Cardiac Signals Research Articles

Development of a novel four-channel atomic gradiometric magnetometer for magnetocardiography: Advancing non-invasive cardiac research with enhanced sensitivity and spatial resolution

The field of magnetocardiography (MCG) has witnessed significant advancements with the use of atomic magnetometers (AMs) and gradiometers, offering numerous advantages over traditional superconducting quantum interference devices (SQUIDs). This study focuses on the development of a highly sensitive four-channel atomic gradiometer specifically designed for measuring the biomagnetic fields of a rat’s heart. By utilizing gradiometric detection and monitoring deviations in the Larmor frequency of rubidium (Rb) atoms, this gradiometer captures cardiac signals with exceptional sensitivity and spatial resolution. One of the key challenges in utilizing AMs as gradiometers lies in minimizing cross-talk effects among the sensors to ensure accurate measurements. In this study, we successfully address this challenge, leading to precise and reliable data acquisition. Furthermore, our gradiometer demonstrates a linear response across a wide range of frequencies, enhancing its versatility and applicability in various experimental setups. The achieved sensitivity of 350 fT/√Hz by our atomic gradiometer showcases its potential in assessing MCG measurements for studying cardiac electrical activities. The non-invasive nature of the technique, combined with the elimination of cryogenic cooling requirements, opens up new avenues for cardiac research and diagnostics. The enhanced spatial and spatiotemporal resolution offered by optically pumped magnetometers (OPMs) further enhances our ability to understand and analyze complex cardiac phenomena.

Early detection of pulmonary arterial hypertension through [18F] positron emission tomography imaging with a vascular endothelial receptor small molecule.

The objective of this study is to provide a positron emission tomography (PET) imaging modality targeting vascular endothelial growth factor receptors (VEGFR) for the early noninvasive detection and assessment of pulmonary arterial hypertension (PAH) severity. To validate the effectiveness of the [18F]VEGFR PET tracer, we utilized a monocrotaline (MCT)-induced PAH rat model. Molecular optical imaging, using a Cy5.5-conjugated VEGFR targeting agent, was employed to demonstrate the uptake of the agent at pulmonary arterioles, correlating with the onset and progression of PAH. Histological examinations of the MCT-PAH rat lung revealed a significant correlation between VEGFR2 expression and the pathogenesis of PAH. Molecular optical imaging demonstrated heightened uptake of the Cy5.5-conjugated VEGFR targeting agent at pulmonary arterioles, corresponding with the onset and progression of PAH. [18F]VEGFR PET showed increased lung uptake detectable in early-stage PAH before increase in pulmonary artery pressures, and this uptake correlated with increased PAH severity. Moreover, when compared to [18F]FDG PET, [18F]VEGFR PET exhibited markedly lower background cardiac signal, enhancing imaging sensitivity for lung abnormalities. Our study provides a compelling evidence for the potential utility of the innovative [18F]VEGFR PET tracer, in non-invasively detecting early signs of PAH, and monitoring its progression. The observed correlations between VEGFR2 expression, molecular optical imaging results, and [18F]VEGFR PET findings support the use of this tracer for early detection, and assessment of PAH severity. The lower background cardiac signal observed with [18F]VEGFR PET further enhances its imaging sensitivity, emphasizing its potential clinical significance.

Low-cost electrocardiogram monitoring system for elderly people using LabVIEW

Cardiovascular diseases increase due to factors such as obesity, an inadequate diet, and are a problem due to shortages of medical personnel and hospitals. In this case, the implementation of technological solutions is presented as a necessity to prevent heart diseases. Various approaches are used to design low-cost electrocardiogram (ECG) devices, from the use of Bluetooth technology to facilitate data transmission, to the development of wearable ECG devices that use artificial intelligence. The objective is to develop a monitoring system in LabVIEW to visualize the heart rhythms of older adults in the city of Lima (Peru), focusing on ease of use and adaptation to their needs, with the purpose of collaboration between health professionals. A development approach is used that encompasses design, implementation, and iterative testing, as well as practical evaluations and pilot testing. As a result, the correct functioning of the ECG device was validated. Electronic components and electrodes were integrated into the board to capture cardiac signals, energized with batteries and sending the information to an interface in LabVIEW. In conclusion, a portable ECG device has been developed that uses operational amplifiers (Op-amps) and analog filters to reduce noise in cardiac measurements and an intuitive interface in LabVIEW

Central venous pressure waveform analysis during sleep/rest: a novel approach to enhance intensive care unit post-extubation monitoring of extubation failure.

