Open access EEG dataset of repeated measurements from a single subject for microstate analysis.

Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that has received considerable attention in recent years and is widely used for analysing EEG signals. EEG is easily influenced by internal and external factors, which can affect the repeatability and stability of EEG microstate analysis. However, there have been few reports and publicly available datasets on the repeatability of EEG microstate analysis. In the current study, a 39-year-old healthy male underwent a total of 60 simultaneous electroencephalography and electrocardiogram measurements over a period of three months. After the EEG recording was completed, magnetic resonance imaging (MRI) was also conducted. To date, this EEG dataset has the highest number of repeated measurements for one individual. The dataset can be used to assess the stability and repeatability of EEG microstates and other analytical methods, to decode resting EEG states among subjects with open eyes, and to explore the stability and repeatability of cortical spatiotemporal dynamics through source analysis with individual MRI.

From physiological complexity to data interactions-A case study of recordings from exercise monitoring.

The popularity of nonlinear analysis has been growing simultaneously with the technology of effort monitoring. Therefore, considering the simple methods of physiological data collection and the approaches from the information domain, we proposed integrating univariate and bivariate analysis for the rest and effort comparison. Two sessions separated by an intensive training program were studied. Nine subjects participated in the first session (S1) and seven in the second session (S2). The protocol included baseline (BAS), exercise, and recovery phase. During all phases, electrocardiogram (ECG) was recorded. For the analysis, we selected corresponding data lengths of BAS and exercise usually lasting less than 5 min. We found the utility of the differences between original data and their surrogates for sample entropy Sdiff and Kullback-Leibler divergence KLDdiff. Sdiff of heart rate variability was negative in BAS and exercise but its sensitivity for phases discrimination was not satisfactory. We studied the bivariate analysis of RR intervals and corresponding QT peaks by Interlayer Mutual Information (IMI) and average edge overlap (AVO) markers. While the IMI parameter decreases in exercise conditions, AVO increased in effort compared to BAS. These findings conclude that researchers should consider a bivariate analysis of extracted RR intervals and corresponding QT datasets, when only ECG is recorded during tests.

Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA.

Arrhythmias are irregular heartbeat rhythms caused by various conditions. Automated ECG signal classification aids in diagnosing and predicting arrhythmias. Current studies mostly focus on 1D ECG signals, overlooking the fusion of multiple ECG modalities for enhanced analysis. We converted ECG signals into modal images using RP, GAF, and MTF, inputting them into our classification model. To optimize detail retention, we introduced a CNN-based model with FCA for multimodal ECG tasks. Achieving 99.6% accuracy on the MIT-BIH arrhythmia database for five arrhythmias, our method outperforms prior models. Experimental results confirm its reliability for ECG classification tasks.

T-wave inversion through inhomogeneous voltage diffusion within the FK3V cardiac model.

The heart beats are due to the synchronized contraction of cardiomyocytes triggered by a periodic sequence of electrical signals called action potentials, which originate in the sinoatrial node and spread through the heart's electrical system. A large body of work is devoted to modeling the propagation of the action potential and to reproducing reliably its shape and duration. Connection of computational modeling of cells to macroscopic phenomenological curves such as the electrocardiogram has been also intense, due to its clinical importance in analyzing cardiovascular diseases. In this work, we simulate the dynamics of action potential propagation using the three-variable Fenton-Karma model that can account for both normal and damaged cells through a the spatially inhomogeneous voltage diffusion coefficient. We monitor the action potential propagation in the cardiac tissue and calculate the pseudo-electrocardiogram that reproduces the R and T waves. The R-wave amplitude varies according to a double exponential law as a function of the (spatially homogeneous, for an isotropic tissue) diffusion coefficient. The addition of spatial inhomogeneity in the diffusion coefficient by means of a defected region representing damaged cardiac cells may result in T-wave inversion in the calculated pseudo-electrocardiogram. The transition from positive to negative polarity of the T-wave is analyzed as a function of the length and the depth of the defected region.

Electrocardiographic, biochemical, and scintigraphic evidence for the cardioprotective effect of paricalcitol and vitamin D3 on doxorubicin-induced acute cardiotoxicity in rats.

