R-wave Detection Research Articles

Robust In-Vehicle Signal Quality Assessment Using Multimodal Signal Fusion.

Continuous monitoring of physiological signals such as electrocardiogram (ECG) in driving environments has the potential to reduce the need for frequent health check-ups by providing real-time information on cardiovascular health. However, capturing ECG from sensors mounted on steering wheels creates difficulties due to motion artifacts, noise, and dropouts. To address this, we propose a novel method for reliable and accurate detection of heartbeats using sensor fusion with a bidirectional long short-term memory (BiLSTM) model. Our dataset contains reference ECG, steering wheel ECG, photoplethysmogram (PPG), and imaging PPG (iPPG) signals, which are more feasible to capture in driving scenarios. We combine these signals for R-wave detection. We conduct experiments with individual signals and signal fusion techniques to evaluate the performance of detected heartbeat positions. The BiLSTMs model achieves a performance of 62.69% in the driving scenario city. The model can be integrated into the system to detect heartbeat positions for further analysis.

Cardiovascular Autonomic Control During Hibernation in American Black Bears

The dramatic early decrease in HR upon entry into torpor in small hibernators has recently been shown to be associated with a marked increase in cardiac parasympathetic activity as indicated by increased short term beat to beat heart rate variability (RMSSD) normalized to average R-R interval (RMDSSD/RR, Zanetti et al 2023). At steady state torpor, as their body temperature drop to ~2°C, small hibernators benefit from the Q10 effect on cardiac pacemaker cells in maintaining a low HR with a reduced parasympathetic activity. Black bears in hibernation maintain body temperatures between 30-35°C while suppressing metabolism by 75% and with a marked decrease in HR from non-hibernating 55 bpm to hibernating 6-12 bpm including extreme respiratory sinus arrhythmia (Tøien et al 2011). As hibernating bears would not significantly benefit from the Q10 effect on cardiac pacemaker cells to sustain a low HR, we hypothesized that an elevated cardiac parasympathetic tone is needed to suppress HR. Continuous electrocardiogram (ECG) and blood pressure (BP) were recorded with implantable telemetry transmitters (DSI D70-CCP/EET and Konigsberg T28F-14B) in American black bears. A Pan-Tomkins algorithm modified for R-wave detection with long R-R intervals followed by visual inspection was used for the analysis. RMDSSD/RR for a 2 hour period in mid hibernation with no activity was 3 times higher than that during the resting phase in non-hibernation (0.68 vs. 0.23, n=10 and 7 respectively, p<0.001), confirming the presence of a high parasympathetic tone during hibernation. HR decreased from 68.3 ± 5.8 bpm to 12.9 ± 0.9 bpm (±SE, p<0.0001). RMSSD/RR during a 10 min period recovering from anesthesia (Telazol with supplemental Ketamin) during hibernation in mid March was only 0.019 ± 0.008 vs 0.58 ± 0.14 during a 10 min period before disturbance to induce anesthesia and HR 87.0 ± 8.7 b/min vs. 16.3 ± 1.4 b/min, demonstrating a parasympatholytic effect (n=6, p< 0.0006). The longest R-R intervals during extreme sinus arrhythmia detected in mid hibernation ranged from 11.8 to 33.0 s (Mean 19.0 ± 2.7, n=21). BP typically increased to above 200 mmHg during periods of rapid heart beats, and decayed slowly to 50-60 mmHg at the end of the longest R-R intervals. Analysis of data from a single bear from 2 hour periods at 2 week intervals throughout hibernation and recovery indicates that a high parasympathetic activity is maintained throughout hibernation and strongly correlated to the maximum observed R-R interval. We conclude that opposed to small hibernators, hibernating bears sustain low HR in hibernation with high parasympathetic activity controlling the extreme sinus arrhythmia. We suggest that BP is maintained during long R-R intervals by peripheral vasoconstriction. Supported by NIH COBRE grant number [P20GM130443]. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Completing the Cabrera Circle: deriving adaptable leads from ECG limb leads by combining constraints with a correction factor

