Long-term Electrocardiogram Recordings Research Articles

A generalizable and robust deep learning method for atrial fibrillation detection from long-term electrocardiogram

The reliable detection of atrial fibrillation (AF) is important for diagnosing the disease, tracking its progression, and developing individualized care strategies. However, models based on limited data are prone to data dependency due to differences in data feature distribution, which generally degrades their performance on unseen external datasets. In this work, we propose a multi-input fusion AF detection network (MIF-AFNet), which cascades residual convolutional neural networks and bidirectional long short-term memory networks to capture the deep features of electrocardiogram (ECG) and RR intervals (RRIs), respectively. Additionally, the ECG signals use a data augmentation method to alleviate the morphological imbalance. MIF-AFNet learns a robust feature representation for accurate AF detection by fusing the available information from RRIs and ECG. The proposed method was developed and evaluated using 5 long-term ECG datasets (CPSC2021, AFDB, LTAF, MITDB, and NSRDB) from PhysioNet. The subject-wise five-fold cross-validation was performed on CPSC2021, and the proposed method achieved an AF detection accuracy of 98.63%. The generalization performance is further evaluated on four external independent datasets (AFDB, LTAF, MITDB, and NSRDB), achieving accuracies of 98.63%, 97.04%, 98.07%, and 100%, respectively. The results show that the proposed method can accurately detect AF from long-term ECG recordings. In addition, the low complexity of the model makes it less demanding on computing resources. Therefore, it has the potential to improve the automatic diagnosis and management of AF in wearable device-based long-term home monitoring.

Forecasting imminent atrial fibrillation in long-term electrocardiogram recordings

BackgroundDespite the morbidity associated with acute atrial fibrillation (AF), no models currently exist to forecast its imminent onset. We sought to evaluate the ability of deep learning to forecast the imminent onset of AF with sufficient lead time, which has important implications for inpatient care. MethodsWe utilized the Physiobank Long-Term AF Database, which contains 24-h, labeled ECG recordings from patients with a history of AF. AF episodes were defined as ≥5 min of sustained AF. Three deep learning models incorporating convolutional and transformer layers were created for forecasting, with two models focusing on the predictive nature of sinus rhythm segments and AF epochs separately preceding an AF episode, and one model utilizing all preceding waveform as input. Cross-validated performance was evaluated using area under time-dependent receiver operating characteristic curves (AUC(t)) at 7.5-, 15-, 30-, and 60-min lead times, precision-recall curves, and imminent AF risk trajectories. ResultsThere were 367 AF episodes from 84 ECG recordings. All models showed average risk trajectory divergence of those with an AF episode from those without ∼15 min before the episode. Highest AUC was associated with the sinus rhythm model [AUC = 0.74; 7.5-min lead time], though the model using all preceding waveform data had similar performance and higher AUCs at longer lead times. ConclusionsIn this proof-of-concept study, we demonstrated the potential utility of neural networks to forecast the onset of AF in long-term ECG recordings with a clinically relevant lead time. External validation in larger cohorts is required before deploying these models clinically.

Atrial Fibrillation Burden Estimation Using Multi-Task Deep Convolutional Neural Network