AIM: We aimed to investigate the possible cardioprotective effects of paricalcitol (PR), its vitamin D receptor agonist, and vitamin D3 (VIT-D3) on an experimental model of doxorubicin (DX) cardiotoxicity by 99mTc-PYP scintigraphy, electrocardiographic (ECG) and biochemical methods. METHOD: Forty-two male Wistar/Albino rats (250‒300 g; aged 10‒12 weeks) were randomly separated into six groups, namely into control (CN), doxorubicin (DX), paricalcitol (PR), vitamin D3 (VIT-D3), paricalcitol + doxorubicin (PR+DX), and vitamin D3 + doxorubicin (VIT-D3+DX) groups. Cardiotoxicity was induced by three doses of DX (18 mg/kg, i.p.) at 24-hour intervals on days 18, 19 and 20. PR (0.5 ug/ kg, i.p) and VIT-D3 (5,000 IU/kg, i.p) were injected for 20 days before and after the application of DX (18 mg/kg, i.p.). On day 21 of the experiment, biochemical parameters [tumor necrosis factor TNF-alpha (TNF-α); interleukin-6 (IL-6), nitric oxide (NO), and cardiac troponin T (cTnT)], as well as ECG and scintigraphic (99mTc-PYP) features were assessed. RESULTS: Compared to CN, DX significantly raised TNF-α, IL-6, and NO in heart tissue, cTnT in serum, 99mTc-PYP uptake in the myocardium, and ECG parameters, specifically QRS complex duration, QT interval duration, and ST-segment amplitude, while also reducing heart rate (p<0.001). Pretreatment with PR and VIT-D3 mitigated these abnormalities produced by DX in the heart (p<0.001). CONCLUSION: Results show that vitamin D receptor agonist paricalcitol and vitamin D protect against DX-induced cardiotoxicity through anti-inflammatory and antioxidant effects (Fig. 4, Ref. 59). Text in PDF www.elis.sk Keywords: paricalcitol, doxorubicin, vitamin D, ECG, 99mTc-PYP scintigraphy, cardiotoxicity, inflammation.

[Development of Wireless Wearable Sleep Monitoring System Based on EEG Signal].

A wireless wearable sleep monitoring system based on EEG signals is developed. The collected EEG signals are wirelessly sent to the PC or mobile phone Bluetooth APP for real-time display. The system is small in size, low in power consumption, and light in weight. It can be worn on the patient's forehead and is comfortable. It can be applied to home sleep monitoring scenarios and has good application value. The key performance indicators of the system are compared with the industry-related medical device measurement standards, and the measurement results are better than the special standards.

[Design and Development of ECG Monitoring Cloud Platform Based on the Internet of Things Electrocardiograph].

The design and development of electrocardiogram（ECG） monitoring cloud platform based on the Internet of Things（IoT） electrocardiograph is introduced. The platform is mainly composed of ECG acquisition module, algorithm module, diagnostic model comparison module, warning module, positioning module and medical aid system. The ECG acquisition module collects ECG signals of patients and displays them in real time on the mobile terminals. Then they are uploaded to the ECG monitoring cloud platform through the IoT. The algorithm module and the diagnostic model comparison module mark, process, analyze and diagnose the ECG. Meanwhile, the ECG diagnosis and warning results are pushed to patients and 120 emergency centers through the IoT. Furthermore, the cloud platform will guide patients to self-rescue and the emergency platform will open the green channel to save patients in time.The platform realizes from the onset to diagnosis and treatment in one step, and saves lives against time.

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography.

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

The effect of an anti-malarial herbal remedy, Maytenus senegalensis, on electrocardiograms of healthy Tanzanian volunteers.