Objective. We present a concept for processing 6-lead electrocardiography (ECG) signals which can be applied to various use cases in quantitative electrocardiography. Approach. Our work builds upon the mathematics of the well-known Cabrera sequence which is a re-sorting of the six limb leads (I, II, III, aV R, aV L, aV F) into a clockwise and physiologically-interpretable order. By deriving correction factors for harmonizing lead strengths and choosing an appropriate basis for the leads, we extend this concept towards what we call the ‘Cabrera Circle’ based on a mathematically sound foundation. Main results. To demonstrate the practical effectiveness and relevance of this concept, we analyze its suitability for deriving interpolated leads between the six limb leads and a ‘radial’ lead which both can be useful for specific use cases. We focus on the use cases of i) determination of the electrical heart axis by proposing a novel interactive tool for reconstructing the heart’s vector loop and ii) improving accuracy in time of automatic R-wave detection and T-wave delineation in 6-lead ECG. For the first use case, we derive an equation which allows projections of the 2-dimensional vector loops to arbitrary angles of the Cabrera Circle. For the second use case, we apply several state-of-the-art algorithms to a freely-available 12-lead dataset (Lobachevsky University Database). Out-of-the-box results show that the derived radial lead outperforms the other limb leads (I, II, III, aV R, aV L, aV F) by improving F1 scores of R-peak and T-peak detection by 0.61 and 2.12, respectively. Results of on- and offset computations are also improved but on a smaller scale. Significance. In summary, the Cabrera Circle offers a methodology that might be useful for quantitative electrocardiography of the 6-lead subsystem—especially in the digital age.

First in Human Subjects Testing of the UroMonitor: A Catheter-free Wireless Ambulatory Bladder Pressure Monitor.

Urodynamics is the standard method of diagnosing bladder dysfunction, but involves catheters and retrograde bladder filling. With these artificial conditions, urodynamics cannot always reproduce patient complaints. We have developed a wireless, catheter-free intravesical pressure sensor, the UroMonitor, which enables catheter-free telemetric ambulatory bladder monitoring. The purpose of this study was twofold: to evaluate accuracy of UroMonitor pressure data, and assess safety and feasibility of use in humans. Eleven adult female patients undergoing urodynamics for overactive bladder symptoms were enrolled. After baseline urodynamics, the UroMonitor was transurethrally inserted into the bladder and position was confirmed cystoscopically. A second urodynamics was then performed with the UroMonitor simultaneously transmitting bladder pressure. Following removal of urodynamics catheters, the UroMonitor transmitted bladder pressure during ambulation and voiding in private. Visual analogue pain scales (0-5) were used to assess patient discomfort. The UroMonitor did not significantly alter capacity, sensation, or flow during urodynamics. The UroMonitor was also easily inserted and removed in all subjects. The UroMonitor reproduced bladder pressure, capturing 98% (85/87) of voiding and nonvoiding urodynamic events. All subjects voided with only the UroMonitor in place with low post-void residual volume. Median ambulatory pain score with the UroMonitor was rated 0 (0-2). There were no post-procedural infections or changes to voiding behavior. The UroMonitor is the first device to enable catheter-free telemetric ambulatory bladder pressure monitoring in humans. The UroMonitor appears safe and well tolerated, does not impede lower urinary tract function, and can reliably identify bladder events compared to urodynamics.

ECG R-Wave Detection and Its Application in Left Ventricular Assist Device

Heart failure is one of the major diseases endangering human life. As a transitional support treatment before heart transplantation, the left ventricular assist device (LVAD) has significantly improved the quality of life and survival rate of patients with end-stage heart failure. In the automatic detection of electrocardiogram (ECG), the detection of QRS wave groups is the most critical aspect, which affects the correctness and accuracy of subsequent data analysis and processing. The paper used the data from the MIT-BIH database and the data collected from human ECG as the original number of samples for further processing. It processed the data through a high-order finite impulse response (FIR) low-pass filter and Shannon energy algorithm, and added the use of a high-order adaptive median filter algorithm on a field programmable gate array (FPGA) to minimize the noise of the processed real-time data. Finally, the tested ECG R-wave was used to drive the LVAD. After successful simulation by MATLAB and Modelsim software, the scheme of the real-time ECG signal controlling blood pump system was realized on the FPGA platform. The experiment showed that the method proposed could accurately extract the R-wave and control the LVAD to pump blood simultaneously.