This pilot study aimed to investigate the relation between cardio-respiratory parameters derived from Central Venous Pressure (CVP) waveform and Extubation Failure (EF) in mechanically ventilated ICU patients during post-extubation period. This study also proposes a new methodology for analysing these parameters during rest/sleep periods to try to improve the identification of EF. We conducted a prospective observational study, computing CVP-derived parameters including breathing effort, spectral analyses, and entropy in twenty critically ill patients post-extubation. The Dynamic Warping Index (DWi) was calculated from the respiratory component extracted from the CVP signal to identify rest/sleep states. The obtained parameters from EF patients and patients without EF were compared both during arbitrary periods and during reduced DWi (rest/sleep). We have analysed data from twenty patients of which nine experienced EF. Our findings may suggest significantly increased respiratory effort in EF patients compared to those successfully extubated. Our study also suggests the occurrence of significant change in the frequency dispersion of the cardiac signal component. We also identified a possible improvement in the differentiation between the two groups of patients when assessed during rest/sleep states. Although with caveats regarding the sample size, the results of this pilot study may suggest that CVP-derived cardio-respiratory parameters are valuable for monitoring respiratory failure during post-extubation, which could aid in managing non-invasive interventions and possibly reduce the incidence of EF. Our findings also indicate the possible importance of considering sleep/rest state when assessing cardio-respiratory parameters, which could enhance respiratory failure detection/monitoring.

Cardiac signals classification via optional multimodal multiscale receptive fields CNN-enhanced Transformer

The advancement in medical data collection technology has propelled an increase in demand for modeling cardiac physiological signals. However, current research primarily focuses on unimodal signals, leaving a gap in the study of more comprehensive multimodal signals. Directly applying late fusion specific to modalities or early fusion mixing modalities fails to adequately capture crossmodal information. This paper proposes an optional multimodal CNN-enhanced Transformer fusion network based on multiscale receptive fields. Introducing a switching modal experts for stage-wise representation, the first stage excavates modality-specific features and balances intermodal relationships, while the second stage captures crossmodal interaction information in a shared latent space, promoting deep modality fusion. Due to the flexibility of the switching modal experts, the model can be applied not only to multimodal data but also to unimodal data. Additionally, to address the performance disparity between Transformers and Convolutional Neural Networks (CNN), we combine the advantages of CNN to construct a CNN-enhanced Transformer. Specifically, improving patch embedding introduces multiscale receptive fields and integrates convolution and residual connections into the feed forward network (FFN) to assist the FFN in learning complex non-linear features and aggregating local features. Experimental results demonstrate that our model achieves outstanding performance in both unimodal and multimodal modes across different datasets, surpassing a range of CNNs, Transformers, and CNN-Transformer hybrid networks.

Beat Pilot Tone (BPT): Simultaneous MRI and RF motion sensing at arbitrary frequencies.

To introduce a simple system exploitation with the potential to turn MRI scanners into general-purpose radiofrequency (RF) motion monitoring systems. Inspired by Pilot Tone (PT), this work proposes Beat Pilot Tone (BPT), in which two or more RF tones at arbitrary frequencies are transmitted continuously during the scan. These tones create motion-modulated standing wave patterns that are sensed by the receiver coil array, incidentally mixed by intermodulation in the receiver chain, and digitized simultaneously with the MRI data. BPT can operate at almost any frequency as long as the intermodulation products lie within the bandwidth of the receivers. BPT's mechanism is explained in electromagnetic simulations and validated experimentally. Phantom and volunteer experiments over a range of transmit frequencies suggest that BPT may offer frequency-dependent sensitivity to motion. Using a semi-flexible anterior receiver array, BPT appears to sense cardiac-induced body vibrations at microwave frequencies ( 1.2 GHz). At lower frequencies, it exhibits a similar cardiac signal shape to PT, likely due to blood volume changes. Other volunteer experiments with respiratory, bulk, and head motion show that BPT can achieve greater sensitivity to motion than PT and greater separability between motion types. Basic multiple-input multiple-output ( MIMO) operation with simultaneous PT and BPT in head motion is demonstrated using two transmit antennas and a 22-channel head-neck coil. BPT may offer a rich source of motion information that is frequency-dependent, simultaneous, and complementary to PT and the MRI exam.

