis conditions in windows spanning anthesis (mean AIC ¼ 999).…”

“…Scalar-on-function regression is applicable to any situation where interest lies in modelling a static outcome (binary or continuous) in relation to explanatory variables observed over time. Recent examples have included the modelling of myocardial infarction occurrences in relation to electrocardiographic traces [25], of lupus flares from daily stress levels [26] and of influenza rates from weather in the previous weeks [8]. We illustrated, via application to a pernicious disease of wheat [27], the utility of scalar-on-function regression in predicting a binary plant disease outcome.…”

“…Twelve new variables (built from T, RH and TRH) were created as summaries over periods associated with FHB epidemics. These 12 variables formed the basis of eight new lr models (19)(20)(21)(22)(23)(24)(25)(26), each with three weather-based predictors and scalar predictors representing resist, wheat type and maize residue.…”

Discussion of “multivariate functional outlier detection” by M. Hubert, P. Rousseeuw and P. Segaert

“…It is more and more often the case that applications provide data with a higher number of components (see, for example, the application presented in Ieva et al 2013;Ieva and Paganoni 2013b;Tarabelloni et al 2013), where multi-lead ECG signals are considered, for which p = 8, since the data contain measurements of the human heart activity registered at 8 different parts of the body. Then the computational feasibility of the methods used for computing the depths becomes an issue.…”

Use of Depth Measure for Multivariate Functional Data in Disease Prediction: An Application to Electrocardiograph Signals

“…In Ieva and Paganoni [3] a similar problem has been faced, mainly performing a data dimensionality reduction by a Multivariate Functional Principal Component Analysis (see Ramsay and Silverman [4] and Berrendero et al [5]). It consists of summarizing the information contained in covariance operators of the signals and their first derivatives by the corresponding scores.…”