Domain randomization using synthetic electrocardiograms for training neural networks

We present a method for training neural networks with synthetic electrocardiograms that mimic signals produced by a wearable single lead electrocardiogram monitor. We use domain randomization where the synthetic signal properties such as the waveform shape, RR-intervals and noise are varied for every training example. Models trained with synthetic data are compared to their counterparts trained with real data. Detection of r-waves in electrocardiograms recorded during different physical activities and in atrial fibrillation is used to assess the performance. By allowing the randomization of the synthetic signals to increase beyond what is typically observed in the real-world data the performance is on par or superseding the performance of networks trained with real data. Experiments show robust model performance using different seeds and on different unseen test sets that were fully separated from the training phase. The ability of the model to generalize well to hidden test sets without any specific tuning provides a simple and explainable alternative to more complex adversarial domain adaptation methods for model generalization. This method opens up the possibility of extending the use of synthetic data towards domain insensitive cardiac disease classification when disease specific a priori information is used in the electrocardiogram generation. Additionally, the method provides training with free-to-collect data with accurate labels, control of the data distribution eliminating class imbalances that are typically observed in health-related data, and the generated data is inherently private.

Insertable cardiac monitor with a long sensing vector: Impact of obesity on sensing quality and safety.

Fat layers in obese patients can impair R-wave detection and diagnostic performance of a subcutaneous insertable cardiac monitor (ICM). We compared safety and ICM sensing quality between obese patients [body mass index (BMI) ≥ 30 kg/m2] and normal-weight controls (BMI <30 kg/m2) in terms of R-wave amplitude and time in noise mode (noise burden) detected by a long-sensing-vector ICM. Patients from two multicentre, non-randomized clinical registries are included in the present analysis on January 31, 2022 (data freeze), if the follow-up period was at least 90 days after ICM insertion, including daily remote monitoring. The R-wave amplitudes and daily noise burden averaged intraindividually for days 61-90 and days 1-90, respectively, were compared between obese patients (n = 104) and unmatched (n = 268) and a nearest-neighbour propensity score (PS) matched (n = 69) normal-weight controls. The average R-wave amplitude was significantly lower in obese (median 0.46 mV) than in normal-weight unmatched (0.70 mV, P < 0.0001) or PS-matched (0.60 mV, P = 0.003) patients. The median noise burden was 1.0% in obese patients, which was not significantly higher than in unmatched (0.7%; P = 0.056) or PS-matched (0.8%; P = 0.133) controls. The rate of adverse device effects during the first 90 days did not differ significantly between groups. Although increased BMI was associated with reduced signal amplitude, also in obese patients the median R-wave amplitude was >0.3 mV, a value which is generally accepted as the minimum level for adequate R-wave detection. The noise burden and adverse event rates did not differ significantly between obese and normal-weight patients.Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04075084 and NCT04198220.

Vital Signs Sensing Gown Employing ECG-Based Intelligent Algorithms.

This study presents a long-term vital signs sensing gown consisting of two components: a miniaturized monitoring device and an intelligent computation platform. Vital signs are signs that indicate the functional state of the human body. The general physical health of a person can be assessed by monitoring vital signs, which typically include blood pressure, body temperature, heart rate, and respiration rate. The miniaturized monitoring device is composed of a compact circuit which can acquire two kinds of physiological signals including bioelectrical potentials and skin surface temperature. These two signals were pre-processed in the circuit and transmitted to the intelligent computation platform for further analysis using three algorithms, which incorporate R-wave detection, ECG-derived respiration, and core body temperature estimation. After the processing, the derived vital signs would be displayed on a portable device screen, including ECG signals, heart rate (HR), respiration rate (RR), and core body temperature. An experiment for validating the performance of the intelligent computation platform was conducted in clinical practices. Thirty-one participants were recruited in the study (ten healthy participants and twenty-one clinical patients). The results showed that the relative error of HR is lower than 1.41%, RR is lower than 5.52%, and the bias of core body temperature is lower than 0.04 °C in both healthy participant and clinical patient trials. In this study, a miniaturized monitoring device and three algorithms which derive vital signs including HR, RR, and core body temperature were integrated for developing the vital signs sensing gown. The proposed sensing gown outperformed the commonly used equipment in terms of usability and price in clinical practices. Employing algorithms for estimating vital signs is a continuous and non-invasive approach, and it could be a novel and potential device for home-caring and clinical monitoring, especially during the pandemic.

