New approach for T-wave end detection on electrocardiogram: performance in noisy conditions

Cardiac activity impacts cortical motor excitability

Human cognition and action can be influenced by internal bodily processes such as heartbeats. For instance, somatosensory perception is impaired both during the systolic phase of the cardiac cycle and when heartbeats evoke stronger cortical responses. Here, we test whether these cardiac effects originate from overall changes in cortical excitability. Cortical and corticospinal excitability were assessed using electroencephalographic and electromyographic responses to transcranial magnetic stimulation while concurrently monitoring cardiac activity with electrocardiography. Cortical and corticospinal excitability were found to be highest during systole and following stronger neural responses to heartbeats. Furthermore, in a motor task, hand-muscle activity and the associated desynchronization of sensorimotor oscillations were stronger during systole. These results suggest that systolic cardiac signals have a facilitatory effect on motor excitability-in contrast to sensory attenuation that was previously reported for somatosensory perception. Thus, it is possible that distinct time windows exist across the cardiac cycle, optimizing either perception or action.

Beat-wise segmentation of electrocardiogram using adaptive windowing and deep neural network

Timely detection of anomalies and automatic interpretation of an electrocardiogram (ECG) play a crucial role in many healthcare applications, such as patient monitoring and post treatments. Beat-wise segmentation is one of the essential steps in ensuring the confidence and fidelity of many automatic ECG classification methods. In this sense, we present a reliable ECG beat segmentation technique using a CNN model with an adaptive windowing algorithm. The proposed adaptive windowing algorithm can recognise cardiac cycle events and perform segmentation, including regular and irregular beats from an ECG signal with satisfactorily accurate boundaries.The proposed algorithm was evaluated quantitatively and qualitatively based on the annotations provided with the datasets and beat-wise manual inspection. The algorithm performed satisfactorily well for the MIT-BIH dataset with a 99.08% accuracy and a 99.08% of F1-score in detecting heartbeats along with a 99.25% of accuracy in determining correct boundaries. The proposed method successfully detected heartbeats from the European S-T database with a 98.3% accuracy and 97.4% precision. The algorithm showed 99.4% of accuracy and precision for Fantasia database. In summary, the algorithm's overall performance on these three datasets suggests a high possibility of applying this algorithm in various applications in ECG analysis, including clinical applications with greater confidence.

A Hardware-Software System for Accurate Segmentation of Phonocardiogram Signal

Background

Phonocardiogram (PCG) signal provides valuable information for diagnosing heart diseases. However, its applications in quantitative analyses of heart function are limited because the interpretation of this signal is difficult. A key step in quantitative PCG is the identification of the first and second sounds (S1 and S2) in this signal.

Objective

This study aims to develop a hardware-software system for synchronized acquisition of two signals electrocardiogram (ECG) and PCG and to segment the recorded PCG signal via the information provided in the acquired ECG signal.

Material and Methods

In this analytical study, we developed a hardware-software system for real-time identification of the first and second heart sounds in the PCG signal. A portable device to capture synchronized ECG and PCG signals was developed. Wavelet de-noising technique was used to remove noise from the signal. Finally, by fusing the information provided by the ECG signal (R-peaks and T-end) into a hidden Markov model (HMM), the first and second heart sounds were identified in the PCG signal.

Results

ECG and PCG signals from 15 healthy adults were acquired and analyzed using the developed system. The average accuracy of the system in correctly detecting the heart sounds was 95.6% for S1 and 93.4% for S2.

Conclusion

The presented system is cost-effective, user-friendly, and accurate in identifying S1 and S2 in PCG signals. Therefore, it might be effective in quantitative PCG and diagnosing heart diseases.

Electrocardiogram to Determine Mitral and Aortic Valve Opening and Closure

PURPOSE

Knowledge of the timing of cardiac valve opening and closing is important in cardiac physiology. The relationship between valve motion and electrocardiogram (ECG) is often assumed, however is not clearly defined. Here we investigate the accuracy of cardiac valve timing estimated using only the ECG, compared to Doppler echocardiography (DE) flow imaging as the gold standard.

METHODS

DE was obtained in 37 patients with simultaneous ECG recording. ECG was digitally processed and identifiable features (QRS, T, P waves) were examined as potential reference points to determine opening and closure of aortic and mitral valves, as compared to DE outflow and inflow measurement. Timing offset of the cardiac valves opening and closure between ECG features and DE was measured from derivation set (n = 19). The obtained mean offset in combination with the ECG features model was then evaluated on a validation set (n = 18). Using the same approach, additional measurement was also done for the right sided valves.

RESULTS

From the derivation set, we found a fixed offset of 22 ± 9 ms, 2 ± 13 ms, 90 ± 26 ms, and - 2 ± - 27 ms when comparing S to aortic valve opening, Tend to aortic valve closure, Tend to mitral valve opening, and R to mitral valve closure respectively. Application of this model to the validation set showed good estimation of aortic and mitral valve opening and closure timing value, with low model absolute error (median of the mean absolute error of the four events = 19 ms compared to the gold standard DE measurement). For the right-sided (tricuspid and pulmonic) valves in our patient set, there was considerably higher median of the mean absolute error of 42 ms for the model.

