This study describes an IoT-based health monitoring system designed to notify attending physicians when necessary. The developed IoT system incorporates sensors for ECG, PPG, and temperature; a gyroscope/accelerometer; and a microcontroller. A critical analysis of existing components in these areas was conducted to ensure the IoT system’s good performance, reliability, and suitability for continuous cardiac monitoring and data processing. This study addresses the challenge of monitoring cardiac activity in patients with arrhythmias, focusing on the differences in heart rate variability (HRV) parameters between healthy individuals and those with extrasystolic arrhythmia. The purpose of this research is to evaluate the effectiveness of IoT-based systems using PPG and ECG sensors for cardiac data registration and HRV analysis. The system leverages time domain and frequency domain methods for HRV analysis to assess the states of the autonomic nervous system. Significant differences were observed in HRV parameters, such as the SDNN, SDANN, RMSSD, and the LF/HF ratio. The results demonstrated that both the PPG and ECG methods provide comparable HRV measurements, despite PPG’s higher susceptibility to noise. This study concludes that IoT-based monitoring systems with PPG and ECG integration can reliably detect arrhythmias and offer real-time data for cardiac care.

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart.

The article presents a methodology to support the process of correct cardiodiagnostics based on cardio signals recorded with modern optical photoplethysmographic (PPG) sensor devices. An algorithm for preprocessing registered PPG signals and the formation of a time series for the analysis of heart rate variability is presented, which is an important information indicator in the diagnosis of cardiovascular diseases. In order to validate the proposed algorithm, an experimental scheme for synchronous recordings of PPG and electrocardiographic (ECG) signals and the study of the accuracy of the registered signals was created. The obtained results show high accuracy of the studied signals in terms of the following parameters: number of QRS complexes/pulse waves and mean RR intervals/PP intervals and the finding that the proposed algorithm is suitable for preprocessing PPG signals, as well as the possibility of interchangeable use of PPG and ECG. The results of the mathematical analysis of heart rate variability by applying linear methods (Time-Domain and Frequency-Domain) to two groups of people are presented: healthy controls and patients with cardiovascular disease (syncope). After determining the values of the parameters of the methods used, in order to distinguish healthy subjects from sick ones, statistical analysis was applied using t-test and Receiver Operating Characteristics (ROC) analysis. The obtained results show that the linear methods used are suitable for analysing the dynamics of PP interval series and for distinguishing healthy subjects from those with pathological diseases. The presented research and analyses can find applications in guaranteeing correctness and accuracy of conducting cardiodiagnostics in clinical practice.

This article introduces an integrated photoplethysmographic (PPG) based cardiovascular monitoring system that consists of an individually portable PPG device for recording photoplethysmographic signals and a software system with a serverless architecture for processing, storing, and analyzing the obtained signals. The portable device uses the optical plethysmography technique for measuring blood volume in blood vessels. The device was tested and validated by a comparative analysis of three photoplethysmographic signals and one Electrocardiographic signal registered simultaneously in the target subject. The comparative analysis of these signals shows insignificant deviations in the obtained results, with the mean squared error between the studied signals being less than 21 ms. This deviation cannot affect the results that were obtained from the analysis of the interval series tested. Based on this result, we assume that the detected signals with the proposed device are realistic. The designed software system processes the registered data, performs preprocessing, determines the pulse rate variability, and performs mathematical analysis of PP intervals. Two groups of subjects were studied: 42 patients with arrhythmia and 40 healthy controls. Mathematical methods for data analysis in time and frequency domain and nonlinear methods (Poincaré plots, Rescaled Range Plot, Detrended Fluctuation Analysis, and MultiFractal Detrended Fluctuation Analysis) are applied. The obtained results are presented in tabular form and some of them in graphical form. The parameters studied in the time and frequency domain, as well as with the nonlinear methods, have statistical significance (p < 0.05) and they can distinguish between the two studied groups. Visual analysis of PP intervals, based on Poincare’s nonlinear method, provides important information on the physiological status of patients, allowing for one to see at a glance the entire PP interval series and quickly detect cardiovascular disorders, if any. The photoplethysmographic data of healthy individuals and patients diagnosed with arrhythmia were recorded, processed, and examined through the system under the guidance of a cardiologist. The results were analyzed and it was concluded that this system could serve to monitor patients with cardiovascular diseases and, when the condition worsens, a signal could be generated and sent to the hospital for undertaking immediate measures to stabilize patient’s health.

The mathematical analysis and the assessment of heart rate variability (HRV) based on computer systems can assist the diagnostic process with determining the cardiac status of patients. The new cardio-diagnostic assisting computer system created uses the classic Time-Domain, Frequency-Domain, and Time-Frequency analysis indices, as well as the nonlinear methods (Poincaré plot, Recurrence plot, Hurst R/S method, Detrended Fluctuation Analysis (DFA), Multi-Fractal DFA, Approximate Entropy and Sample Entropy). To test the feasibility of the software developed, 24-hour Holter recordings of four groups of people were analysed: healthy subjects and patients with arrhythmia, heart failure and syncope. Time-Domain (SDNN < 50 ms, SDANN < 100 ms, RMSSD < 17 ms) and Frequency-Domain (the spectrum of HRV in the LF < 550 ms², and HF < 540 ms²) parameter values decreased in the cardiovascular disease groups compared to the control group as a result of lower HRV due to decreased parasympathetic and increased sympathetic activity. The results of the nonlinear analysis showed low values of (SD1 < 56 ms, SD2 < 110 ms) at Poincaré plot (Alpha < 90 ms) at DFA in patients with diseases. Significantly reducing these parameters are markers of cardiac dysfunction. The examined groups of patients showed an increase in the parameters (DET% > 95, REC% > 38, ENTR > 3.2) at the Recurrence plot. This is evidence of a pathological change in the regulation of heart rhythm. The system created can be useful in making the diagnosis by the cardiologist and in bringing greater accuracy and objectivity to the treatment.

Early detection of physiological dysregulation is critical for timely intervention and effective health management. Traditional monitoring systems often rely on labeled data and predefined thresholds, limiting their adaptability and generalization to unseen conditions. To address this, we propose a framework for label-free classification of physiological states using Heart Rate Variability (HRV), combined with unsupervised machine learning techniques. This approach is particularly valuable when annotated datasets are scarce or unavailable—as is often the case in real-world wearable and IoT-based health monitoring. In this study, data were collected from participants under controlled conditions representing rest, stress, and physical exertion. Core HRV parameters such as the SDNN (Standard Deviation of all Normal-to-Normal intervals), RMSSD (Root Mean Square of the Successive Differences), DFA (Detrended Fluctuation Analysis) were extracted. Principal Component Analysis was applied for dimensionality reduction. K-Means, hierarchical clustering, and Density-based spatial clustering of applications with noise (DBSCAN) were used to uncover natural groupings within the data. DBSCAN identified outliers associated with atypical responses, suggesting potential for early anomaly detection. The combination of HRV descriptors enabled unsupervised classification with over 90% consistency between clusters and physiological conditions. The proposed approach successfully differentiated the three physiological conditions based on HRV and fractal features, with a clear separation between clusters in terms of DFA α1, α2, LF/HF, and RMSSD (with high agreement to physiological labels (Purity ≈ 0.93; ARI = 0.89; NMI = 0.92)). Furthermore, DBSCAN identified three outliers with atypical autonomic profiles, highlighting the potential of the method for early warning detection in real-time monitoring systems.