Quantitative analysis of heart rate variability parameter and mental stress index.

BackgroundCardiovascular disease not only occurs in the elderly but also tends to become a common social health problem. Considering the fast pace of modern life, quantified heart rate variability (HRV) indicators combined with the convenience of wearable devices are of great significance for intelligent telemedicine. To quantify the changes in human mental state, this article proposes an improved differential threshold algorithm for R-wave detection and recognition of electrocardiogram (ECG) signals.MethodsHRV is a specific quantitative indicator of autonomic nerve regulation of the heart. The recognition rate is increased by improving the starting position of R wave and the time-window function of the traditional differential threshold method. The experimental platform is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Analytic hierarchy process (AHP) is used to construct the mental stress assessment model, the weight judgment matrix is constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check is carried out to obtain the weight value of the corresponding HRV analysis parameters.ResultsExperimental results show that the recognition rate of R wave of real-time 5 min ECG data collected by this algorithm is >99%. The comprehensive index of HRV based on weight matrix can greatly reduce the deviation caused by the measurement error of each parameter. Compared with traditional characteristic wave recognition algorithms, the proposed algorithm simplifies the process, has high real-time performance, and is suitable for wearable analysis devices with low-configuration requirements.ConclusionOur algorithm can describe the mental stress of the body quantitatively and meet the requirements of application demonstration.

Autonomic Features in Prediction of Coronary Artery Disease and Myocardial Infarction

Atherosclerosis is one of the causes of the progression of cardiovascular diseases that lead to coronary artery diseases (CAD) and myocardial infarction (MI). The disease severity causes electrocardiogram (ECG) shape deformation leads to difficulty in R-wave detection required for heart rate variability (HRV) analysis. This limitation in the diagnosis of atherosclerotic events using ECG leads to computer-aided ECG-derived features in the prediction of CAD and MI. In this study, the lead-II ECG was recorded from CAD (N = 30), MI (N = 10), and control (N = 30) subjects. The ECG-derived heart rate variability (HRV) features were extracted. The HRV features-based support vector machine (SVM) and the artificial neural network (ANN) models have been optimized in the prediction of CAD and MI subjects. The present study revealed an efficacy of time-domain HRV parameters with the ANN model in the classification of CAD and MI subjects with an accuracy of 100% and 99.6%, respectively. While SVM presented an accuracy of 75.5% and 98.9%, respectively. The time-domain HRV parameter-based automated computer-aided diagnostic approach can be used in developing low-cost technology that may provide aid to clinicians in the screening of CAD and MI subjects.

Atrial Fibrillation Detection Using a Feedforward Neural Network

PurposeIn this study, we aimed to develop an automatic atrial fibrillation detection technique for the early prediction of atrial fibrillation, that can be used with wearable devices.MethodsAn effective deep learning-based technology is proposed to automatically detect atrial fibrillation. First, novel preprocessing algorithms, wavelet transform and sliding window filtering, are introduced to reduce the noise and outliers, respectively, from ECG signals. Then, a robust R-wave detection algorithm is developed. In addition, we proposed a feedforward neural network to detect atrial fibrillation based on ECG records.ResultsExperiments verified using a tenfold cross-validation strategy showed that the proposed method achieves competitive detection performance, and can be applied to wearable detection devices. The proposed R-wave detection algorithm achieved a detection sensitivity of 99.22%, a positive recognition rate of 98.55%, and a deviance of 2.25% on the MIT-BIH arrhythmia database. The proposed atrial fibrillation detection model achieved an accuracy of 84.00%, a detection sensitivity of 84.26%, a specificity of 93.23%, and an area under the receiver operating curve of 89.40% on a mixed dataset composed of the Challenge2017 database and the MIT-BIH arrhythmia database.ConclusionThe analysis demonstrated that the proposed atrial fibrillation detection method could automatically detect atrial fibrillation with high accuracy and efficiency, could be applied to wearable devices, and has great value in the early detection of atrial fibrillation. We believe that our work will make a valuable contribution to the area of atrial fibrillation.