Atrial fibrillation (AF) burden is defined as the percentage of time the patient is in AF rhythm during a certain monitoring period. The accurate AF burden estimation from the long-term electrocardiogram (ECG) recordings provides improved prognostic value compared to the traditional binary AF diagnosis (present or absent) using the snapshot ECG. However, the presence of frequent ectopic beats and different noise levels pose a challenge for precise AF burden estimation. For the first time, we hypothesized that a multi-task deep convolutional neural network (MT-DCNN) could accurately estimate the AF burden from the long-term ambulatory ECG recordings. The model consists of AF detection as a primary task and reconstruction of ECG sequence as an auxiliary task using DCNNs. The auxiliary task regularizes the model to learn robust feature representations for efficient AF detection, thereby aiding accurate AF burden estimation. The MT-DCNN is compared with the state-of-the-art rhythm-based, rhythm- and morphology-based approaches. The models are developed and evaluated on a large database of n=84 patients, totaling t=1,900 h of continuous ECG recordings from the LTAF database. The generalization performance is evaluated on three independent datasets (AFDB, NSRDB and LTNSRDB) of n=48 subjects, totaling t=761 h of continuous ECG recordings. On the LTAF test set, the proposed model exhibits a lesser mean absolute AF burden estimation error of 2.8 % over the rhythm-based and the rhythm- and morphology-based approaches. In addition, the MT-DCNN provides better generalization results on independent test datasets and at different noise levels. The results demonstrate that the MT-DCNN can accurately estimate the AF burden from long-term ECG recordings; thus, it has the potential to be used in remote patient monitoring applications for improved diagnosis, phenotyping, and management of AF.

Accurate QT correction method from transfer entropy

The QT interval in the electrocardiogram (ECG) is a fundamental risk measure for arrhythmic adverse cardiac events. However, the QT interval depends on the heart rate and must be corrected accordingly. The present QT correction (QTc) methods are either simple models leading to under- or overcorrection, or impractical in requiring long-term empirical data. In general, there is no consensus on the best QTc method. We introduce a model-free QTc method-AccuQT-that computes QTc by minimizing the information transfer from R-R to QT intervals. The objective is to establish and validate a QTc method that provides superior stability and reliability without models or empirical data. We tested AccuQT against the most commonly used QT correction methods by using long-term ECG recordings of more than 200 healthy subjects from PhysioNet and THEW databases. AccuQT overperforms the previously reported correction methods: the proportion of false-positives is reduced from 16% (Bazett) to 3% (AccuQT) for the PhysioNet data. In particular, the QTc variance is significantly reduced and thus the RR-QT stability is increased. AccuQT has significant potential to become the QTc method of choice in clinical studies and drug development. The method can be implemented in any device recording R-R and QT intervals.

Diagnostic quality assessment for low-dimensional ECG representations

There have been several attempts to quantify the diagnostic distortion caused by algorithms that perform low-dimensional electrocardiogram (ECG) representation. However, there is no universally accepted quantitative measure that allows the diagnostic distortion arising from denoising, compression, and ECG beat representation algorithms to be determined. Hence, the main objective of this work was to develop a framework to enable biomedical engineers to efficiently and reliably assess diagnostic distortion resulting from ECG processing algorithms. We propose a semiautomatic framework for quantifying the diagnostic resemblance between original and denoised/reconstructed ECGs. Evaluation of the ECG must be done manually, but is kept simple and does not require medical training. In a case study, we quantified the agreement between raw and reconstructed (denoised) ECG recordings by means of kappa-based statistical tests. The proposed methodology takes into account that the observers may agree by chance alone. Consequently, for the case study, our statistical analysis reports the “true”, beyond-chance agreement in contrast to other, less robust measures, such as simple percent agreement calculations. Our framework allows efficient assessment of clinically important diagnostic distortion, a potential side effect of ECG (pre-)processing algorithms. Accurate quantification of a possible diagnostic loss is critical to any subsequent ECG signal analysis, for instance, the detection of ischemic ST episodes in long-term ECG recordings.

A PDMS-based microneedle array electrode for long-term ECG recording.

To acquire high-quality electrocardiogram (ECG) signals, traditional Ag/AgCl wet electrodes used together with conductive gel can effectively reduce electrode-skin interface impedance (EII) in a short term. However, their weaknesses of poor flexibility and instability can no longer meet the long-term monitoring requirements of intelligent wearable devices. Owing to the flexible dry electrode without conductive gel, it is a good choice to solve the critical problem on drying-out of conductive gel. Therefore, we develop a flexible microneedle array electrode (FMAE) based on polydimethylsiloxane (PDMS) substrate, which obtains reliable bioelectrical signals by way of penetrating into the stratum corneum (SC) of the skin. The fabrication process, including silicon mold, twice PDMS shape-transferring and encapsulation, has advantages of low cost, repeatable production and good biocompatibility. Afterwards, by comparing the performance with different electrodes, impedance test results indicate that the impedance of FMAE are smaller and more stable, and ECG tests in long term and at resting/jogging states also verify that FMAE can obtain durable, stable and reliable signals. In conclusion, FMAE is promising in long-term ECG monitoring.