Inhibition of Extracellular Signal-Regulated Kinase Activity Improves Cognitive Function in Mice Subjected to Myocardial Infarction.

Cognitive impairment is a commonly observed complication following myocardial infarction; however, the underlying mechanisms are still not well understood. The most recent research suggests that extracellular signal-regulated kinase (ERK) plays a critical role in the development and occurrence of cognitive dysfunction-related diseases. This study aims to explore whether the ERK inhibitor U0126 targets the ERK/Signal Transducer and Activator of Transcription 1 (STAT1) pathway to ameliorate cognitive impairment after myocardial infarction. To establish a mouse model of myocardial infarction, we utilized various techniques including Echocardiography, Hematoxylin-eosin (HE) staining, Elisa, Open field test, Elevated plus maze test, and Western blot analysis to assess mouse cardiac function, cognitive function, and signal transduction pathways. For further investigation into the mechanisms of cognitive function and signal transduction, we administered the ERK inhibitor U0126 via intraperitoneal injection. Reduced total distance and activity range were observed in mice subjected to myocardial infarction during the open field test, along with decreased exploration of the open arms in the elevated plus maze test. However, U0126 treatment exhibited a significant improvement in cognitive decline, indicating a protective effect through the inhibition of the ERK/STAT1 signaling pathway. Hence, this study highlights the involvement of the ERK/STAT1 pathway in regulating cognitive dysfunction following myocardial infarction and establishes U0126 as a promising therapeutic target.

Voice reduction in cardiac auscultation sounds with reference signals measured from vocal resonators.

This study proposes the use of vocal resonators to enhance cardiac auscultation signals and evaluates their performance for voice-noise suppression. Data were collected using two electronic stethoscopes while each study subject was talking. One collected auscultation signal from the chest while the other collected voice signals from one of the three voice resonators (cheek, back of the neck, and shoulder). The spectral subtraction method was applied to the signals. Both objective and subjective metrics were used to evaluate the quality of enhanced signals and to investigate the most effective vocal resonator for noise suppression. Our preliminary findings showed a significant improvement after enhancement and demonstrated the efficacy of vocal resonators. A listening survey was conducted with thirteen physicians to evaluate the quality of enhanced signals, and they have received significantly better scores regarding the sound quality than their original signals. The shoulder resonator group demonstrated significantly better sound quality than the cheek group when reducing voice sound in cardiac auscultation signals. The suggested method has the potential to be used for the development of an electronic stethoscope with a robust noise removal function. Significant clinical benefits are expected from the expedited preliminary diagnostic procedure.

Transdiagnostic failure to adapt interoceptive precision estimates across affective, substance use, and eating disorders: A replication and extension of previous results

Recent Bayesian theories of interoception suggest that perception of bodily states rests upon a precision-weighted integration of afferent signals and prior beliefs. In a previous study, we fit a computational model of perception to behavior on a heartbeat tapping task to test whether aberrant precision-weighting could explain misestimation of cardiac states in psychopathology. We found that, during an interoceptive perturbation designed to amplify afferent signal precision (inspiratory breath-holding), healthy individuals increased the precision-weighting assigned to ascending cardiac signals (relative to resting conditions), while individuals with anxiety, depression, substance use disorders, and/or eating disorders did not. In this pre-registered study, we aimed to replicate and extend our prior findings in a new transdiagnostic patient sample (N = 285) similar to the one in the original study. As expected, patients in this new sample were also unable to adjust beliefs about the precision of cardiac signals – preventing the ability to accurately perceive changes in their cardiac state. Follow-up analyses combining samples from the previous and current study (N = 719) also afforded power to identify group differences between narrower diagnostic categories, and to examine predictive accuracy when logistic regression models were trained on one sample and tested on the other. With this confirmatory evidence in place, future studies should examine the utility of interoceptive precision measures in predicting treatment outcomes and test whether these computational mechanisms might represent novel therapeutic targets.