A new fastening system for temporary pacing with active-fixation leads: effectiveness and safety.

Temporary cardiac pacing with active-fixation leads (TPAFL) using a reusable permanent pacemaker generator has been shown to be safer than lead systems without fixation. However, TPAFL requires the off-label use of pacemaker leads and generators. We designed a fastening system to ensure the safety and efficacy of the procedure: the KronoSafe System®. To demonstrate the safety and effectiveness of the KronoSafe System® for temporary pacing in a series of patients receiving TPAFL. A prospective cohort of 20 patients undergoing TPAFL between August 2019 and June 2020 was recruited in a Spanish region. The temporary pacemaker was implanted through jugular access and secured with the KronoSafe System®. R-wave detection, lead impedance, and capture threshold were assessed every 48 h. Complications associated with the procedure or occurring during TPAFL were recorded. There were no complications associated with temporary pacing, and the therapy was effective in all cases. TPAFL was used for a mean of 7.6 days (maximum 25 days), and 84.56% of the time in a cardiology ward. TPAFL secured using the KronoSafe system® provides safe and stable cardiac stimulation for patients requiring temporary cardiac pacing.

Effects of denoising strategies on R-wave detection in ECG analysis.

The use of ECG in cardiovascular health monitoring is well established. The signal is collected using specialised equipment, capturing the electrical discharge properties of the human heart. This produces a well-structured signal trace, which can be characterised through its peaks and troughs. The signal can then be used by clinicians to diagnose cardiac disorders. However, as with any measuring equipment, the ECG output signal can experience deterioration resulting from noise. This can happen due to environmental interference, human issues or measuring equipment failure, necessitating the development of various denoising strategies to reduce, or remove, the noise. In this paper, we study typically occurring types of noise and implement popular strategies used to rectify them. We also show, that the given strategy's denoising potential is directly related to R-wave detection, and provide best strategies to apply when faced with specific noise type.

A new algorithm to reduce T-wave over-sensing based on phase space reconstruction in S-ICD system

The subcutaneous implantable cardioverter defibrillator (S-ICD) reduces mortality in individuals at high risk of sudden arrhythmic death, by rapid defibrillation of life-threatening arrhythmia. Unfortunately, S-ICD recipients are also at risk of inappropriate shock therapies, which themselves are associated with increased rates of mortality and morbidity. The commonest cause of inappropriate shock therapies is T wave oversensing (TWOS), where T waves are incorrectly counted as R waves leading to an overestimation of heart rate. It is important to develop a method to reduce TWOS and improve the accuracy of R-peak detection in S-ICD system. This paper introduces a novel algorithm to reduce TWOS based on phase space reconstruction (PSR); a common method used to analyse the chaotic characteristics of non-linear signals. The algorithm was evaluated against 34 records from University Hospital Southampton (UHS) and all 48 records from the MIT-BIH arrhythmia database. In the UHS analysis we demonstrated a sensitivity of 99.88%, a positive predictive value of 99.99% and an accuracy of 99.88% with reductions in TWOS episodes (from 166 to 0). Whilst in the MIT-BIH analysis we demonstrated a sensitivity of 99.87%, a positive predictive value of 99.99% and an accuracy of 99.91% for R wave detection. The average processing time for 1min ECG signals from all records is 2.9s. Our algorithm is sensitive for R-wave detection and can effectively reduce the TWOS with low computational complexity, and it would therefore have the potential to reduce inappropriate shock therapies in S-ICD recipients, which would significantly reduce shock related morbidity and mortality, and undoubtedly improving patient's quality of life.

A 2.63 μW ECG Processor With Adaptive Arrhythmia Detection and Data Compression for Implantable Cardiac Monitoring Device.

