Diagnosis of Heart Failure Based on the Phase Space Complexity of Ballistocardiogram With Ensemble Empirical Mode Decomposition

This study introduced a novel auxiliary method for heart failure (HF) diagnosis using the phase space complexity features of ballistocardiogram (BCG) signals collected from piezoelectric sensors. Such a method can potentially monitor high-risk patients out of the clinic. Experimental measurements were collected from 46 patients with HF and 24 healthy subjects. The signals were divided into 1014 nonoverlapping segments (HF: 684 segments, Healthy: 330 segments). First, a digital signal processing framework was established to extract phase space complexity features of BCG with ensemble empirical mode decomposition. Applying a targeted selection strategy, we then identified three key intrinsic mode function (IMF) bands (IMF4–IMF6) for subsequent analysis. Different IMF combinations of features were evaluated using the K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) approaches. Through 10-fold cross-validation, the proposed method exhibited 94.98%, 93.80%, 94.76%, and 94.86% accuracies for the KNN, SVM, RF, and XGB classifiers, respectively. The best performance was achieved by combining IMF4–IMF6 features with the KNN classifier. The proposed BCG signal processing framework is lucrative for diagnosing HF in a home setting.