CONCLUSION

ECG features can be used to estimate aortic and mitral valve timings with good accuracy as compared to DE, allowing useful hemodynamic information to be derived from this easily available test.

COVID-19 contact tracing as an indicator for evaluating the pandemic situation: a simulation study

BACKGROUND

Contact tracing is a fundamental intervention in Public Health. When systematically applied, it enables the breaking of chains of transmission, important in controlling COVID-19 transmission. In a theoretically perfect contact tracing, all new cases should occur among quarantined individuals and an epidemic should vanish. However, the availability of resources influences the capacity to perform contact tracing. Therefore, it prompts the need to estimate its effectiveness threshold. We propose that this effectiveness threshold may be indirectly estimated by the ratio of COVID-19 cases arising from quarantined high-risk contacts, where higher ratios indicate better control and under a threshold contact tracing may fail and other restrictions become necessary.

OBJECTIVE

To study the ratio of COVID-19 cases in high-risk contacts quarantined through contact tracing and its potential use as an ancillary pandemic control indicator.

METHODS

We built a 6-compartment epidemiological model to emulate COVID-19 infection flow according to publicly available data from Portuguese authorities. Our model extended the usual SEIR model by adding a compartment Q with individuals in mandated quarantine that could develop infection or return to the susceptible pool; and a compartment P with individuals protected from infection due to vaccination. To model infection dynamics, data on SARS-CoV-2 infection risk, time until infection and vaccine efficacy were collected. Estimation was needed for vaccine data, to reflect the timing of inoculation and booster efficacy. Two simulations were built: the first (A) adjusting for the presence and absence of variants or vaccination, and the second (B) maximizing infection risk in quarantined individuals. Both simulations were based on a set of 100 unique parameterizations. The daily ratio of infected cases arising from high-risk contacts (q estimate) was calculated. A theoretical effectiveness threshold of contact tracing was defined for 14-day average q estimates based on the classification of COVID-19 daily cases according to the Pandemic phases and was compared with the timing of population lockdowns in Portugal. A sensitivity analysis was performed to understand the relation between different parameter values and the threshold obtained.

RESULTS

An inverse relationship was found between the q estimate and the daily cases in both simulations (correlations over -0.70). The theoretical effectiveness thresholds for both simulations attained an Alert phase predictive positive value of over 70% and could have anticipated the need for additional measures in at least 4 days for the second and fourth lockdowns. Sensitivity analysis showed that only the infection risk and the booster dose efficacy at inoculation significantly impacted the q estimates.

CONCLUSIONS

We demonstrate the impact of applying an effectiveness threshold for contact tracing in decision-making. While only theoretical thresholds could be provided, their relationship with the number of confirmed cases and prediction of pandemic phases shows the role as indirect indicator of the efficacy of contact tracing.

CLINICALTRIAL

Temporal change in repolarization parameters after surgical correction of valvular heart diseases

BACKGROUND

Ventricular tachyarrhythmia is a potentially fatal outcome of cardiac surgery. Abrupt changes in the hemodynamics after surgical correction of valvular heart disease (VHD) can lead to alterations in ventricular repolarization. We compared the difference between temporal changes in repolarization parameters after correction of left-sided VHD.

METHODS

We retrospectively analyzed the electrograms of patients who underwent surgical correction of isolated VHD between 2006 and 2015 at Asan Medical Center, including mitral stenosis (MS), mitral regurgitation (MR), aortic stenosis (AS), and aortic regurgitation (AR). Ventricular repolarization parameters were measured at pre-specified time intervals after index surgery using a custom-made ECG analysis program. We compared repolarization parameters, including QT and corrected QT intervals, T peak-to-end interval, and corrected T peak-to-end interval.

RESULTS

Analysis of 8265 ECGs from 2110 patients (266 MS, 1059 MR, 421 AS, and 364 AR) was performed. Patients with AS were characterized by older age and more comorbidities than other VHDs. The corrected QT interval showed a peak value immediately after surgery and decreased thereafter in the AS groups. However, a gradual increase over 1 month after surgery in AR, MS, and MR groups was observed. The corrected T peak-to-end interval increased in the MS and MR groups and was unchanged in the AS and AR groups.

CONCLUSIONS

The repolarization parameters of surgery changed dynamically after left-sided valvular surgery. Understanding differential temporal change of repolarization parameters according to the type of VHD would help clinicians avoid fatal arrhythmias related to the repolarization changes.