An ultra-low power ECG processor ASIC (application specific integrated circuit) with R-wave detection and data compression is presented, which is designed for the long-term implantable cardiac monitoring (ICM) device for arrhythmia diagnosis. An adaptive derivative-based detection algorithm with low computation overhead for potential arrhythmia recording is proposed to detect arrhythmia with the occasional abnormal heart beats. In order to save as much as possible cardiac information with the limited memory size available in the ICM device, a hierarchical data buffer structure is proposed which saves 3 types of data, including the raw ECG data segments of 2 seconds, compressed ECG data segments of 45 seconds, and R-peak values and interval lengths of >2000 beat cycles. A modified swinging-door-trending (SDT) method is proposed for the ECG data compression. The ASIC has been implemented based on fully-customized near-threshold standard cells using the thick-gate transistors in 65-nm CMOS technology for low dynamic power consumption and leakage. The ASIC core occupies a die area of 1.77 mm2. The measured total power is 2.63 μW, which is among the ECG processors with the lowest core power consumption. It exhibits a relatively high positive precision rate (P+) of 99.3% with a sensitivity of 98.2%, in contrast to the similar designs in literature with the same core power consumption level. Also, an ECG data compression ratio (CR) of up to 17.0 has been achieved, with a good trade-off between the compression efficiency and loss.

Moving average and standard deviation thresholding (MAST): a novel algorithm for accurate R-wave detection in the murine electrocardiogram.

Advances in implantable radio-telemetry or diverse biologging devices capable of acquiring high-resolution ambulatory electrocardiogram (ECG) or heart rate recordings facilitate comparative physiological investigations by enabling detailed analysis of cardiopulmonary phenotypes and responses in vivo. Two priorities guiding the meaningful adoption of such technologies are: (1) automation, to streamline and standardize large dataset analysis, and (2) flexibility in quality-control. The latter is especially relevant when considering the tendency of some fully automated software solutions to significantly underestimate heart rate when raw signals contain high-amplitude noise. We present herein moving average and standard deviation thresholding (MAST), a novel, open-access algorithm developed to perform automated, accurate, and noise-robust single-channel R-wave detection from ECG obtained in chronically instrumented mice. MAST additionally and automatically excludes and annotates segments where R-wave detection is not possible due to artefact levels exceeding signal levels. Customizable settings (e.g. window width of moving average) allow for MAST to be scaled for use in non-murine species. Two expert reviewers compared MAST's performance (true/false positive and false negative detections) with that of a commercial ECG analysis program. Both approaches were applied blindly to the same random selection of 270 3-min ECG recordings from a dataset containing varying amounts of signal artefact. MAST exhibited roughly one quarter the error rate of the commercial software and accurately detected R-waves with greater consistency and virtually no false positives (sensitivity, Se: 98.48% ± 4.32% vs. 94.59% ± 17.52%, positive predictivity, +P: 99.99% ± 0.06% vs. 99.57% ± 3.91%, P < 0.001 and P = 0.0274 respectively, Wilcoxon signed rank; values are mean ± SD). Our novel, open-access approach for automated single-channel R-wave detection enables investigators to study murine heart rate indices with greater accuracy and less effort. It also provides a foundational code for translation to other mammals, ectothermic vertebrates, and birds.

Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm.

AimsThis work attempts to develop a standalone heart rhythm alerting system for the intensive care unit (ICU), where life-threatening arrhythmias have to be identified/alerted more precisely and more instantaneously (i.e. with lower latency) than existing bedside monitors. Methods and resultsWe use the dataset from the PhysioNet 2015 Challenge, which contains records that led to true and false arrhythmic alarms in the ICU. These records have been re-annotated as one of eight classes, namely (i) asystole, (ii) extreme bradycardia, (iii) extreme tachycardia, (iv) ventricular fibrillation (VF), (v) ventricular tachycardia (VT), (vi) normal sinus rhythm, (vii) sinus tachycardia, and (viii) noise/artefacts. Arrhythmia-specific features and features that measure the signal quality were extracted from all the records. To improve VF detection, an improved, over an existing, single-lead R-wave detection was developed that takes into account the R-waves detected in all electrocardiographic (ECG) leads. To avoid false R-wave detection due to pacing spikes, ECG signals were filtered with a low pass filter prior to R-wave detection, while the raw signals were used for feature extraction. Random forest was used as the classifier, and 10-time five-fold cross-validation, resulted in a macro-average sensitivity of 81.54%.ConclusionsIn conclusion, comparing with the bedside monitors used in the PhysioNet 2015 competition, we find that our method achieves higher positive predictive values for asystole, extreme bradycardia, VT, and VF; furthermore, our method is able to alert the presence of arrhythmia instantaneously, i.e. up to 4 s earlier.