BACKGROUND: The emergence of resistance to artemisinin-based combination therapy necessitates the search for new, more potent antiplasmodial compounds, including herbal remedies. The whole extract of Maytenus senegalensis has been scientifically investigated for potential biological activities both in vitro and in vivo, demonstrating strong antimalarial activity. However, there is a lack of data on the electrocardiographic effects of M. senegalensis in humans, which is a crucial aspect in the investigation of malaria treatment. Assessing the electrocardiographic effects of M. senegalensis is essential, as many anti-malarial drugs can inadvertently prolong the QT interval on electrocardiograms. Therefore, the study's objective was to evaluate the electrocardiographic effects of M. senegalensis in healthy adult volunteers. METHODS: This study is a secondary analysis of an open-label single-arm dose escalation. Twelve healthy eligible Tanzanian males, aged 18 to 45, were enrolled in four study dose groups. A single 12-lead electrocardiogram (ECG) was performed at baseline and on days 3, 7, 14, 28, and 56. RESULTS: No QTcF adverse events occurred with any drug dose. Only one volunteer who received the highest dose (800 mg) of M. senegalensis experienced a moderate transient change (△QTcF > 30 ms; specifically, the value was 37 ms) from baseline on day 28. There was no difference in maximum QTcF and maximum △QTcF between volunteers in all four study dose groups. CONCLUSIONS: A four-day regimen of 800 mg every 8 h of M. senegalensis did not impact the electrocardiographic parameters in healthy volunteers. This study suggests that M. senegalensis could be a valuable addition to malaria treatment, providing a safer alternative and potentially aiding in the battle against artemisinin-resistant malaria. The results of this study support both the traditional use and the modern therapeutic potential of M. senegalensis. They also set the stage for future research involving larger and more diverse populations to explore the safety profile of M. senegalensis in different demographic groups. This is especially important considering the potential use of M. senegalensis as a therapeutic agent and its widespread utilization as traditional medicine. Trial registration ClinicalTrials.gov, NCT04944966. Registered 30 June 2021-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04944966?term=kamaka&draw=2&rank=1.

Heart Rate Variability and Pulse Rate Variability: Do Anatomical Location and Sampling Rate Matter?

Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample at ~20-100 Hz, where the minimum sampling frequency to derive valid PRV metrics is unknown. Further, due to different autonomic innervation, it is unknown if PRV metrics are harmonious between the cerebral and peripheral vasculature. Cardiac activity via electrocardiography (ECG) and PPG were obtained concurrently in 54 participants (29 females) in an upright orthostatic position. PPG data were collected at three anatomical locations: left third phalanx, middle cerebral artery, and posterior cerebral artery using a Finapres NOVA device and transcranial Doppler ultrasound. Data were sampled for five minutes at 1000 Hz and downsampled to frequencies ranging from 20 to 500 Hz. HRV (via ECG) and PRV (via PPG) were quantified and compared at 1000 Hz using Bland-Altman plots and coefficient of variation (CoV). A sampling frequency of ~100-200 Hz was required to produce PRV metrics with a bias of less than 2%, while a sampling rate of ~40-50 Hz elicited a bias smaller than 20%. At 1000 Hz, time- and frequency-domain PRV measures were slightly elevated compared to those derived from HRV (mean bias: ~1-8%). In conjunction with previous reports, PRV and HRV were not surrogate biomarkers due to the different nature of the collected waveforms. Nevertheless, PRV estimates displayed greater validity at a lower sampling rate compared to HRV estimates.

Fundamental and Practical Feasibility of Electrocardiogram Reconstruction from Photoplethysmogram.

Electrocardiogram (ECG) reconstruction from contact photoplethysmogram (PPG) would be transformative for cardiac monitoring. We investigated the fundamental and practical feasibility of such reconstruction by first replicating pioneering work in the field, with the aim of assessing the methods and evaluation metrics used. We then expanded existing research by investigating different cycle segmentation methods and different evaluation scenarios to robustly verify both fundamental feasibility, as well as practical potential. We found that reconstruction using the discrete cosine transform (DCT) and a linear ridge regression model shows good results when PPG and ECG cycles are semantically aligned-the ECG R peak and PPG systolic peak are aligned-before training the model. Such reconstruction can be useful from a morphological perspective, but loses important physiological information (precise R peak location) due to cycle alignment. We also found better performance when personalization was used in training, while a general model in a leave-one-subject-out evaluation performed poorly, showing that a general mapping between PPG and ECG is difficult to derive. While such reconstruction is valuable, as the ECG contains more fine-grained information about the cardiac activity as well as offers a different modality (electrical signal) compared to the PPG (optical signal), our findings show that the usefulness of such reconstruction depends on the application, with a trade-off between morphological quality of QRS complexes and precise temporal placement of the R peak. Finally, we highlight future directions that may resolve existing problems and allow for reliable and robust cross-modal physiological monitoring using just PPG.

Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device.

Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10-3 comparing the DigiPen's computed Δt (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR =60Δt) from the pen accurately correlate with the reference ECG signals.

Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating photoplethysmogram (PPG) and electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls, our approach focuses on continuous, non-invasive monitoring. Key features, including the QRS interval, RR interval, augmentation index, heart rate, systolic pressure, diastolic pressure, and peak-to-peak amplitude, were carefully selected for their clinical relevance and ability to capture cardiovascular dynamics. This feature selection not only highlighted important physiological indicators but also helped reduce computational complexity and the risk of overfitting in machine learning models. The use of these features in training machine learning algorithms led to a model with impressive accuracy (98%), sensitivity (97.60%), specificity (96.90%), and precision (97.20%). Our integrated approach, combining PPG and ECG signals, demonstrates superior performance compared to single-signal strategies, emphasizing its potential in early and precise heart failure diagnosis. The study also highlights the importance of continuous monitoring with wearable technology, suggesting a significant stride forward in non-invasive cardiovascular health assessment. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Electrocardiogram prediction of atrial fibrillation risk after stroke: A protocol for systematic review and meta-analysis.

BACKGROUND: Atrial fibrillation (AF) is one of the most common clinical arrhythmias. This study aims to predict the risk of post-stroke AF through electrocardiographic changes in sinus rhythm. METHODS: We searched the MEDLINE (PubMed) and EMBASE databases to identify relevant research articles published until August 2023. Prioritized items from systematic reviews and meta-analyses were screened, and data related to AF detection rate were extracted. A meta-analysis using a random-effects model was conducted for data synthesis and analysis. RESULTS: A total of 32 studies involving electrocardiograms (ECG) were included, with a total analysis population of 330,284 individuals. Among them, 16,662 individuals (ECG abnormal group) developed AF, while 313,622 individuals (ECG normal group) did not. ECG patterns included terminal P-wave terminal force V1, interatrial block (IAB), advanced interatrial block, abnormal P-wave axis, pulse rate prolongation, and atrial premature complexes. Overall, 15,762 patients experienced AF during the study period (4.77%). In the ECG abnormal group, the proportion was 14.21% (2367/16,662), while in the control group (ECG normal group), the proportion was 4.27% (13,395/313,622). The pooled risk ratio for developing AF was 2.45 (95% confidence interval [CI]: 2.02-2.98, P < .001), with heterogeneity (I2) of 95%. The risk ratio values of alAB, P-wave terminal force V1, interatrial block, abnormal P-wave axis, pulse rate prolongation and atrial premature complexes were 4.12 (95% CI, 2.99-5.66), 1.47 (95% CI, 1.19-1.82), 2.54 (95% CI, 1.83-3.52), 1.70 (95% CI, 0.98-2.97), 2.65 (95% CI, 1.88-3.72), 3.79 (95% CI, 2.12-6.76), respectively. CONCLUSION: There is a significant correlation between ECG patterns and the occurrence of AF. The alAB exhibited the highest level of predictability for the occurrence of AF. These indicators support their use as screening tools to identify high-risk individuals who may benefit from further examinations or empirical anticoagulation therapy following stroke.

Characteristics of Patients With Acute Coronary Syndrome and Normal Electrocardiogram.