A New Deep Learning Method with Self-Supervised Learning for Delineation of the Electrocardiogram

Heartbeat characteristic points are the main features of an electrocardiogram (ECG), which can provide important information for ECG-based cardiac diagnosis. In this manuscript, we propose a self-supervised deep learning framework with modified Densenet to detect ECG characteristic points, including the onset, peak and termination points of P-wave, QRS complex wave and T-wave. We extracted high-level features of ECG heartbeats from the QT Database (QTDB) and two other larger datasets, MIT-BIH Arrhythmia Database (MITDB) and MIT-BIH Normal Sinus Rhythm Database (NSRDB) with no human-annotated labels as pre-training. By applying different transformations to ECG signals, the task of discriminating signals before and after transformation was defined as the pretext task. Subsequently, the convolutional layer was frozen and the weights of the self-supervised network were transferred to the downstream task of characteristic point localizations on heart beats in the QT dataset. Finally, the mean ± standard deviation of the detection errors of our proposed self-supervised learning method in QTDB for detecting the onset, peak, and termination points of P-waves, the onset and termination points of QRS waves, and the peak and termination points of T-waves were -0.24 ± 10.04, -0.48 ± 11.69, -0.28 ± 10.19, -3.72 ± 8.18, -4.12 ± 13.54, -0.68 ± 20.42, and 1.34 ± 21.04. The results show that the deep learning network based on the self-supervised framework constructed in this manuscript can accurately detect the feature points of a heartbeat, laying the foundation for automatic extraction of key information related to ECG-based diagnosis.

Independent Detection of T-Waves in Single Lead ECG Signal Using Continuous Wavelet Transform

INTRODUCTION

In the ECG signals, T-waves play a very important role in the detection of cardiac arrest. During myocardial ischemia, the first significant change occurs on the T-wave. These waves are generated due to the repolarization of the heart ventricle. The independent detection of T-waves is a bit challenging due to its variable nature, therefore, most of the algorithms available in the literature for T-wave detection use the detection of the QRS complex as the starting point. But accurate detection of Twave is very much required, as clinically, the first indication of a shortage of blood supply to the heart muscle (myocardial ischemia) shows up as changes in T-wave followed by other changes in the morphology of the ECG signal.

MATERIALS AND METHODS

In this paper, an efficient and novel algorithm based on Continuous Wavelet Transform (CWT) is presented to detect the Twave independently. In CWT, for better matching, a new mother wavelet is designed using the pattern and shape of the Twave. This algorithm is validated on all the signals of the QT database.

CONCLUSION

The algorithm attains an average sensitivity of 99.88% and positive predictivity of 99.81% for the signals annotated by the cardiologists in the database.

Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning

The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.

Cardio PyMEA: A user-friendly, open-source Python application for cardiomyocyte microelectrode array analysis

Open source analytical software for the analysis of electrophysiological cardiomyocyte data offers a variety of new functionalities to complement closed-source, proprietary tools. Here, we present the Cardio PyMEA application, a free, modifiable, and open source program for the analysis of microelectrode array (MEA) data obtained from cardiomyocyte cultures. Major software capabilities include: beat detection; pacemaker origin estimation; beat amplitude and interval; local activation time, upstroke velocity, and conduction velocity; analysis of cardiomyocyte property-distance relationships; and robust power law analysis of pacemaker spatiotemporal instability. Cardio PyMEA was written entirely in Python 3 to provide an accessible, integrated workflow that possesses a user-friendly graphical user interface (GUI) written in PyQt5 to allow for performant, cross-platform utilization. This application makes use of object-oriented programming (OOP) principles to facilitate the relatively straightforward incorporation of custom functionalities, e.g. power law analysis, that suit the needs of the user. Cardio PyMEA is available as an open source application under the terms of the GNU General Public License (GPL). The source code for Cardio PyMEA can be downloaded from Github at the following repository: https://github.com/csdunhamUC/cardio_pymea.

Semantic Segmentation of 12-Lead ECG Using 1D Residual U-Net with Squeeze-Excitation Blocks

Analyzing biomedical data is a complex task that requires specialized knowledge. The development of knowledge and technology in the field of deep machine learning creates an opportunity to try and transfer human knowledge to the computer. In turn, this fact influences the development of systems for the automatic evaluation of the patient’s health based on data acquired from sensors. Electrocardiography (ECG) is a technique that enables visualizing the electrical activity of the heart in a noninvasive way, using electrodes placed on the surface of the skin. This signal carries a lot of information about the condition of heart muscle. The aim of this work is to create a system for semantic segmentation of the ECG signal. For this purpose, we used a database from Lobachevsky University available on Physionet, containing 200, 10-second, and 12-lead ECG signals with annotations, and applied one-dimensional U-Net with the addition of squeeze-excitation blocks. The created model achieved a set of parameters indicating high performance (for the test set: accuracy—0.95, AUC—0.99, specificity—0.95, sensitivity—0.99) in extracting characteristic parts of ECG signal such as P and T-waves and QRS complex, regardless of the lead.