Adaptive learning and cross training improves R-wave detection in ECG

Background and ObjectiveAutomated R-wave detection plays a vital role in electrocardiography (ECG) and ECG-based computer-aided diagnosis. Recently, a multi-level one-dimensional (1D) deep learning approach was presented that shows good performance as compared to traditional methods. MethodsIn this paper, we present several improvements of the multi-level 1D convolutional neural network (CNN)-based deep learning approach using: (i) adaptive deep learning, (ii) cross-database training, and (iii) cross-lead training. For this, we consider ECG signals from four publicly available databases: MIT-BIH, INCART, TELE, and SDDB, having 109,404, 175,660, 6,708, and 1,684,447 annotated beats, respectively. Except for TELE, all databases provide at least two-lead recordings. To evaluate the improvements, experiments are performed with adaptive k-times cross-trained databases validation scheme (k = 5). The hypothesis tested are: (i) the improvements outperform the state-of-the-art, (ii) cross-database training and adaptive deep learning contribute, and (iii) additional databases or cross-lead training further improves the results. ResultsOur proposed approach outperforms the state-of-the-art. In terms of F-measure, F = 99.75% and F = 95.25% is obtained for the MIT-BIH and TELE databases, respectively. Further, cross-database training (F = 98.02%) is found to be more effective than training on individual databases (F = 97.33%). The performance of our approach further improves when additional databases and different leads are used for training. ConclusionExisting state-of-the-art methods perform low on noisy and pathological signals. Adaptive cross-data training identifies the optimal model. Using multiple datasets and leads allows analyzing noisy, pathological and mobile-recorded long-term ECG signals without ground truths. These conclusions are based on the comprehensive evaluation of four different databases, and in total, about 4.5 million annotated beats.

Method for detecting R-waves of an ECG signal based on wavelet decomposition

Increasing the efficiency of cardiological diagnostics based on the analysis of human heart rate variability necessitates the development of accurate methods for detecting the R-waves of the electrocardiosignal (ECG signal). A technique for detecting R-waves of an ECG signal based on the wavelet multiresolution analysis (WMRA). The proposed technique for detecting R-waves includes sequential stages of digital processing of an ECG signal: WMRA; a set of nonlinear operators; adaptive algorithm for detecting signal peaks. A comparative analysis of the proposed technique with existing approaches to the detection of R-waves of the ECG signal has been carried out. To obtain quantitative characteristics of evaluating the efficiency of detecting R-waves, we used imitation modeling of an ECG signal containing noises and interferences of various intensity and nature of occurrence. The effectiveness of the considered approaches to the detection of R-waves of the ECG signal was investigated for clinical recordings of ECG signal. The absolute error of measuring the RR-interval durations for model signals with different noise levels is estimated. It is shown that the proposed method for detecting R-waves of an ECG signal based on WMRA is characterized by small errors in measuring the duration of RR-intervals, high rates of true detection and small errors of false detection and omission.

An Improved Real-Time R-Wave Detection Efficient Algorithm in Exercise ECG Signal Analysis.

R-wave detection is a prerequisite for the extraction and recognition of ECG signal feature parameters. In the analysis and diagnosis of exercise electrocardiograms, accurate and real-time detection of QRS complexes is very important for the prevention and monitoring of heart disease. This paper proposes a lightweight R-wave real-time detection method for exercise ECG signals. After real-time denoising of the exercise ECG signal, the median line is used to correct the baseline, and the first-order difference processing is performed on the differential square signal. Max-Min Threshold (MMT) is used to realize real-time R-wave detection of the exercise ECG signal. The abovementioned method was verified by using the measured data in the MIT-BIH ECG database of the Massachusetts Institute of Technology and the exercise plate experiment. The R-wave detection rates were 99.93% and 99.98%, respectively. Experimental results show that this method has high accuracy and low computational complexity and is suitable for wearable devices and motion process monitoring.