Robust PVC Identification by Fusing Expert System and Deep Learning.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

Heart Rate Topic Models

A key challenge in reducing the burden of cardiovascular disease is matching patients to treatments that are most appropriate for them. Different cardiac assessment tools have been developed to address this goal. Recent research has focused on heart rate motifs, i.e., short-term heart rate sequences that are over- or under-represented in long-term electrocardiogram (ECG) recordings of patients experiencing cardiovascular outcomes, which provide novel and valuable information for risk stratification. However, this approach can leverage only a small number of motifs for prediction and results in difficult to interpret models. We address these limitations by identifying latent structure in the large numbers of motifs found in long-term ECG recordings. In particular, we explore the application of topic models to heart rate time series to identify functional sets of heart rate sequences and to concisely describe patients using task-independent features for various cardiovascular outcomes. We evaluate the approach on a large collection of real-world ECG data, and investigate the performance of topic mixture features for the prediction of cardiovascular mortality. The topics provided an interpretable representation of the recordings and maintained valuable information for clinical assessment when compared with motif frequencies, even after accounting for commonly used clinical risk scores.

Unsupervised semantic-aware adaptive feature fusion network for arrhythmia detection

An electrocardiogram (ECG) consists of complex P-QRS-T waves. Detecting long-term ECG recordings is time-consuming and error-prone for cardiologists. Deep neural networks (DNNs) can learn deep representations and empower automatic arrhythmia detection. However, when applying DNNs in practice, they usually suffer from domain shift that exits between the training data and testing data. Such shift can be caused by the high variability contained in ECG signals between patients and internal-variability of heartbeats for same patients, leading to degrading performance and impeding generalization of DNNs. To tackle this problem, we propose an unsupervised semantic-aware adaptive feature fusion network (USAFFN) to reduce such shift by alleviating the semantic distribution discrepancy between the feature spaces of two domains. Furthermore, an ECG contains rich information from different angles (beat, rhythm, and frequency levels), which is essential for arrhythmia detection. Therefore, a multi-perspective adaptive feature fusion (MPAFF) module is introduced to extract informative ECG representations. The experimental results show that the detection performance of our approach is highly competitive with the upper bound of alternative methods on the ARDB, and the generalization is confirmed on the INCART and LTDB.

Double Trouble from POLG1 and CLCN1 Variants with Intrafamilial Phenotypic Heterogeneity.