Electrocardiography Classification with Leaky Integrate-and-Fire Neurons in an Artificial Neural Network-Inspired Spiking Neural Network Framework.

Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely on a detailed analysis of ECG recordings to pinpoint deviations that are indicative of heart anomalies. This traditional method, while effective, demands significant expertise and is susceptible to inaccuracies due to its manual nature. In the realm of computational analysis, Artificial Neural Networks (ANNs) have gained prominence across various domains, which can be attributed to their superior analytical capabilities. Conversely, Spiking Neural Networks (SNNs), which mimic the neural activity of the brain more closely through impulse-based processing, have not seen widespread adoption. The challenge lies primarily in the complexity of their training methodologies. Despite this, SNNs offer a promising avenue for energy-efficient computational models capable of displaying a high-level performance. This paper introduces an innovative approach employing SNNs augmented with an attention mechanism to enhance feature recognition in ECG signals. By leveraging the inherent efficiency of SNNs, coupled with the precision of attention modules, this model aims to refine the analysis of cardiac signals. The novel aspect of our methodology involves adapting the learned parameters from ANNs to SNNs using leaky integrate-and-fire (LIF) neurons. This transfer learning strategy not only capitalizes on the strengths of both neural network models but also addresses the training challenges associated with SNNs. The proposed method is evaluated through extensive experiments on two publicly available benchmark ECG datasets. The results show that our model achieves an overall accuracy of 93.8% on the MIT-BIH Arrhythmia dataset and 85.8% on the 2017 PhysioNet Challenge dataset. This advancement underscores the potential of SNNs in the field of medical diagnostics, offering a path towards more accurate, efficient, and less resource-intensive analyses of heart diseases.

ECG imaging as a real time tool to guide left bundle branch pacing implant in patients with left bundle branch block and resynchronization therapy indication

Abstract Background Left bundle branch pacing (LBBP) has emerged as a promising alternative technique for cardiac resynchronization therapy (CRT). ECG imaging (ECGI) may provide an accurate assessment of electrical resynchronization using real-time maps during LBBP implant. Purpose To evaluate the usefulness of ECGI to guide LBBP implant, by analyzing the ventricular activation times and the degree of resynchronization at each screwing step during the lead implant. Methods Four patients with CRT indication and left bundle branch block (LBBB) referred for LBBP were prospectively enrolled. ECGI was performed during the implant procedure. Cardiac signals were recorded using ECGI and projected into an artificial intelligence-derived geometric model. Thus, generating imageless and non-invasive activation maps at defined stages during lead screwing: basal rhythm (LBBB), right ventricular (RV) septum, mid-septal, deep-septal, and finally pacing from the left bundle branch (according with guidelines ECG criteria). The following parameters were measured: a) total activation time (TAT); b) left ventricular activation time (LVAT); c) left ventricular dyssynchrony index (LVDI) (as a quantifier of intraventricular dyssynchrony); d) ventricular electrical uncoupling (VEU); and e) histogram overlap between LV and RV distribution (the last 2 markers of interventricular dyssynchrony). Results The study cohort comprised men with a median age of 68 [Q1 59- Q3 79], with a median basal LBBB QRS duration of 175 ms [160-188] and a median LVEF of 32% [28-39]. A shortening of the activation times and a decrease in intra and interventricular dyssynchrony was achieved when the final lead position was reached. See the results in Figure 2. Conclusion Real-time and imageless ECGI system may offer a novel approach for guiding LBBP and it is useful to ensure left ventricular resynchronization.ECGI to guide LBBP implantActivation and dyssynchrony measurements

Pectus carinatum, small pneumothorax, and Brugada patterns: First description and pathophysiological analysis