The Cardiac Timing Toolbox (CaTT): Testing for physiologically plausible effects of cardiac timing on behaviour

There is a long history of, and renewed interest in, cardiac timing effects on behaviour and cognition. Cardiac timing effects may be identified by expressing events as a function of their location in the cardiac cycle, and applying circular (i.e. directional) statistics to test cardiac time-behaviour associations. Typically this approach 'stretches' all points in the cardiac cycle equally, but this is not necessarily physiologically valid. Moreover, many tests impose distributional assumptions that are not met by such data. We present a set of statistical techniques robust to this, instantiated within our new Cardiac Timing Toolbox (CaTT) for MATLAB: A physiologically-motivated method of wrapping behaviour to the cardiac cycle; and a set of non-parametric statistical tests that control for common confounds and distributional characteristics of these data. Using a reanalysis of previously published data, we guide readers through analyses using CaTT, aiding researchers in identifying physiologically plausible associations between heart-timing and cognition.

Respiration, Heartbeat, and Conscious Tactile Perception

Previous studies have shown that timing of sensory stimulation during the cardiac cycle interacts with perception. Given the natural coupling of respiration and cardiac activity, we investigated here their joint effects on tactile perception. Forty-one healthy female and male human participants reported conscious perception of finger near-threshold electrical pulses (33% null trials) and decision confidence while electrocardiography, respiratory activity, and finger photoplethysmography were recorded. Participants adapted their respiratory cycle to expected stimulus onsets to preferentially occur during late inspiration/early expiration. This closely matched heart rate variation (sinus arrhythmia) across the respiratory cycle such that most frequent stimulation onsets occurred during the period of highest heart rate probably indicating highest alertness and cortical excitability. Tactile detection rate was highest during the first quadrant after expiration onset. Interindividually, stronger respiratory phase-locking to the task was associated with higher detection rates. Regarding the cardiac cycle, we confirmed previous findings that tactile detection rate was higher during diastole than systole and newly specified its minimum at 250-300 ms after the R-peak corresponding to the pulse wave arrival in the finger. Expectation of stimulation induced a transient heart deceleration which was more pronounced for unconfident decision ratings. Interindividually, stronger poststimulus modulations of heart rate were linked to higher detection rates. In summary, we demonstrate how tuning to the respiratory cycle and integration of respiratory-cardiac signals are used to optimize performance of a tactile detection task.SIGNIFICANCE STATEMENT Mechanistic studies on perception and cognition tend to focus on the brain neglecting contributions of the body. Here, we investigated how respiration and heartbeat influence tactile perception: respiration phase-locking to expected stimulus onsets corresponds to highest heart rate (and presumably alertness/cortical excitability) and correlates with detection performance. Tactile detection varies across the heart cycle with a minimum when the pulse reaches the finger and a maximum in diastole. Taken together with our previous finding of unchanged early event-related potentials across the cardiac cycle, we conclude that these effects are not a peripheral physiological artifact but a result of cognitive processes that model our body's internal state, make predictions to guide behavior, and might also tune respiration to serve the task.

Heartbeat and somatosensory perception

Our perception of the external world is influenced by internal bodily signals. For example, we recently showed that timing of stimulation along the cardiac cycle and spontaneous fluctuations of heartbeat-evoked potential (HEP) amplitudes influence somatosensory perception and the associated neural processing (Al et al., 2020). While cardiac phase affected detection sensitivity and late components of the somatosensory-evoked potentials (SEPs), HEP amplitudes affected detection criterion and both early and late SEP components. In a new EEG study, we investigate whether these results are replicable in a modified paradigm, which includes two succeeding temporal intervals. In one of the intervals, subjects received a weak electrical finger stimulation and reported first whether they detected any stimulation and then allocated the stimulus to one of the two intervals. Our results confirm the previously reported cardiac cycle and prestimulus HEP effects on somatosensory perception and evoked potentials. In addition, we obtained two new findings. Source analyses in this and our original study show that the increased likelihood of conscious perception goes along with HEP fluctuations in parietal and posterior cingulate regions, known to play important roles in interoceptive processes. Furthermore, HEP amplitudes were shown to decrease when subjects engaged in the somatosensory task compared to a resting state condition. Our findings are consistent with the view that HEP amplitudes are a marker of interoceptive (versus exteroceptive) attention and provide a neural underpinning for this view.

A New Method for Detecting Myocardial Ischemia Based on ECG T-Wave Area Curve (TWAC)

In recent years, coronary heart disease (CHD) has become one of the main diseases that endanger human health, with a high mortality and disability rate. Myocardial ischemia (MI) is the main symptom in the development of CHD. Continuous and severe myocardial ischemia will lead to myocardial infarction. The clinical manifestations of MI are mainly the changes of ST-T segment of ECG, that is, ST segment and T wave. Nearly one third of patients with CHD, however, has no obvious ECG changes. In this paper, a new method for detecting MI based on the T-wave area curve (TWAC) was proposed. Through observation and analysis of clinical data, it was found that there exist significant correlation between the morphology of TWAC and MI. The TWAC morphology of normal subject is smooth and gentle, while the TWAC morphology of patients with coronary stenosis is mostly jagged, and the curve becomes more severe with more severe stenosis. The preliminary test results show that the sensitivity, specificity, and accuracy of the proposed method for detecting MI are 84.3, 83.6, and 84%, respectively. This study shows that the TWAC based approach may be an effective method for detecting MI, especially for the CHD patients with no obvious ECG changes.