I read with interest the article by Pauly and colleagues1 about a family in which the father carried the variants c.844T>G in POLG1 and c.937G>A in CLCN1. The POLG1 variant manifested with ptosis, ophthalmoparesis, parkinsonism, and depression, and the CLCN1 variant with myotonia (myoclonic twitches and muscle cramping triggered by rapid movements or sneezing) and muscle hypertrophy.1 All of his 3 children carried his POLG1 variant and 1 the CLCN1 variant, but only 2 manifested clinically.1 The study has a number of shortcomings. Missing are the results of cerebral magnetic resonance imaging and the electroencephalogram. “Twitching” and “cramping” may not only be attributable to the CLCN1 variant but also to the POLG1 variant.2 POLG1 variants may manifest with myoclonic epilepsy, which needs to be excluded as it requires a completely different therapy. This is of relevance as the deceased daughter had Alpers-Huttenlocher disease, thus intractable epilepsy. Because “twitching” was triggerable,1 we should know if the clinical phenomenon correlated with the occurrence of myotonic discharges on needle electromyography. Because the father and his son both had myopathy of the extraocular eye muscles and POLG1 variants can also manifest with myopathy of the skeletal muscles,3 we should know if the father or his son had undergone muscle biopsy to see if features for a mitochondrial disorder (MID) were present or not. Missing are results of mitochondrial DNA (mtDNA) investigations. Because POLG1 variants may secondarily cause mtDNA depletion or multiple mtDNA deletions,4 we should know if the amount of mtDNA was reduced or if there were multiple mtDNA deletions. There is no explanation provided why the second girl did not manifest clinically from the POLG1 variant. Because MIDs frequently manifest subclinically, we should know if the girl was investigated for subclinical involvement of the brain, eyes, ears, endocrine organs, heart, intestines, or kidneys. Early detection of clinical manifestations is crucial as early treatment may improve the outcome in these patients. Assuming that the girl did not even manifest subclinically, the missing penetrance of the variant should be explained. It is conceivable that secondary damage of mtDNA was not present in this girl but only in her sister and brother. It is also not discussed why the mother of the 3 children who carried the POLG1 variant c.1399GA>A did not manifest clinically. Cardiac complications may not only derive from the CLCN1 variant but also from the POLG1 variant.5 Because cardiac involvement in MIDs strongly determines the outcome of these patients, it is crucial that they undergo regular follow-up investigations and long-term electrocardiogram recordings. If ventricular arrhythmias are recorded, implantation of an implantable cardioverter-defibrillator may strongly improve the survival and outcome of these patients. Cardiac surveillance is crucial in the light of the side effects of flecainide. It should be mentioned that, among the syndromic MIDs, POLG1 variants can also cause Leigh syndrome; mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes; myoclonic epilepsy with ragged-red fibers; myoclonic epilepsy myopathy sensory ataxia; sensory ataxic neuropathy, dysarthria, ophthalmoparesis; and chronic progressive external ophthalmoplegia syndrome. Overall, this study has a number of shortcomings that should be addressed and considered for modifying the conclusions. Genetic findings need to be related to the clinical presentation, and factors determining phenotypic variability need to be discussed. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. Informed patient consent and the approval of an institutional review board was not necessary for this work. There are no conflicts of interest and no funding was received for this work. There are no disclosures to report.

A Flexible 12-Lead/Holter Device with Compression Capabilities for Low-Bandwidth Mobile-ECG Telemedicine Applications.

In recent years, a number of proposals for electrocardiogram (ECG) monitoring based on mobile systems have been delivered. We propose here an STM32F-microcontroller-based ECG mobile system providing both long-term (several weeks) Holter monitoring and 12-lead ECG recording, according to the clinical standard requirements for these kinds of recordings, which in addition can yield further digital compression at stages close to the acquisition. The system can be especially useful in rural areas of developing countries, where the lack of specialized medical personnel justifies the introduction of telecardiology services, and the limitations of coverage and bandwidth of cellular networks require the use of efficient signal compression systems. The prototype was implemented using a small architecture, with a 16-bits-per-sample resolution. We also used a low-noise instrumentation amplifier TI ADS1198, which has a multiplexer and an analog-to-digital converter (16 bits and 8 channels) connected to the STM32F processor, the architecture of which incorporates a digital signal processing unit and a floating-point unit. On the one hand, the system portability allows the user to take the prototype in her/his pocket and to perform an ECG examination, either in 12-lead controlled conditions or in Holter monitoring, according to the required clinical scenario. An app in the smartphone is responsible for giving the users a friendly interface to set up the system. On the other hand, electronic health recording of the patients are registered in a web application, which in turn allows them to connect to the Internet from their cellphones, and the ECG signals are then sent though a web server for subsequent and ubiquitous analysis by doctors at any convenient terminal device. In order to determine the quality of the received signals, system testing was performed in the three following scenarios: (1) The prototype was connected to the patient and the signals were subsequently stored; (2) the prototype was connected to the patient and the data were subsequently transferred to the cellphone; (3) the prototype was connected to the patient, and the data were transferred to the cellphone and to the web via the Internet. An additional benchmarking test with expert clinicians showed the clinical quality provided by the system. The proposed ECG system is the first step and paves the way toward mobile cardiac monitors in terms of compatibility with the electrocardiographic practice, including the long-term monitoring, the usability with 12 leads, and the possibility of incorporating signal compression at the early stages of the ECG acquisition.

