Abstract
Heart rate is a nonstationary signal and its variation may contain indicators of current disease or warnings about impending cardiac diseases. Hence, heart rate variation analysis has become a noninvasive tool to further study the activities of the autonomic nervous system. In this scenario, the Poincaré plot analysis has proven to be a valuable tool to support cardiac diseases diagnosis. The study’s aim is a preliminary exploration of the feasibility of machine learning to classify subjects belonging to five cardiac states (healthy, hypertension, myocardial infarction, congestive heart failure and heart transplanted) using ten unconventional quantitative parameters extracted from bidimensional and three-dimensional Poincaré maps. Knime Analytic Platform was used to implement several machine learning algorithms: Gradient Boosting, Adaptive Boosting, k-Nearest Neighbor and Naïve Bayes. Accuracy, sensitivity and specificity were computed to assess the performances of the predictive models using the leave-one-out cross-validation. The Synthetic Minority Oversampling technique was previously performed for data augmentation considering the small size of the dataset and the number of features. A feature importance, ranked on the basis of the Information Gain values, was computed. Preliminarily, a univariate statistical analysis was performed through one-way Kruskal Wallis plus post-hoc for all the features. Machine learning analysis achieved interesting results in terms of evaluation metrics, such as demonstrated by Adaptive Boosting and k-Nearest Neighbor (accuracies greater than 90%). Gradient Boosting and k-Nearest Neighbor reached even 100% score in sensitivity and specificity, respectively. The most important features according to information gain are in line with the results obtained from the statistical analysis confirming their predictive power. The study shows the proposed combination of unconventional features extracted from Poincaré maps and well-known machine learning algorithms represents a valuable approach to automatically classify patients with different cardiac diseases. Future investigations on enriched datasets will further confirm the potential application of this methodology in diagnostic.
Highlights
IntroductionDevelopments in the measurement and available devices have led to even more accurate observations on the heart rate and its variations; this led to defining heart rate variability (HRV) as a diagnostic tool for heart disease evaluations
This paper is an extension of the work originally presented in the 2020 11th conference of the European Study Group on Cardiovascular Oscillations [1].Developments in the measurement and available devices have led to even more accurate observations on the heart rate and its variations; this led to defining heart rate variability (HRV) as a diagnostic tool for heart disease evaluations.Traditionally, HRV analysis from short-term laboratory recordings is based on time and frequency domains measurements [2,3]
Where the authors showed Naive Bayes (NB), ADA-B and KNN effectively classified C patients’ severity based on New York Heart Association functional classification, using the same unconventional features extracted from bi-dimensional and three-dimensional Poincaré Plots. Their accuracies and, generally, the overall evaluation metrics are lower than ours in this study but, again, a direct comparison is not completely fair since we considered a different target and we applied Synthetic Minority Oversampling technique (SMOTE) for data augmentation
Summary
Developments in the measurement and available devices have led to even more accurate observations on the heart rate and its variations; this led to defining heart rate variability (HRV) as a diagnostic tool for heart disease evaluations. HRV analysis from short-term laboratory recordings is based on time and frequency domains measurements [2,3]. Mainly based on nonlinear dynamics properties of the heart rate variability signal, are applied to long-term time series, owing to the need for large amount of data to derive the desired indexes [4,5]. The Poincaré plot is a simple and robust graphical technique which can be applied both to long- and short-term HRV recordings, in order to extract relevant information on beat-to-beat signal dynamics [6]. It has been frequently used to deal with biomedical issues in several contexts: cardiology [7,8], fetal monitoring [9,10,11], medical imaging analysis [12,13], oncology [14,15,16] and in several other medical specialties [17,18,19]
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