Robust T-End Detection via T-End Signal Quality Index and Optimal Shrinkage

An automatic accurate T-wave end (T-end) annotation for the electrocardiogram (ECG) has several important clinical applications. While there have been several algorithms proposed, their performance is usually deteriorated when the signal is noisy. Therefore, we need new techniques to support the noise robustness in T-end detection. We propose a new algorithm based on the signal quality index (SQI) for T-end, coined as tSQI, and the optimal shrinkage (OS). For segments with low tSQI, the OS is applied to enhance the signal-to-noise ratio (SNR). We validated the proposed method using eleven short-term ECG recordings from QT database available at Physionet, as well as four 14-day ECG recordings which were visually annotated at a central ECG core laboratory. We evaluated the correlation between the real-world signal quality for T-end and tSQI, and the robustness of proposed algorithm to various additive noises of different types and SNR’s. The performance of proposed algorithm on arrhythmic signals was also illustrated on MITDB arrhythmic database. The labeled signal quality is well captured by tSQI, and the proposed OS denoising help stabilize existing T-end detection algorithms under noisy situations by making the mean of detection errors decrease. Even when applied to ECGs with arrhythmia, the proposed algorithm still performed well if proper metric is applied. We proposed a new T-end annotation algorithm. The efficiency and accuracy of our algorithm makes it a good fit for clinical applications and large ECG databases. This study is limited by the small size of annotated datasets.

Mapping Spatiotemporal Diffusion of COVID-19 in Lombardy (Italy) on the Base of Emergency Medical Services Activities

The epidemic of coronavirus-disease-2019 (COVID-19) started in Italy with the first official diagnosis on 21 February 2020; hence, it is now known how many cases were already present in earlier days and weeks, thus limiting the possibilities of conducting any retrospective analysis. We hypothesized that an unbiased representation of COVID-19 diffusion in these early phases could be inferred by the georeferenced calls to the emergency number relevant to respiratory problems and by the following emergency medical services (EMS) interventions. Accordingly, the aim of this study was to identify the beginning of anomalous trends (change in the data morphology) in emergency calls and EMS ambulances dispatches and reconstruct COVID-19 spatiotemporal evolution on the territory of Lombardy region. Accordingly, a signal processing method, previously used to find morphological features on the electrocardiographic signal, was applied on a time series representative of territorial clusters of about 100,000 citizens. Both emergency calls and age- and gender-weighted ambulance dispatches resulted strongly correlated to COVID-19 casualties on a provincial level, and the identified local starting days anticipated the official diagnoses and casualties, thus demonstrating how these parameters could be effectively used as early indicators for the spatiotemporal evolution of the epidemic on a certain territory.

Heart-brain interactions shape somatosensory perception and evoked potentials

Even though humans are mostly not aware of their heartbeats, several heartbeat-related effects have been reported to influence conscious perception. It is not clear whether these effects are distinct or related phenomena, or whether they are early sensory effects or late decisional processes. Combining electroencephalography and electrocardiography, along with signal detection theory analyses, we identify two distinct heartbeat-related influences on conscious perception differentially related to early vs. late somatosensory processing. First, an effect on early sensory processing was found for the heartbeat-evoked potential (HEP), a marker of cardiac interoception. The amplitude of the prestimulus HEP negatively correlated with localization and detection of somatosensory stimuli, reflecting a more conservative detection bias (criterion). Importantly, higher HEP amplitudes were followed by decreases in early (P50) as well as late (N140, P300) somatosensory-evoked potential (SEP) amplitudes. Second, stimulus timing along the cardiac cycle also affected perception. During systole, stimuli were detected and correctly localized less frequently, relating to a shift in perceptual sensitivity. This perceptual attenuation was accompanied by the suppression of only late SEP components (P300) and was stronger for individuals with a more stable heart rate. Both heart-related effects were independent of alpha oscillations' influence on somatosensory processing. We explain cardiac cycle timing effects in a predictive coding account and suggest that HEP-related effects might reflect spontaneous shifts between interoception and exteroception or modulations of general attentional resources. Thus, our results provide a general conceptual framework to explain how internal signals can be integrated into our conscious perception of the world.

Interactions between cardiac activity and conscious somatosensory perception

Fluctuations in the heart's activity can modulate the access of external stimuli to consciousness. The link between perceptual awareness and cardiac signals has been investigated mainly in the visual and auditory domain. Here, we investigated whether the phase of the cardiac cycle and the prestimulus heart rate influence conscious somatosensory perception. We also tested how conscious detection of somatosensory stimuli affects the heart rate. Electrocardiograms (ECG) of 33 healthy volunteers were recorded while applying near-threshold electrical pulses at a fixed intensity to the left index finger. Conscious detection was not uniformly distributed across the cardiac cycle but significantly higher in diastole than in systole. We found no evidence that the heart rate before a stimulus influenced its detection, but hits (correctly detected somatosensory stimuli) led to a more pronounced cardiac deceleration than misses. Our findings demonstrate interactions between cardiac activity and conscious somatosensory perception, which highlights the importance of internal bodily states for sensory processing beyond the auditory and visual domain.