A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation

Objective: The electrocardiogram (ECG) is currently the most widely used recording to diagnose cardiac disorders, including the most common supraventricular arrhythmia, such as atrial fibrillation (AF). However, different types of electrical disturbances, in which power-line interference (PLI) is a major problem, can mask and distort the original ECG morphology. This is a significant issue in the context of AF, because accurate characterization of fibrillatory waves (f-waves) is unavoidably required to improve current knowledge about its mechanisms. This work introduces a new algorithm able to reduce high levels of PLI and preserve, simultaneously, the original ECG morphology. Approach: The method is based on stationary wavelet transform shrinking and makes use of a new thresholding function designed to work successfully in a wide variety of scenarios. In fact, it has been validated in a general context with 48 ECG recordings obtained from pathological and non-pathological conditions, as well as in the particular context of AF, where 380 synthesized and 20 long-term real ECG recordings were analyzed. Main results: In both situations, the algorithm has reported a notably better performance than common methods designed for the same purpose. Moreover, its effectiveness has proven to be optimal for dealing with ECG recordings affected by AF, since f-waves remained almost intact after removing very high levels of noise. Significance: The proposed algorithm may facilitate a reliable characterization of the f-waves, preventing them from not being masked by the PLI nor distorted by an unsuitable filtering applied to ECG recordings with AF.

Association of frequent premature ventricular complex >10% and stroke-like symptoms without a prior diagnosis of stroke or transient ischemic attack

IntroductionPremature ventricular complex (PVCs) detected from long-term ECG recordings have been associated with an increased risk of ischemic stroke. However, there was limited data about the association between high PVCs burdens (>10%) and stroke-like symptoms without a prior diagnosis of stroke or transient ischemic attack in the long-term follow up. MethodsThe Kosin University 24-hours holter monitoring, echocardiography, electrocardiogram (ECG) database were reviewed from 2013 to 2015 to identify patients with frequent PVCs (>10%). We compared the long-term clinical outcomes between the patients with frequent PVCs (>10%) and control group without PVC. ResultsAmong 572 patients who underwent 24-hours holter monitoring, finally, 373 consecutive patients (mean age; 59.5 ± 15.8 years, 45.2% male) were enrolled. Among them, 203(54.4%) patients had high PVCs burdens (>10%). There was no difference of the baseline characteristics. In the long term follow-up, PVCs burden was not associated with PVCs -related symptoms (P = 0.210). In univariate analysis, female, non-sustained ventricular tachycardia (VT), sinus QRS duration, PVC coupling interval (CI), post- PVC CI, and late precordial R-wave transition of PVCs were associated with PVCs-related symptoms. In multivariate analysis, non-sustained VT (P = 0.022) and late precordial R-wave transition of PVCs (P = 0.044) were independent risk factors for PVCs-related stroke-like symptoms with frequent idiopathic PVCs > 10%. ConclusionHigh PVCs burdens (>10%) were associated with and stroke-like symptoms without a prior diagnosis of stroke or transient ischemic attack in the long-term follow up, suggesting more intensive medical therapy with close clinical follow-up will be required.

A Real-Time QRS Detection System With PR/RT Interval and ST Segment Measurements for Wearable ECG Sensors Using Parallel Delta Modulators.