Pectus carinatum is a chest wall deformity where the breastbone and ribs protrude outward, sometimes referred to as “pigeon chest” due to the bird-like appearance of the chest. This condition occurs in approximately 1 in 1000 children, more commonly affecting boys than girls. Pectus carinatum tends to worsen as a child grows, especially during puberty, with about 15% of the children experiencing associated conditions like scoliosis. We present a case report of a young patient with pectus carinatum who experienced a small right apex pneumothorax and exhibited Brugada patterns on electrocardiogram tracing. This is the first clinical case reporting Brugada pattern presentation in both a patient with pectus carinatum and a small pneumothorax. We have conducted a clinical anatomical and electrocardiographic analysis to explore a physiopathological explanation for these findings, considering that our patient has no history or clinical signs of Brugada Syndrome. To further elucidate the clinical, anatomical, and electrocardiographic findings observed in our patient, detailed analyses were conducted. These investigations focused on how the pectus carinatum may influence electrocardiographic patterns through structural alterations of the thoracic wall, potentially modifying the transmural electrical field across the myocardium. This analysis aimed to explore a physiopathological explanation for the Brugada patterns observed, considering our patient’s unique thoracic anatomy and the absence of clinical Brugada Syndrome. We conclude that: 1. Pectus carinatum may create a window that facilitates the less resistive measurement of cardiac action potentials; 2. Small pneumothoraces can induce modifications in the cardiac electrical signal detected by surface electrodes, and pectus carinatum could unmask these modifications; and 3. Surface electrodes may measure subepicardial potentials that include and are modified by adjacent pericardial tissues, potentially resulting in alterations in transmural potential differences, thus producing fictitious Brugada patterns. Similarly, there is a possibility that the right ventricular outflow tract may be included within the subendocardial potentials.

Explainable AI for myocardial infarction using the vectorcardiogram

Abstract Background and purpose Myocardial infarction (MI), a widespread cardiovascular ailment, often lacks immediate patient awareness being early and accurate MI diagnosis crucial for lifesaving interventions. In artificial intelligence studies, ECG signals are extensively explored, while fewer examine the potential of vectorcardiogram (VCG) which offers a three-dimensional cardiac dipole representation. Traditional Machine Learning methods manually extract features; some studies advocate VCG features. Deep Learning, especially Convolutional Neural Networks (CNN), automates feature extraction. Although ECG contains redundant information from the VCG's 12 projections, no studies directly apply CNNs to VCG, nor assess the impact of redundancy on predictive ability. This study aims to bridge this gap by investigating CNNs' diagnostic capabilities with VCG inputs, specifically focusing on improving MI classification. Methods The PTB-XL database was used for this study, containing 10-seconds ECG data from various pathologies, including MI. The database encompasses 9,481 records from healthy patients and 4,129 records from patients with MI. Data was divided into 80% for training and 20% for testing. Consequently, the dataset was expanded to have 52% representing MI and 48% representing healthy cases. We designed a 1D-CNN classifier (Figure 1.A) with three convolutional layers. The network was trained using both ECG and VCG reconstruction through the Inverse Dower Transform. Results The trained model achieved accuracy values of 0.895 and 0.914 for the test set when utilizing ECG and VCG, respectively. These results underscore how the information condensation in the VCG contributes to an enhanced predictive capacity of the model. In a qualitative analysis, activation maps extracted from the CNN illustrate heightened activation in the QRS complex when the disease is present, contrasting with diminished activation in its absence. These maps depict the weight assigned to each signal segment, enabling the classification of signals into healthy and diseased categories. The presented figure exemplifies a notable activation in the QRS loop in the case of myocardial infarction (MI), aligning with the observed alteration in the cardiac signal associated with this pathology (Figure 1.B). Conclusion In this study, an interpretable CNN has been designed for MI detection, with the VCG yielding better results than the ECG for this task due to information compression. This provides a new insight for enhancing classification capability in Deep Learning models, allowing for advancements in the MI detection.