An Improved Sliding Window Area Method for T Wave Detection

BACKGROUND

The T wave represents ECG repolarization, whose detection is required during myocardial ischemia, and the first significant change in the ECG signal is being observed in the ST segment followed by changes in other waves like P wave and QRS complex. To offer guidance in clinical diagnosis, decision-making, and daily mobile ECG monitoring, the T wave needs to be detected firstly. Recently, the sliding area-based method has received an increasing amount of attention due to its robustness and low computational burden. However, the parameter setting of the search window's boundaries in this method is not adaptive. Therefore, in this study, we proposed an improved sliding window area method with more adaptive parameter setting for T wave detection.

METHODS

Firstly, k-means clustering was used in the annotated MIT QT database to generate three piecewise functions for delineating the relationship between the RR interval and the interval from the R peak to the T wave onset and that between the RR interval and the interval from the R peak to the T wave offset. Then, the grid search technique combined with 5-fold cross validation was used to select the suitable parameters' combination for the sliding window area method.

RESULTS

With respect to onset detection in the QT database, F1 improved from 54.70% to 70.46% and 54.05% to 72.94% for the first and second electrocardiogram (ECG) channels, respectively. For offset detection, F1 also improved in both channels as it did in the European ST-T database.

CONCLUSIONS

F1 results from the improved algorithm version were higher than those from the traditional method, indicating a potentially useful application for the proposed method in ECG monitoring.

Quantitative Evaluation of the Relationship between T-Wave-Based Features and Serum Potassium Level in Real-World Clinical Practice

BACKGROUND

Proper management of hyperkalemia that leads to fatal cardiac arrhythmia has become more important because of the increased prevalence of hyperkalemia-prone diseases. Although T-wave changes in hyperkalemia are well known, their usefulness is debatable. We evaluated how well T-wave-based features of electrocardiograms (ECGs) are correlated with estimated serum potassium levels using ECG data from real-world clinical practice.

METHODS

We collected ECGs from a local ECG repository (MUSE™) from 1994 to 2017 and extracted the ECG waveforms. Of about 1 million reports, 124,238 were conducted within 5 minutes before or after blood collection for serum potassium estimation. We randomly selected 500 ECGs and two evaluators measured the amplitude (T-amp) and right slope of the T-wave (T-right slope) on five lead waveforms (V3, V4, V5, V6, and II). Linear correlations of T-amp, T-right slope, and their normalized feature (T-norm) with serum potassium levels were evaluated using Pearson correlation coefficient analysis.

RESULTS

Pearson correlation coefficients for T-wave-based features with serum potassium between the two evaluators were 0.99 for T-amp and 0.97 for T-right slope. The coefficient for the association between T-amp, T-right slope, and T-norm, and serum potassium ranged from -0.22 to 0.02. In the normal ECG subgroup (normal ECG or otherwise normal ECG), there was no correlation between T-wave-based features and serum potassium level.

CONCLUSIONS

T-wave-based features were not correlated with serum potassium level, and their use in real clinical practice is currently limited.

T-wave end detection using neural networks and Support Vector Machines

BACKGROUND AND OBJECTIVE

In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines.

METHODS

Both, Multilayer Perceptron (MLP) neural networks and Fixed-Size Least-Squares Support Vector Machines (FS-LSSVM) were used as regression algorithms to determine the end of the T-wave. Different strategies for selecting the training set such as random selection, k-means, robust clustering and maximum quadratic (Rényi) entropy were evaluated. Individual parameters were tuned for each method during training and the results are given for the evaluation set. A comparison between MLP and FS-LSSVM approaches was performed. Finally, a fair comparison of the FS-LSSVM method with other state-of-the-art algorithms for detecting the end of the T-wave was included.

RESULTS

The experimental results show that FS-LSSVM approaches are more suitable as regression algorithms than MLP neural networks. Despite the small training sets used, the FS-LSSVM methods outperformed the state-of-the-art techniques.

CONCLUSION

FS-LSSVM can be successfully used as a T-wave end detection algorithm in ECG even with small training set sizes.

Performance of an open-source heart sound segmentation algorithm on eight independent databases

OBJECTIVE

Heart sound segmentation is a prerequisite step for the automatic analysis of heart sound signals, facilitating the subsequent identification and classification of pathological events. Recently, hidden Markov model-based algorithms have received increased interest due to their robustness in processing noisy recordings. In this study we aim to evaluate the performance of the recently published logistic regression based hidden semi-Markov model (HSMM) heart sound segmentation method, by using a wider variety of independently acquired data of varying quality.