This paper presents a real-time electrocardiogram (ECG) monitoring system for wearable devices. The system is based on the proposed parallel delta modulator architecture with local maximum point and local minimum point algorithms to detect QRS and PT waves. Therefore, using the proposed system and algorithm, real-time PR and RT intervals, and ST segment measurements can be achieved in long-term wearable ECG recording. The algorithm is tested with the MIT-BIH Arrhythmia Database for QRS complex detection and with the QT Database for the P and T wave detections. The simulation result shows that the algorithm achieves above 99%, 91%, and 98% accuracy in the QRS complex, P wave, and T wave detections, respectively. Experimental results are presented from the system prototype, in which the parallel delta modulator circuits are fabricated in IBM 0.13$\mu \text{m}$ standard CMOS technology and the algorithms are implemented in a Xilinx Spartan-6 field programmable gate array (FPGA). The parallel delta modulators consume 720nW at 1kHz sampling rate with $\pm$0.6V power supply. The proposed system has the potential to be applied in future long-term wearable ECG recording devices.

A new approach for analysis of heart rate variability and QT variability in long-term ECG recording

Background and purposeWith the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24–48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings.MethodsIn this report, we propose a new nonlinear time–frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from extra-long-term ECG recordings. This approach is a generalization of Short Time Fourier Transform and Continuous Wavelet Transform approaches.ResultsAs proof of concept, we used 14-day ECG recordings to show that the ConceFT provides a sharpened and stabilized spectrogram by taking the phase information of the time series and the multitaper technique into account.ConclusionThe ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings.

Clustering ECG heartbeat using improved semi‐supervised affinity propagation

The electrocardiogram (ECG) has become an important tool for the diagnosis of cardiovascular diseases. As long-term ECG recordings become more common, driven partly by the development of intelligent hardware, the requirement for automatic ECG analysis continues to grow. Research has attempted to use the expert knowledge to optimise ECG-related algorithms, however, visual analysis of long-term ECG is tedious and operator dependent. In previous studies, an ECG beat clustering approach based on self-organising maps has been applied to reduce the amount of time the operator must to spend. This unsupervised approach partitions the ECG beats into 25 groups, however, the cluster number (25) does not accurately reflect the actual number of categories. In this study, an integrated method is presented for the clustering of ECG beats based on an improved semi-supervised affinity propagation algorithm with independent component analysis. Using the MIT-BIH arrhythmia database, the authors find that the resulting clusters to exhibit a high degree of precision. The integrated method outperforms other conventional methods in the MIT-BIH database, and has great theoretical and practical significance in the field of cardiac disease.

Design of a smart ECG garment based on conductive textile electrode and flexible printed circuit board.

A smart electrocardiogram (ECG) garment system was designed for continuous, non-invasive and comfortable ECG monitoring, which mainly consists of four components: Conductive textile electrode, garment, flexible printed circuit board (FPCB)-based ECG processing module and android application program. Conductive textile electrode and FPCB-based ECG processing module (6.8 g, 55 mm × 53 mm × 5 mm) are identified as two key techniques to improve the system's comfort and flexibility. Preliminary experimental results verified that the textile electrodes with circle shape, 40 mm size in diameter, and 5mm thickness sponge are best suited for the long-term ECG monitoring application. The tests on the whole system confirmed that the designed smart garment can obtain long-term ECG recordings with high signal quality.

Adaptive Spectro-Temporal Filtering for Electrocardiogram Signal Enhancement.

Advances in low-cost portable electrocardiogram (ECG) devices have opened doors for numerous new applications, including fitness tracking, remote health, and peak athletic performance monitoring, to name a few. Many such devices, however, have been shown to be highly contaminated by movement and/or muscle contraction artifacts, which, in turn, can lead to erroneous heart rate and heart rate variability (HRV) analyses. Here, we propose a new denoising method based on adaptive spectro-temporal filtering for ECG enhancement. The algorithm relies on the so-called modulation spectral signal representation, which is shown to accurately separate ECG and noise components. The proposed method was tested on synthetic ECG signals corrupted with varying levels of recorded noise and on long-term bedside noisy ECG recordings. Gains over a state-of-the-art wavelet-based denoising algorithm were achieved, particularly for very noisy scenarios. Overall, the proposed algorithm achieved a 61.8% gain in signal-to-noise ratio improvement, a three times reduction in average heart rate measurement error, and a 15% reduction in HRV measurement error relative to the benchmark, thus suggesting that it is an ideal candidate for ECG-based fitness/athletic monitoring applications.

