Detail description of hardware and software of novel pulse-meter device – 4-channel photoplethysmograph CARDIOPULS has been presented. Above device is intended for noninvasive measurements of optical signals recorded from the pulse wave induced by the human heart. Hardware part includes 4 photometric channels, single- or 6 standard reference ECG channels, data processing unit, ECG electrodes, computer and software. Device can perform examination of the patients within 10-15 minutes at lying and sitting positions both in clinical environment and field location. Novel methods for data processing and device design were proposed. Device is aimed to diagnose of heart and vessel diseases.

This study explores the potential of unsupervised machine learning algorithms to identify latent cardiac risk profiles by analyzing ECG-derived parameters from two general groups: clinically healthy individuals (Norm dataset, n = 14,863) and patients hospitalized with heart failure (patients’ dataset, n = 8220). Each dataset includes 153 ECG and heart rate variability (HRV) features, including both conventional and novel diagnostic parameters obtained using a Universal Scoring System. The study aims to apply unsupervised clustering algorithms to ECG data to detect latent risk profiles related to heart failure, based on distinctive ECG features. The focus is on identifying patterns that correlate with cardiac health risks, potentially aiding in early detection and personalized care. We applied a combination of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and Hierarchical Density-Based Spatial Clustering (HDBSCAN) for unsupervised clustering. Models trained on one dataset were applied to the other to explore structural differences and detect latent predispositions to cardiac disorders. Both Euclidean and Manhattan distance metrics were evaluated. Features such as the QRS angle in the frontal plane, Detrended Fluctuation Analysis (DFA), High-Frequency power (HF), and others were analyzed for their ability to distinguish different patient clusters. In the Norm dataset, Euclidean distance clustering identified two main clusters, with Cluster 0 indicating a lower risk of heart failure. Key discriminative features included the “ALPHA QRS ANGLE IN THE FRONTAL PLANE” and DFA. In the patients’ dataset, three clusters emerged, with Cluster 1 identified as potentially high-risk. Manhattan distance clustering provided additional insights, highlighting features like “ST DISLOCATION” and “T AMP NORMALIZED” as significant for distinguishing between clusters. The analysis revealed distinct clusters that correspond to varying levels of heart failure risk. In the Norm dataset, two main clusters were identified, with one associated with a lower risk profile. In the patients’ dataset, a three-cluster structure emerged, with one subgroup displaying markedly elevated risk indicators such as high-frequency power (HF) and altered QRS angle values. Cross-dataset clustering confirmed consistent feature shifts between groups. These findings demonstrate the feasibility of ECG-based unsupervised clustering for early risk stratification. The results offer a non-invasive tool for personalized cardiac monitoring and merit further clinical validation. These findings emphasize the potential for clustering techniques to contribute to early heart failure detection and personalized monitoring. Future research should aim to validate these results in other populations and integrate these methods into clinical decision-making frameworks.

Detail description of hardware and software of novel Ukrainian generation of magnetocardiographic apparatus - cardiomagnetic scanner CARDIOMAGSCAN have been presented. This cheaper supersensitive device includes 4 signal SQUID-channels, electrocardiograph, and automatic electromechanical system for patient scanning. Scanner is enabled to perform observation of the human heart during 15 minutes at unshielded environment. Device is intended to non-invasive medical diagnostics of cardiology diseases with help of advanced software including early stages, difficult-to-diagnose cases, and asymptomatic forms.