APPROACH

Firstly, we constructed a systematic evaluation scheme based on a new collection of heart sound databases, which we assembled for the PhysioNet/CinC Challenge 2016. This collection includes a total of more than 120 000 s of heart sounds recorded from 1297 subjects (including both healthy subjects and cardiovascular patients) and comprises eight independent heart sound databases sourced from multiple independent research groups around the world. Then, the HSMM-based segmentation method was evaluated using the assembled eight databases. The common evaluation metrics of sensitivity, specificity, accuracy, as well as the [Formula: see text] measure were used. In addition, the effect of varying the tolerance window for determining a correct segmentation was evaluated.

MAIN RESULTS

The results confirm the high accuracy of the HSMM-based algorithm on a separate test dataset comprised of 102 306 heart sounds. An average [Formula: see text] score of 98.5% for segmenting S1 and systole intervals and 97.2% for segmenting S2 and diastole intervals were observed. The [Formula: see text] score was shown to increases with an increases in the tolerance window size, as expected.

SIGNIFICANCE

The high segmentation accuracy of the HSMM-based algorithm on a large database confirmed the algorithm's effectiveness. The described evaluation framework, combined with the largest collection of open access heart sound data, provides essential resources for evaluators who need to test their algorithms with realistic data and share reproducible results.

Application of a new robust ECG T-wave delineation algorithm for the evaluation of the autonomic innervation of the myocardium

T-wave amplitude (TWA) is a well know index of the autonomic innervation of the myocardium. However, until now it has been evaluated only manually or with simple and inefficient algorithms. In this paper, we developed a new robust single-lead electrocardiogram (ECG) T-wave delineation algorithm that is able to detect the T-wave with a wavelet based method and automatically calculate the TWA. We evaluated the algorithm on the QT database, achieving a sensitivity of 99.92% for the T wave peak and 99.38% for the T wave end. In addition, the percentage of records automatically delineated with high precision was higher than previous published works. Finally, the algorithm was applied to study the influence of anticholinergic and antiadrenergic drugs (i.e. atropine and metoprolol) on the TWA. It was observed that atropine significantly decreased the TWA when compared to baseline level, that head-up tilt caused a decrease of TWA and that metoprolol blunted this decrease. Through the development of a robust algorithm, this study opens the way for further research on the T-wave analysis for the assessment of the autonomic innervation of the ventricular myocardium.

Mouse ECG findings in aging, with conduction system affecting drugs and in cardiac pathologies: Development and validation of ECG analysis algorithm in mice

Mouse models are extremely important in studying cardiac pathologies and related electrophysiology, but very few mouse ECG analysis programs are readily available. Therefore, a mouse ECG analysis algorithm was developed and validated. Surface ECG (lead II) was acquired during transthoracic echocardiography from C57Bl/6J mice under isoflurane anesthesia. The effect of aging was studied in young (2–3 months), middle‐aged (14 months) and old (20–24 months) mice. The ECG changes associated with pharmacological interventions and common cardiac pathologies, that is, acute myocardial infarction (AMI) and progressive left ventricular hypertrophy (LVH), were studied. The ECG raw data were analyzed with an in‐house ECG analysis program, modified specially for mouse ECG. Aging led to increases in P‐wave duration, atrioventricular conduction time (PQ interval), and intraventricular conduction time (QRS complex width), while the R‐wave amplitude decreased. In addition, the prevalence of arrhythmias increased during aging. Anticholinergic atropine shortened PQ time, and beta blocker metoprolol and calcium‐channel blocker verapamil increased PQ interval and decreased heart rate. The ECG changes after AMI included early JT elevation, development of Q waves, decreased R‐wave amplitude, and later changes in JT/T segment. In progressive LVH model, QRS complex width was increased at 2 and especially 4 weeks timepoint, and also repolarization abnormalities were seen. Aging, drugs, AMI, and LVH led to similar ECG changes in mice as seen in humans, which could be reliably detected with this new algorithm. The developed method will be very useful for studies on cardiovascular diseases in mice.

Fast T Wave Detection Calibrated by Clinical Knowledge with Annotation of P and T Waves

Background

There are limited studies on the automatic detection of T waves in arrhythmic electrocardiogram (ECG) signals. This is perhaps because there is no available arrhythmia dataset with annotated T waves. There is a growing need to develop numerically-efficient algorithms that can accommodate the new trend of battery-driven ECG devices. Moreover, there is also a need to analyze long-term recorded signals in a reliable and time-efficient manner, therefore improving the diagnostic ability of mobile devices and point-of-care technologies.

Methods

Here, the T wave annotation of the well-known MIT-BIH arrhythmia database is discussed and provided. Moreover, a simple fast method for detecting T waves is introduced. A typical T wave detection method has been reduced to a basic approach consisting of two moving averages and dynamic thresholds. The dynamic thresholds were calibrated using four clinically known types of sinus node response to atrial premature depolarization (compensation, reset, interpolation, and reentry).

