Comparison of detection of arrhythmias in patients with chronic heart failure secondary to non-ischemic versus ischemic cardiomyopathy by 1 versus 7-day holter monitoring

Machine learning based detection of T-wave alternans in real ambulatory conditions

BACKGROUND AND OBJECTIVE

T-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis. However, TWA detection in ambulatory settings remains a challenge due to the absence of standardized evaluation metrics and detection thresholds.

METHODS

In this work we use traditional TWA analysis signal processing-based methods for feature extraction, and two machine learning (ML) methods, namely, K-nearest-neighbor (KNN) and random forest (RF), for TWA detection, addressing hyper-parameter tuning and feature selection. The final goal is the detection in ambulatory recordings of short, non-sustained and sparse TWA events.

RESULTS

We train ML methods to detect a wide variety of alternant voltage from 20 to 100 μV, i.e., ranging from non-visible micro-alternans to TWA of higher amplitudes, to recognize a wide range in concordance to risk stratification. In classification, RF outperforms significantly the recall in comparison with the signal processing methods, at the expense of a small lost in precision. Despite ambulatory detection stands for an imbalanced category context, the trained ML systems always outperform signal processing methods.

CONCLUSIONS

We propose a comprehensive integration of multiple variables inspired by TWA signal processing methods to fed learning-based methods. ML models consistently outperform the best signal processing methods, yielding superior recall scores.

Characterization of noise in long-term ECG monitoring with machine learning based on clinical criteria

Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings. Graphical Abstract Clinical Noise Severity Classification based on Machine Learning techniques towards Long-Term ECG Monitoring.

Nonsustained Ventricular Tachycardia Is Independently Associated With Sustained Ventricular Arrhythmias in Nonischemic Dilated Cardiomyopathy

BACKGROUND

Spontaneous nonsustained ventricular tachycardia (NSVT) on Holter, VT inducibility during electrophysiology study, and late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) have been associated with sustained ventricular arrhythmias (SVAs) in nonischemic dilated cardiomyopathy (DCM). This study aimed to analyze whether these parameters carry independent prognostic value for spontaneous SVA in DCM.

METHODS

Between 2011 and 2018, patients with the DCM clinical spectrum and documented SVA, suspected SVA, or considered to be at intermediate or high risk for SVA were enrolled in the prospective Leiden Nonischemic Cardiomyopathy Study. Patients underwent a comprehensive evaluation including 24-hour Holter, LGE-CMR, and electrophysiology study. Holters were assessed for the presence of NSVT (≥3 beats; rate, ≥120 bpm; lasting <30 s) and NSVT characteristics (coupling interval, duration, cycle length, morphology, regularity). Patients were followed at 6 to 12 monthly intervals.

RESULTS

Of all 115 patients (age, 59±12 years; 77% men; left ventricular ejection fraction, 33±13%; history of SVA, 36%; LGE in 63%; median LGE mass, 13 g; interquartile range, 8-23 g), 62 (54%) had NSVT on Holter, and sustained monomorphic VT was inducible in 34 of 114 patients (30%). NSVT was not associated with LGE on CMR or VT inducibility during electrophysiology study nor were its features (all P>0.05). During 4.0±1.8 years of follow-up, SVA occurred in 39 patients (34%). NSVT (HR, 4.47 [95% CI, 1.87-10.72]; P=0.001) and VT inducibility (HR, 3.08 [95% CI, 1.08-8.81]; P=0.036) were independently associated with SVA during follow-up. A bivariable model including only noninvasively acquired parameters also allowed identification of a high-risk subgroup (ie, those with both NSVT and LGE on CMR). The findings remained similar when only patients without prior SVA were included.

CONCLUSIONS

In patients with DCM, NSVT on Holter and VT inducibility during electrophysiology study predict SVA during follow-up independent of LGE on CMR. NSVTs may serve as an initiator, and sustained VT inducibility indicates the presence of the substrate for SVA in DCM. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01940081.

Comparison of single- versus seven-day Holter analysis for the identification of dilated cardiomyopathy predictive criteria in apparently healthy Doberman Pinscher dogs

INTRODUCTION

The primary objective of this study was to test whether seven-day Holter recording improves the sensitivity of detecting dilated cardiomyopathy (DCM) predictive criteria (DCM_p) compared with 24-h Holter recording in asymptomatic Doberman Pinscher (DP) dogs.