A multi-task learning model using RR intervals and respiratory effort to assess sleep disordered breathing

BackgroundSleep-disordered breathing (SDB) affects a significant portion of the population. As such, there is a need for accessible and affordable assessment methods for diagnosis but also case-finding and long-term follow-up. Research has focused on exploiting cardiac and respiratory signals to extract proxy measures for sleep combined with SDB event detection. We introduce a novel multi-task model combining cardiac activity and respiratory effort to perform sleep–wake classification and SDB event detection in order to automatically estimate the apnea–hypopnea index (AHI) as severity indicator.MethodsThe proposed multi-task model utilized both convolutional and recurrent neural networks and was formed by a shared part for common feature extraction, a task-specific part for sleep–wake classification, and a task-specific part for SDB event detection. The model was trained with RR intervals derived from electrocardiogram and respiratory effort signals. To assess performance, overnight polysomnography (PSG) recordings from 198 patients with varying degree of SDB were included, with manually annotated sleep stages and SDB events.ResultsWe achieved a Cohen’s kappa of 0.70 in the sleep–wake classification task, corresponding to a Spearman’s correlation coefficient (R) of 0.830 between the estimated total sleep time (TST) and the TST obtained from PSG-based sleep scoring. Combining the sleep–wake classification and SDB detection results of the multi-task model, we obtained an R of 0.891 between the estimated and the reference AHI. For severity classification of SBD groups based on AHI, a Cohen’s kappa of 0.58 was achieved. The multi-task model performed better than a single-task model proposed in a previous study for AHI estimation, in particular for patients with a lower sleep efficiency (R of 0.861 with the multi-task model and R of 0.746 with single-task model with subjects having sleep efficiency < 60%).ConclusionAssisted with automatic sleep–wake classification, our multi-task model demonstrated proficiency in estimating AHI and assessing SDB severity based on AHI in a fully automatic manner using RR intervals and respiratory effort. This shows the potential for improving SDB screening with unobtrusive sensors also for subjects with low sleep efficiency without adding additional sensors for sleep–wake detection.

Preliminary evidence for altered brain-heart coherence during anxiogenic movies

Abstract During states of anxiety, fundamental threat circuitry in the brain can increase heart rate via alterations in autonomic balance (increased sympathetic activity and parasympathetic withdrawal) and may serve to promote interoceptive integration and awareness of cardiac signals. Moreover, evidence indicates pathological anxiety could be associated with increased communication between the brain and the heart. Yet, this phenomenon remains not well understood. For instance, studies in this area have been conducted within the confines of tightly controlled experimental paradigms. Whether anxiety impacts brain-heart communication outside of such experimental settings, and in relatively more naturalistic contexts, is less clear. Here, we used a suspenseful movie fMRI paradigm to study induced anxiety (n = 29 healthy volunteers; Caltech Conte dataset; Kliemann et al., 2022). We predicted that brain responses across an anxiety-relevant “defensive response network” (amygdala, hypothalamus, periaqueductal gray, bed nucleus of the stria terminalis, dorsomedial prefrontal cortex, ventromedial prefrontal cortex, subgenual anterior cingulate, and anterior insula; Abend et al., 2022) would show increased coherence with heart rate as participants watched a suspenseful movie clip compared to a non-suspenseful movie clip. Counter to our predictions, we found decreased coherence between heart rate and brain responses during increased anxiety, namely in amygdala-prefrontal circuitry. We suggest these alterations may be underpinned by parasympathetic withdrawal and/or decreased interoceptive awareness during suspenseful movie-watching.

CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation.

Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly integrates causality and graph neural network (GNN) for cuffless continuous BP estimation. The first stage concentrates on the generation of a causal graph between BP and wearable features from the the perspective of causal inference, so as to identify features that are causally related to BP variations. This stage is pivotal for the identification of novel causal features from the causal graph beyond pulse transit time (PTT). We found these causal features empower better tracking in BP changes compared to PTT. For the second stage, a spatio-temporal GNN (STGNN) is utilized to learn from the causal graph obtained from the first stage. The STGNN can exploit both the spatial information within the causal graph and temporal information from beat-by-beat cardiac signals for refined cuffless continuous BP estimation. We evaluated the proposed method with three datasets that include 305 subjects (102 hypertensive patients) with age ranging from 20-90 and BP at different levels, with the continuous Finapres BP as references. The mean absolute difference (MAD) for estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 3.77 mmHg and 2.52 mmHg, respectively, which outperformed comparison methods. In all cases including subjects with different age groups, while doing various maneuvers that induces BP changes at different levels and with or without hypertension, the proposed CiGNN method demonstrates superior performance for cuffless continuous BP estimation. These findings suggest that the proposed CiGNN is a promising approach in elucidating the causal mechanisms of cuffless BP estimation and can substantially enhance the precision of BP measurement.