Background: Patients with acute coronary syndrome (ACS) and normal electrocardiogram (ECG) may have an increased risk of late diagnosis and complications of the disease. Objective: To study the demographic, angiographic and echocardiographic characteristics of patients hospitalized for ACS in whom the ECG was normal on admission to the hospital. Methods: This retrospective study included patients who were hospitalized for ACS without ST-elevation between 2015 and 2023 and who had coronary artery disease (CAD) confirmed by coronary angiography. By further inspection of the electronic databases, patients with ACS who had a normal ECG on admission were filtered out and analyzed separately. Results: Of the total 3137 patients with suspected ACS without ST-elevation, 129 patients (4.1%) were diagnosed as having ACS with a normal ECG. In three patients a non-atherosclerotic cause for the ACS was found. A significantly higher proportion of patients had single-vessel (54.3%) compared to two-vessel (29.5%) and three-vessel (14%) CAD. In addition to a normal ECG, 5.7% of patients with single-vessel CAD and 3.5% of patients with multi-vessel CAD had normal troponin levels and normal regional LV systolic function on echocardiography. Conclusion: Less than 5% of hospitalized patients with ACS without ST-elevation had a normal ECG on admission. The majority of these patients have single-vessel CAD. In about 5% of patients with single-vessel CAD, neither elevated troponin levels nor LV asynergy are detected.

Vericiguat improves cardiac function and microcirculation of a male patient with Fabry disease: A case report.

Fabry disease (FD) is a rare X chromosome-linked disorder and can be easily misdiagnosed. Here, we report the case of a 69-year-old male patient with FD who developed heart failure and showed extremely high pulmonary artery pressure. His initial symptom was recurrent atrial fibrillation. The left and right atrial inner diameters were large, and the ventricular wall was thick. Gene analysis which showed GLA c.215T>C p.Met72Thr mutation and single photon emission computed tomography indicated the diagnosis of FD with coronary microvascular dysfunction. The patient was prescribed anti-heart failure drugs, including vericiguat. Following the treatment, his heart function and microvascular perfusion significantly improved, which might be due to the beneficial effects of vericiguat.

The diagnostic efficiency of artificial intelligence based 2 hours Holter monitoring in premature ventricular and supraventricular contractions detection.

BACKGROUND: Electrocardiography (ECG) and 24 hours Holter monitoring (24 h-Holter) provided valuable information for premature ventricular and supraventricular contractions (PVC and PSVC). Currently, artificial intelligence (AI) based 2 hours single-lead Holter (2 h-Holter) monitoring may provide an improved strategy for PSVC/PVC diagnosis. HYPOTHESIS: AI combined with single-lead Holter monitoring improves PSVC/PVC detection. METHODS: In total, 170 patients were enrolled between August 2022 and 2023. All patients wore both devices simultaneously; then, we compared diagnostic efficiency, including the sensitivity/specificity/positive predictive-value (PPV) and negative predictive-value (NPV) in detecting PSVC/PVC by 24 h-Holter and 2 h-Holter. RESULTS: The PPV and NPV in patients underwent 2 h-Holter were 76.00%/87.50% and 96.35%/98.55, respectively, and the sensitivity and specificity were 79.17%/91.30%, and 95.65%/97.84% in PSVC/PVC detection compared with 24 h-Holter. The areas under the ROC curves (AUCs) for PSVC and PVC were 0.885 and 0.741, respectively (p < .0001). CONCLUSIONS: The potential advantages of the 2 h-Holter were shortened wearing period, improved convenience, and excellent consistency of diagnosis.

An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG).

Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.

[High-degree atrioventricular (AV) block]. / Borreliakardit ­ ovanlig orsak till atrioventrikulärt block.

In elderly patients high-degree atrioventricular (AV) block is often due to irreversible degeneration of the cardiac conduction system. Reversible causes must however be excluded prior to pacemaker implantation. In younger patients reversible causes are more likely, as well as more unusual etiologies. Lyme carditis is a rare, but reversible cause of AV block. It is a manifestation of Lyme borreliosis - an infectious disease caused by the bacteria Borrelia burgdorferi. Lyme carditis should particularly be considered in young and middle-aged patients with a high-degree AV block. When pretest probability is intermediate to high, a positive serological test makes the diagnosis of Lyme carditis highly likely. In these cases antibiotic treatment may revert the conduction disturbance, thus preventing unnecessary implantation of a permanent pacemaker.