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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.

Background and objective: Heart failure (HF) is a lethal public health problem in the field of cardiovascular diseases with high incidence, rehospitalization, and mortality rates. Therefore, the prediction of in-hospital mortality of patients with HF is of paramount significance in providing clinical information to doctors. To improve the accuracy of prediction, this study constructed a prediction model for in-hospital mortality of patients with HF based on machine learning algorithms. Methods: We obtained the medical data of 1901 patients with HF from a public database and performed preprocessing to extract 19 variables as inputs of the prediction model. A prediction model was constructed based on the light gradient boosting machine (LightGBM) and its performance was improved using the Optuna framework to optimize the LightGBM hyperparameters. To evaluate the proposed algorithm, five machine learning algorithms widely used in the field of biomedicine were selected for comparison: support vector machine, classification and regression tree, random forests, gradient boosting decision tree, and LightGBM. Further, we explained the proposed model based on Deep SHapley Additive exPlanations. We also quantified the importance of each variable and analyzed its correlation with the results. Results: The accuracy rate of Optuna–LightGBM was $92\pm 1.44\%$, the precision rate was $83.71\pm 8.34\%$, the recall rate was $69.46\pm 11.18\%$, the $F$-measure was $74.81\pm 6.55\%$, and the area under the receiver operating characteristic curve was $83.14\pm 5\%$. The results show that this model outperformed other models on all evaluation indicators. Conclusions: The proposed method can be used to construct a prediction model for in-hospital mortality of patients with HF. Optuna–LightGBM can assist clinicians to quickly classify the high-risk patients with HF so that the clinicians can provide timely care and optimize hospital resources.

Heart failure (HF) is one of the most common, complex, heterogeneous diseases in the world, with over 1-3% of the global population living with the condition. Progression of HF can be tracked via MRI measures of structural and functional changes to the heart, namely left ventricle (LV), including ejection fraction, mass, end-diastolic volume, and LV end-systolic volume. Moreover, while genome-wide association studies (GWAS) have been a useful tool to identify candidate variants involved in HF risk, they lack crucial tissue-specific and mechanistic information which can be gained from incorporating additional data modalities. This study addresses this gap by incorporating transcriptome-wide and proteome-wide association studies (TWAS and PWAS) to gain insights into genetically-regulated changes in gene expression and protein abundance in precursors to HF measured using MRI-derived cardiac measures as well as full-stage all-cause HF. We identified several gene and protein overlaps between LV ejection fraction and end-systolic volume measures. Many of the overlaps identified in MRI-derived measurements through TWAS and PWAS appear to be shared with all-cause HF. We implicate many putative pathways relevant in HF associated with these genes and proteins via gene-set enrichment and protein-protein interaction network approaches. The results of this study (1) highlight the benefit of using multi-omics to better understand genetics and (2) provide novel insights as to how changes in heart structure and function may relate to HF.