Examination of Cardiac Activity with ECG Monitoring Using Heart Rate Variability Methods.

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.

Rapid prototyping and facile customization of conductive hydrogel bioelectronics based on all laser process

Proper customization in size and shape is essential in implantable bioelectronics for stable bio-signal recording. Over the past decades, many researchers have heavily relied on conventional photolithography processes to fabricate implantable bioelectronics. Therefore, they could not avoid the critical limitation of high cost and complex processing steps to optimize bioelectronic devices for target organs with various sizes and shapes. Here, we propose rapid prototyping using all laser processes to fabricate customized bioelectronics. PEDOT:PSS is selectively irradiated by an ultraviolet (UV) pulse laser to form wet-stable conductive hydrogels that can softly interact with biological tissues (50 μm line width). The encapsulation layer is selectively patterned using the same laser source by UV-curing polymer networks (110 μm line width). For high stretchability (over 100%), mesh structures are made by the selective laser cutting process. Our rapid prototyping strategy minimizes the use of high-cost equipment, using only a single UV laser source to process the electrodes, encapsulation, and substrates that constitute bioelectronics without a photomask, enabling the prototyping stretchable microelectrode array with an area of 1 cm2 less than 10 min. We fabricated an optimized stretchable microelectrode array with low impedances (∼1.1 kΩ at 1 kHz) that can effectively record rat's cardiac signals with various health states.

Improved recovery of cardiac auscultation sounds using modified cosine transform and LSTM-based masking.

Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and making the most of telehealth. This research paper introduces an innovative approach that utilizes a modified cosine transform (MCT) and a masking strategy based on long short-term memory (LSTM) to effectively distinguish heart sounds and murmurs from background noise and interfering sounds. The MCT is used to capture the repeated pattern of the heart sounds, while the LSTMs are trained to construct masking based on the repeated MCT spectrum. The proposed strategy's performance in maintaining the clinical relevance of heart sounds continues to demonstrate effectiveness, even in environments marked by increased noise and complex disruptions. The present work highlights the clinical significance and reliability of the suggested methodology through in-depth signal visualization and rigorous statistical performance evaluations. In comparative assessments, the proposed approach has demonstrated superior performance compared to recent algorithms, such as LU-Net and PC-DAE. Furthermore, the system's adaptability to various datasets enhances its reliability and practicality. The suggested method is a potential way to improve the accuracy of cardiovascular diagnostics in an era of rapid advancement in medical signal processing. The proposed approach showed an enhancement in the average signal-to-noise ratio (SNR) by 9.6dB at an input SNR of - 6dB and by 3.3dB at an input SNR of 10dB. The average signal distortion ratio (SDR) achieved across a variety of input SNR values was 8.56dB.

Injectable Mesh-Like Conductive Hydrogel Patch for Elimination of Atrial Fibrillation.

Irregular electrical impulses in atrium are the leading cause of atrial fibrillation (AF), resulting in fatal arrhythmia and sudden cardiac death. Traditional medication and physical therapies are widely used, but generally suffer problems in serious physical damage and high surgical risks. Flexible and soft implants have great potential to be a novel approach for heart diseases therapy. A conductive hydrogel-based mesh cardiac patch is developed for application in AF elimination. The designed mesh patch with rhombic-shaped structure exhibits excellent flexibility, surface conformability, and deformation compliance, making it fit well with heart surface and accommodate to the deformation during heart beating. Moreover, the mechanical elastic and shape-memory properties of the mesh patch enable a minimally invasive injection of the patch into living animals. The mesh patch is implanted on the atrium surface for one month, indicating good biocompatibility and stability. Furthermore, the conductive patch can effectively eliminate AF owing to the conductivity and high charge storage capability (CSC) of the hydrogel. The proposed scheme of cardiac bioelectric signal modulation using conductive hydrogel brings new possibility for the treatment of arrhythmia diseases.