16-83: Association of frequent ventricular premature complex > 20% and stroke symptoms without a prior diagnosis of stroke or transient ischemic attack

Introduction: Ventricular premature complexes (VPCs) detected from long-term ECG recordings have been associated with an increased risk of ischemic stroke. However, there was limited data about the association between high VPCs burdens (>20%) and stroke symptoms without a prior diagnosis of stroke or transient ischemic attack in the long-term follow up. Methods: The Kosin University 24-hours holter monitoring, echocardiography, electrocardiogram (ECG) database were reviewed from 2013 to 2015 to identify patients with frequent VPC (>20%). We compared the long-term clinical outcomes between the patients with frequent VPCs (>20%) and control group without VPC. Results: Among 304 patients who underwent 24-hours holter monitoring, 122 (40.1%) patients had high VPCs burdens (>20%). There was no difference of the incidence of total any events including bleeding events, thromboembolic events, arrhythmic events, re-hospitalizations and mortality in both groups (P = 0.812). However, stroke symptoms including painless weakness, numbness, loss of vision, or inability to speak or understand occurred in 62 patients, with a significantly higher incidence in patients with high VPCs burdens (>20%) than the control group (P<0.001), In univariate analysis, age, diabetes mellitus and high VPCs burdens (>20%) were significantly associated with stroke symptoms. In multivariate analysis, age (P = 0.02) and high VPCs burdens (>20%, P < 0.001) were independent risk factors for stroke symptoms at the long-term follow-up.

ECG feature extraction based on the bandwidth properties of variational mode decomposition

It is a difficult process to detect abnormal heart beats, known as arrhythmia, in long-term ECG recording. Thus, computer-aided diagnosis systems have become a supportive tool for helping physicians improve the diagnostic accuracy of heartbeat detection. This paper explores the bandwidth properties of the modes obtained using variational mode decomposition (VMD) to classify arrhythmia electrocardiogram (ECG) beats. VMD is an enhanced version of the empirical mode decomposition (EMD) algorithm for analyzing non-linear and non-stationary signals. It decomposes the signal into a set of band-limited oscillations called modes. ECG signals from the MIT-BIH arrhythmia database are decomposed using VMD, and the amplitude modulation bandwidth BAM, the frequency modulation bandwidth BFM and the total bandwidth B of the modes are used as feature vectors to detect heartbeats such as normal (N), premature ventricular contraction (V), left bundle branch block (L), right bundle branch block (R), paced beat (P) and atrial premature beat (A). Bandwidth estimations based on the instantaneous frequency (IF) and amplitude (IA) spectra of the modes indicate that the proposed VMD-based features have sufficient class discrimination capability regarding ECG beats. Moreover, the extracted features using the bandwidths (BAM, BFM and B) of four modes are used to evaluate the diagnostic accuracy rates of several classifiers such as the k-nearest neighbor classifier (k-NN), the decision tree (DT), the artificial neural network (ANN), the bagged decision tree (BDT), the AdaBoost decision tree (ABDT) and random sub-spaced k-NN (RSNN) for N, R, L, V, P, and A beats. The performance of the proposed VMD-based feature extraction with a BDT classifier has accuracy rates of 99.06%, 99.00%, 99.40%, 99.51%, 98.72%, 98.71%, and 99.02% for overall, N-, R-, L-, V-, P-, and A-type ECG beats, respectively.