ANIMALS

Twenty-eight asymptomatic DP dogs with normal echocardiographic examinations.

METHODS

Dogs with normal echocardiographic examinations underwent seven-day Holter monitoring. The presence of ≥50 ventricular premature complexes and or ≥ one couplet/one triplet/one episode of ventricular tachycardia per 24-h period was considered positive for DCM_p.

RESULTS

Five dogs were positive on the first day, and an additional six dogs tested positive from day two to seven of the Holter recording. The number of dogs positive for DCM_p detected by four days was significantly different (p = 0.031) compared with the first-day Holter recording.

CONCLUSIONS

Seven-day Holter recording detected significantly more dogs with DCM_p compared with the first-day Holter recording. Follow-up studies are warranted to evaluate the long-term accuracy of multiple-day Holter analysis in predicting the development of DCM in DP dogs.

On the Robustness of Multiscale Indices for Long-Term Monitoring in Cardiac Signals

The identification of patients with increased risk of Sudden Cardiac Death (SCD) has been widely studied during recent decades, and several quantitative measurements have been proposed from the analysis of the electrocardiogram (ECG) stored in 1-day Holter recordings. Indices based on nonlinear dynamics of Heart Rate Variability (HRV) have shown to convey predictive information in terms of factors related with the cardiac regulation by the autonomous nervous system, and among them, multiscale methods aim to provide more complete descriptions than single-scale based measures. However, there is limited knowledge on the suitability of nonlinear measurements to characterize the cardiac dynamics in current long-term monitoring scenarios of several days. Here, we scrutinized the long-term robustness properties of three nonlinear methods for HRV characterization, namely, the Multiscale Entropy (MSE), the Multiscale Time Irreversibility (MTI), and the Multifractal Spectrum (MFS). These indices were selected because all of them have been theoretically designed to take into account the multiple time scales inherent in healthy and pathological cardiac dynamics, and they have been analyzed so far when monitoring up to 24 h of ECG signals, corresponding to about 20 time scales. We analyzed them in 7-day Holter recordings from two data sets, namely, patients with Atrial Fibrillation and with Congestive Heart Failure, by reaching up to 100 time scales. In addition, a new comparison procedure is proposed to statistically compare the poblational multiscale representations in different patient or processing conditions, in terms of the non-parametric estimation of confidence intervals for the averaged median differences. Our results show that variance reduction is actually obtained in the multiscale estimators. The MSE (MTI) exhibited the lowest (largest) bias and variance at large scales, whereas all the methods exhibited a consistent description of the large-scale processes in terms of multiscale index robustness. In all the methods, the used algorithms could turn to give some inconsistency in the multiscale profile, which was checked not to be due to the presence of artifacts, but rather with unclear origin. The reduction in standard error for several-day recordings compared to one-day recordings was more evident in MSE, whereas bias was more patently present in MFS. Our results pave the way of these techniques towards their use, with improved algorithmic implementations and nonparametric statistical tests, in long-term cardiac Holter monitoring scenarios.

On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios

Despite the wide literature on R-wave detection algorithms for ECG Holter recordings, the long-term monitoring applications are bringing new requirements, and it is not clear that the existing methods can be straightforwardly used in those scenarios. Our aim in this work was twofold: First, we scrutinized the scope and limitations of existing methods for Holter monitoring when moving to long-term monitoring; Second, we proposed and benchmarked a beat detection method with adequate accuracy and usefulness in long-term scenarios. A longitudinal study was made with the most widely used waveform analysis algorithms, which allowed us to tune the free parameters of the required blocks, and a transversal study analyzed how these parameters change when moving to different databases. With all the above, the extension to long-term monitoring in a database of 7-day Holter monitoring was proposed and analyzed, by using an optimized simultaneous-multilead processing. We considered both own and public databases. In this new scenario, the noise-avoid mechanisms are more important due to the amount of noise that exists in these recordings, moreover, the computational efficiency is a key parameter in order to export the algorithm to the clinical practice. The method based on a Polling function outperformed the others in terms of accuracy and computational efficiency, yielding 99.48% sensitivity, 99.54% specificity, 99.69% positive predictive value, 99.46% accuracy, and 0.85% error for MIT-BIH arrhythmia database. We conclude that the method can be used in long-term Holter monitoring systems.

Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring

Noise and artifacts are inherent contaminating components and are particularly present in Holter electrocardiogram (ECG) monitoring. The presence of noise is even more significant in long-term monitoring (LTM) recordings, as these are collected for several days in patients following their daily activities; hence, strong artifact components can temporarily impair the clinical measurements from the LTM recordings. Traditionally, the noise presence has been dealt with as a problem of non-desirable component removal by means of several quantitative signal metrics such as the signal-to-noise ratio (SNR), but current systems do not provide any information about the true impact of noise on the ECG clinical evaluation. As a first step towards an alternative to classical approaches, this work assesses the ECG quality under the assumption that an ECG has good quality when it is clinically interpretable. Therefore, our hypotheses are that it is possible (a) to create a clinical severity score for the effect of the noise on the ECG, (b) to characterize its consistency in terms of its temporal and statistical distribution, and (c) to use it for signal quality evaluation in LTM scenarios. For this purpose, a database of external event recorder (EER) signals is assembled and labeled from a clinical point of view for its use as the gold standard of noise severity categorization. These devices are assumed to capture those signal segments more prone to be corrupted with noise during long-term periods. Then, the ECG noise is characterized through the comparison of these clinical severity criteria with conventional quantitative metrics taken from traditional noise-removal approaches, and noise maps are proposed as a novel representation tool to achieve this comparison. Our results showed that neither of the benchmarked quantitative noise measurement criteria represent an accurate enough estimation of the clinical severity of the noise. A case study of long-term ECG is reported, showing the statistical and temporal correspondences and properties with respect to EER signals used to create the gold standard for clinical noise. The proposed noise maps, together with the statistical consistency of the characterization of the noise clinical severity, paves the way towards forthcoming systems providing us with noise maps of the noise clinical severity, allowing the user to process different ECG segments with different techniques and in terms of different measured clinical parameters.

Screening strategies for atrial fibrillation: a systematic review and cost-effectiveness analysis

Background

Atrial fibrillation (AF) is a common cardiac arrhythmia that increases the risk of thromboembolic events. Anticoagulation therapy to prevent AF-related stroke has been shown to be cost-effective. A national screening programme for AF may prevent AF-related events, but would involve a substantial investment of NHS resources.

Objectives

To conduct a systematic review of the diagnostic test accuracy (DTA) of screening tests for AF, update a systematic review of comparative studies evaluating screening strategies for AF, develop an economic model to compare the cost-effectiveness of different screening strategies and review observational studies of AF screening to provide inputs to the model.

Design

Systematic review, meta-analysis and cost-effectiveness analysis.

Setting

Primary care.

Participants

Adults.

Intervention

Screening strategies, defined by screening test, age at initial and final screens, screening interval and format of screening {systematic opportunistic screening [individuals offered screening if they consult with their general practitioner (GP)] or systematic population screening (when all eligible individuals are invited to screening)}.

Main outcome measures

Sensitivity, specificity and diagnostic odds ratios; the odds ratio of detecting new AF cases compared with no screening; and the mean incremental net benefit compared with no screening.

Review methods

Two reviewers screened the search results, extracted data and assessed the risk of bias. A DTA meta-analysis was perfomed, and a decision tree and Markov model was used to evaluate the cost-effectiveness of the screening strategies.

Results

Diagnostic test accuracy depended on the screening test and how it was interpreted. In general, the screening tests identified in our review had high sensitivity (> 0.9). Systematic population and systematic opportunistic screening strategies were found to be similarly effective, with an estimated 170 individuals needed to be screened to detect one additional AF case compared with no screening. Systematic opportunistic screening was more likely to be cost-effective than systematic population screening, as long as the uptake of opportunistic screening observed in randomised controlled trials translates to practice. Modified blood pressure monitors, photoplethysmography or nurse pulse palpation were more likely to be cost-effective than other screening tests. A screening strategy with an initial screening age of 65 years and repeated screens every 5 years until age 80 years was likely to be cost-effective, provided that compliance with treatment does not decline with increasing age.

Conclusions

A national screening programme for AF is likely to represent a cost-effective use of resources. Systematic opportunistic screening is more likely to be cost-effective than systematic population screening. Nurse pulse palpation or modified blood pressure monitors would be appropriate screening tests, with confirmation by diagnostic 12-lead electrocardiography interpreted by a trained GP, with referral to a specialist in the case of an unclear diagnosis. Implementation strategies to operationalise uptake of systematic opportunistic screening in primary care should accompany any screening recommendations.

