Assessment of quality of electrocardiograms, seismocardiograms, and gyrocardiograms based on features derived from symmetric projection attractor reconstruction in healthy subjects

Signal quality assessment is essential for biomedical signal processing, analysis, and interpretation. Various methods exist, including averaged numerical values, thresholding, time- or frequency-domain analysis, and nonlinear approaches. The aim of this study was to evaluate the quality of electrocardiographic (ECG) signals, seismocardiographic signals (SCG), and gyrocardiograms (GCG) based on symmetric projection attractor reconstruction (SPAR) with Takens delay coordinates with fit five classifiers: random forest, gradient boosting, random forest XGB, and support vector machines (SVM) with various number of decision tree-based estimators (100–10,000) and various kernels (linear, radial base function, and polynomial), respectively. The analysis was carried out on a public dataset “Mechanocardiograms with ECG reference” containing 29 concurrent ECG, SCG, and GCG signal recordings. The highest values without SMOTE were observed for ECG signals, SVM with fourth order polynomial kernel (accuracy of 0.6897, PPV of 0.6019, sensitivity of 0.5306, and F1 score of 0.4952), and after applying SMOTE were observed for Gradient Boosting in ECG signal (200 estimators, accuracy 0.7500, PPV of 0.7747, sensitivity of 0.7500, and F2 score of 0.7747 respectively). These findings suggest that the SPAR-based approach is a promising method to accurately assess the quality of cardiovascular signals, including seismocardiograms and gyrocardiograms.