The development of accurate and fast methods for real-time electrocardiogram (ECG) analysis is mandatory in handheld fully automated monitoring devices for high-risk cardiac patients. The present work describes a simple software method for fast detection of pathological cardiac events. It implements real-time procedures for QRS detection, interbeat RR-intervals analysis, QRS waveform evaluation and a decision-tree beat classifier. Two QRS descriptors are defined to assess (i) the RR interval deviation from the mean RR interval and (ii) the QRS waveform deviation from the QRS pattern of the sustained rhythm. The calculation of the second parameter requires a specific technique, in order to satisfy the demand for straight signal processing with minimum iterations and small memory size. This technique includes fast and resource efficient estimation of a histogram matrix, which accumulates dynamically the amplitude-temporal distribution of the successive QRS pattern waveforms. The pilot version of the method is developed in Matlab and it is tested with internationally recognized ECG databases. The assessment of the online single lead QRS detector showed sensitivity and positive predictivity of above 99%. The classification rules for detection of pathological ventricular beats were defined empirically by statistical analysis. The attained specificity and sensitivity are about 99.5% and 95.7% for all databases and about 99.81% and 98.87% for the noise free dataset. The method is applicable in low computational cost systems for long-term ECG monitoring, such as intelligent holters, automatic event/alarm recorders or personal devices with intermittent wireless data transfer to a central terminal.

Herein, we investigate the potential application of a composite consisting of PEDOT:PSS/Graphene, deposited via spray coating on a flexible substrate, as an autonomous conducting film for applications in wearable biosensor devices. The stability of PEDOT:PSS/Graphene is assessed through electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and linear polarization (LP) during exposure to an artificial sweat electrolyte, while scanning electron microscopy (SEM) was employed to investigate the morphological changes in the layer following these. The results indicate that the layers exhibit predominant capacitive behavior in the potential range of −0.3 to 0.7 V vs. Ag/AgCl, with a cut-off frequency of approximately 1 kHz and retain 90% capacity after 500 cycles. Aging under exposure to air for 6 months leads only to a minor increase in impedance, demonstrating potential for storage under non-demanding conditions. However, prolonged exposure (>48 h) to the artificial sweat causes significant degradation, resulting in an impedance increase of over 1 order of magnitude. The observed degradation raises important considerations for the long-term viability of these layers in wearable biosensor applications, prompting the need for additional protective measures during prolonged use. These findings contribute to ongoing efforts to enhance the stability and reliability of conducting materials for biosensors in health care and biotechnology applications.

Electrodes based on PEDOT:PSS are gaining increasing importance as conductive electrodes and functional layers in various sensors and biosensors due to their easy processing and biocompatibility. This study investigates PEDOT:PSS/graphene layers deposited via spray coating on flexible PET substrates. The layers are characterized in terms of their morphology, roughness (via AFM and SEM), and electrochemical properties in artificial sweat using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The layers exhibit dominant capacitive behavior at low frequencies, with cut-off frequencies determined for thicker layers at 1 kHz. The equivalent circuit used to fit the EIS data reveals a resistance of about three orders of magnitude higher inside the layer compared to the charge transfer resistance at the solid/liquid interface. The capacitance values determined from the CV curves range from 54.3 to 122.0 mF m⁻². After 500 CV cycles in a potential window of 1 V (from −0.3 to 0.7 V), capacitance retention for most layers is around 94%, with minimal surface changes being observed in the layers. The results suggest practical applications for PEDOT:PSS/graphene layers, both for high-frequency impedance measurements related to the functioning of individual organs and systems, such as impedance electrocardiography, impedance plethysmography, and respiratory monitoring, and as capacitive electrodes in the low-frequency range, realized as layered PEDOT:PSS/graphene conductive structures for biosignal recording.

In recent years, flexible and wearable biosensor technologies have gained significant attention due to their potential to revolutionize healthcare monitoring. Among the various components involved in these biosensors, the electrode material plays a crucial role in ensuring accurate and reliable detection. In this regard, polymer electrodes, such as Poly(3,4 ethylenedioxythiophene): poly(styrenesulfonate), combined with graphene (PEDOT:PSS/graphene), have emerged as promising candidates due to their unique mechanical properties and excellent electrical conductivity. Understanding the mechanical behavior of these polymer electrodes on flexible substrates is essential to ensure the stability and durability of wearable biosensors. In this paper, PEDOT:PSS/graphene composite was spray-coated on flexible substrates at different growth conditions to explore the effect of the deposition parameters and mode of mechanical loading (longitudinal or transversal) on the electrical and mechanical behavior of the fabricated samples. It was found that the coating grown at lower temperatures and higher spraying pressure exhibited stable behavior no matter the applied stress type.

Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Global System for Mobile Communications) microphones. Thus, the wireless connection between the patient module and the cloud server can be provided over an audio channel, such as a standard telephone call or audio message. Patients, especially the elderly or visually impaired, can benefit from ECG sonification because the wireless interface is readily available, facilitating the communication and transmission of secure ECG data from the patient monitoring device to the remote server. The aim of this study is to develop an AI-driven algorithm for 12-lead ECG sonification to support diagnostic reliability in the signal processing chain of the audio ECG stream. Our methods present the design of two algorithms: (1) a transformer (ECG-to-Audio) based on the frequency modulation (FM) of eight independent ECG leads in the very low frequency band (300–2700 Hz); and (2) a transformer (Audio-to-ECG) based on a four-layer 1D convolutional neural network (CNN) to decode the audio ECG stream (10 s @ 11 kHz) to the original eight-lead ECG (10 s @ 250 Hz). The CNN model is trained in unsupervised regression mode, searching for the minimum error between the transformed and original ECG signals. The results are reported using the PTB-XL 12-lead ECG database (21,837 recordings), split 50:50 for training and test. The quality of FM-modulated ECG audio is monitored by short-time Fourier transform, and examples are illustrated in this paper and supplementary audio files. The errors of the reconstructed ECG are estimated by a popular ECG diagnostic toolbox. They are substantially low in all ECG leads: amplitude error (quartile range RMSE = 3–7 μV, PRD = 2–5.2%), QRS detector (Se, PPV > 99.7%), P-QRS-T fiducial points’ time deviation (<2 ms). Low errors generalized across diverse patients and arrhythmias are a testament to the efficacy of the developments. They support 12-lead ECG sonification as a wireless interface to provide reliable data for diagnostic measurements by automated tools or medical experts.

The present paper provides a review on applications of Low-resolution IR array sensors in Ambient-Assisted Living (AAL) monitoring systems. The review is focused on three main scenarios: indoor people detecting, localizing and counting; fall detection; indoor human activity detection and recognition. A comparative analysis of the reported results in the latest research studies has been carried out in terms of the type and position of the low-resolution IR array sensors used, the employed methods and image processing techniques and the achieved accuracy. The analysis shows that for fall detection and human activity recognition applications, the IR array sensors with $24\times 32$ pixels or multiple sensors arrangements with $4\times 16,\ 16\times 16$ and $24\times 32$ pixels can provide sufficient accuracy and still preserving user privacy.