Results

The determination of T wave peaks is performed and the proposed algorithm is evaluated on two well-known databases, the QT and MIT-BIH Arrhythmia databases. The detector obtained a sensitivity of 97.14% and a positive predictivity of 99.29% over the first lead of the validation databases (total of 221,186 beats).

Conclusions

We present a simple yet very reliable T wave detection algorithm that can be potentially implemented on mobile battery-driven devices. In contrast to complex methods, it can be easily implemented in a digital filter design.

Implementation of a portable device for real-time ECG signal analysis

Background

Cardiac disease is one of the main causes of catastrophic mortality. Therefore, detecting the symptoms of cardiac disease as early as possible is important for increasing the patient’s survival. In this study, a compact and effective architecture for detecting atrial fibrillation (AFib) and myocardial ischemia is proposed. We developed a portable device using this architecture, which allows real-time electrocardiogram (ECG) signal acquisition and analysis for cardiac diseases.

Methods

A noisy ECG signal was preprocessed by an analog front-end consisting of analog filters and amplifiers before it was converted into digital data. The analog front-end was minimized to reduce the size of the device and power consumption by implementing some of its functions with digital filters realized in software. With the ECG data, we detected QRS complexes based on wavelet analysis and feature extraction for morphological shape and regularity using an ARM processor. A classifier for cardiac disease was constructed based on features extracted from a training dataset using support vector machines. The classifier then categorized the ECG data into normal beats, AFib, and myocardial ischemia.

Results

A portable ECG device was implemented, and successfully acquired and processed ECG signals. The performance of this device was also verified by comparing the processed ECG data with high-quality ECG data from a public cardiac database. Because of reduced computational complexity, the ARM processor was able to process up to a thousand samples per second, and this allowed real-time acquisition and diagnosis of heart disease. Experimental results for detection of heart disease showed that the device classified AFib and ischemia with a sensitivity of 95.1% and a specificity of 95.9%.

Conclusions

Current home care and telemedicine systems have a separate device and diagnostic service system, which results in additional time and cost. Our proposed portable ECG device provides captured ECG data and suspected waveform to identify sporadic and chronic events of heart diseases. This device has been built and evaluated for high quality of signals, low computational complexity, and accurate detection.

The detection of T-wave variation linked to arrhythmic risk: an industry perspective

Although the scientific literature contains ample descriptions of peculiar patterns of repolarization linked to arrhythmic risk, the objective quantification and classification of these patterns continues to be a challenge that impacts their widespread adoption in clinical practice. To advance the science, computerized algorithms spawned in the academic environment have been essential in order to find, extract and measure these patterns. However, outside the strict control of a core lab, these algorithms are exposed to poor quality signals and need to be effective in the presence of different forms of noise that can either obscure or mimic the T-wave variation (TWV) of interest. To provide a practical solution that can be verified and validated for the market, important tradeoffs need to be made that are based on an intimate understanding of the end-user as well as the key characteristics of either the signal or the noise that can be used by the signal processing engineer to best differentiate them. To illustrate this, two contemporary medical devices used for quantifying T-wave variation are presented, including the modified moving average (MMA) for the detection of T-wave Alternans (TWA) and the quantification of T-wave shape as inputs to the Morphology Combination Score (MCS) for the trending of drug-induced repolarization abnormalities.

Cardiomyocyte MEA data analysis (CardioMDA)--a novel field potential data analysis software for pluripotent stem cell derived cardiomyocytes

Cardiac safety pharmacology requires in-vitro testing of all drug candidates before clinical trials in order to ensure they are screened for cardio-toxic effects which may result in severe arrhythmias. Micro-electrode arrays (MEA) serve as a complement to current in-vitro methods for drug safety testing. However, MEA recordings produce huge volumes of data and manual analysis forms a bottleneck for high-throughput screening. To overcome this issue, we have developed an offline, semi-automatic data analysis software, ‘Cardiomyocyte MEA Data Analysis (CardioMDA)’, equipped with correlation analysis and ensemble averaging techniques to improve the accuracy, reliability and throughput rate of analysing human pluripotent stem cell derived cardiomyocyte (CM) field potentials. With the program, true field potential and arrhythmogenic complexes can be distinguished from one another. The averaged field potential complexes, analysed using our software to determine the field potential duration, were compared with the analogous values obtained from manual analysis. The reliability of the correlation analysis algorithm, evaluated using various arrhythmogenic and morphology changing signals, revealed a mean sensitivity and specificity of 99.27% and 94.49% respectively, in determining true field potential complexes. The field potential duration of the averaged waveforms corresponded well to the manually analysed data, thus demonstrating the reliability of the software. The software has also the capability to create overlay plots for signals recorded under different drug concentrations in order to visualize and compare the magnitude of response on different ion channels as a result of drug treatment. Our novel field potential analysis platform will facilitate the analysis of CM MEA signals in semi-automated way and provide a reliable means of efficient and swift analysis for cardiomyocyte drug or disease model studies.