Limitations

Many inputs for the economic model relied on a single trial [the Screening for Atrial Fibrillation in the Elderly (SAFE) study] and DTA results were based on a few studies at high risk of bias/of low applicability.

Future work

Comparative studies measuring long-term outcomes of screening strategies and DTA studies for new, emerging technologies and to replicate the results for photoplethysmography and GP interpretation of 12-lead electrocardiography in a screening population.

Study registration

This study is registered as PROSPERO CRD42014013739.

Funding

The National Institute for Health Research Health Technology Assessment programme.

The Evolution of mHealth Solutions for Heart Failure Management

In the last decade, the uptake of information and communication technologies and the advent of mobile internet resulted in improved connectivity and penetrated different fields of application. In particular, the adoption of the mobile devices is expected to reform the provision and delivery of healthcare, overcoming geographical, temporal, and other organizational limitations. mHealth solutions are able to provide meaningful clinical information allowing effective and efficient management of chronic diseases, such as heart failure. A variety of data can be collected, such as lifestyle, sensor/biosensor, and health-related information. The analysis of these data empowers patients and the involved ecosystem actors, improves the healthcare delivery, and facilitates the transformation of existing health services. The aim of this study is to provide an overview of (i) the current practice in the management of heart failure, (ii) the available mHealth solutions, either in the form of the commercial applications, research projects, or related studies, and (iii) the several challenges related to the patient and healthcare professionals' acceptance, the payer and provider perspective, and the regulatory constraints.

Usefulness of extended holter ECG monitoring for serious arrhythmia detection in patients with heart failure and sleep apnea

BACKGROUND

In patients with systolic heart failure (HF), coexisting sleep apnea may promote arrhythmia. Ambulatory Holter electrocardiogram (ECG) monitoring (AECG) is a method of arrhythmia and apnea evaluation. We hypothesized that 24-hour AECG in patients with HF who have a high risk of serious arrhythmia may be less accurate than AECG extended to 48 hours and that, moreover, arrhythmia may be related to apnea.

METHODS

Eighty-four recordings of 48-hour AECG in 84 patients with ischemic HF (mean ejection fraction 34 ± 7%) were analyzed. Day 1, Day 2 were checked for ventricular tachycardia (VT) and supraventricular tachycardia (SVT). Estimated apnea-hypopnea index (est.AHI) was calculated using Holter, monitoring where est.AHI >15 indicates apnea.

RESULTS

In 48-hour AECG, VT occurred in 34 patients (40.5%) whereas SVT in 17 patients (20.2%), and patients with est.AHI > 15 had higher VT occurrence. In two-sample one-sided test for proportions, 24-hour AECG from Day 1 showed a significantly lower percentage of patients with detected VT than 48-hour AECG-it was 23.8% (20 patients), meaning a significant underestimation with P = 0.0089. We assessed VT underestimation in the subgroups with regard to est.AHI, and found that it was present in Day 1 monitoring in the subgroups with est.AHI > 15. It was absent in the subgroups with est.AHI ≤ 15 and also in Day 2 monitoring.

CONCLUSIONS

In patients with systolic HF, 24-hour AECG may have insufficient sensitivity regarding serious arrhythmia occurrence. If significant apnea was detected in the first day, extending the monitoring may be recommended.

Nonsustained ventricular tachycardia

Nonsustained ventricular tachycardia (NSVT) has been recorded in a wide range of conditions, from apparently healthy individuals to patients with significant heart disease. In the absence of heart disease, the prognostic significance of NSVT is debatable. When detected during exercise, and especially at recovery, NSVT indicates increased cardiovascular mortality within the next decades. In trained athletes, NSVT is considered benign when suppressed by exercise. In patients with non-ST-segment elevation acute coronary syndrome, NSVT occurring beyond 48 h after admission indicates an increased risk of cardiac and sudden death, especially when associated with myocardial ischemia. In acute myocardial infarction, in-hospital NSVT has an adverse prognostic significance when detected beyond the first 13 to 24 h. In patients with prior myocardial infarction treated with reperfusion and beta-blockers, NSVT is not an independent predictor of long-term mortality when other covariates such as left ventricular ejection fraction are taken into account. In patients with hypertrophic cardiomyopathy, and most probably genetic channelopathies, NSVT carries prognostic significance, whereas its independent prognostic ability in ischemic heart failure and dilated cardiomyopathy has not been established. The management of patients with NSVT is aimed at treating the underlying heart disease.