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  • 1
    Online-Ressource
    Online-Ressource
    Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology
    Radcliffe Media Media Ltd ; 2020
    In:  Arrhythmia & Electrophysiology Review Vol. 9, No. 3 ( 2020-11-05), p. 146-154
    Details
    In: Arrhythmia & Electrophysiology Review, Radcliffe Media Media Ltd, Vol. 9, No. 3 ( 2020-11-05), p. 146-154
    Materialart: Online-Ressource
    ISSN: 2050-3369 , 2050-3377
    URL: Article
    DOI: 10.15420/aer.2020.26
    Sprache: Englisch
    Verlag: Radcliffe Media Media Ltd
    Publikationsdatum: 2020
    ZDB Id: 2813970-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
    Online-Ressource
    Online-Ressource
    Deep neural networks reveal novel sex-specific electrocardiographic features relevant for mortality risk
    Oxford University Press (OUP) ; 2022
    In:  European Heart Journal - Digital Health Vol. 3, No. 2 ( 2022-07-06), p. 245-254
    Details
    In: European Heart Journal - Digital Health, Oxford University Press (OUP), Vol. 3, No. 2 ( 2022-07-06), p. 245-254
    Kurzfassung: Incorporation of sex in study design can lead to discoveries in medical research. Deep neural networks (DNNs) accurately predict sex based on the electrocardiogram (ECG) and we hypothesized that misclassification of sex is an important predictor for mortality. Therefore, we first developed and validated a DNN that classified sex based on the ECG and investigated the outcome. Second, we studied ECG drivers of DNN-classified sex and mortality. Methods and results A DNN was trained to classify sex based on 131 673 normal ECGs. The algorithm was validated on internal (68 500 ECGs) and external data sets (3303 and 4457 ECGs). The survival of sex (mis)classified groups was investigated using time-to-event analysis and sex-stratified mediation analysis of ECG features. The DNN successfully distinguished female from male ECGs {internal validation: area under the curve (AUC) 0.96 [95% confidence interval (CI): 0.96, 0.97]; external validations: AUC 0.89 (95% CI: 0.88, 0.90), 0.94 (95% CI: 0.93, 0.94)}. Sex-misclassified individuals (11%) had a 1.4 times higher mortality risk compared with correctly classified peers. The ventricular rate was the strongest mediating ECG variable (41%, 95% CI: 31%, 56%) in males, while the maximum amplitude of the ST segment was strongest in females (18%, 95% CI: 11%, 39%). Short QRS duration was associated with higher mortality risk. Conclusion Deep neural networks accurately classify sex based on ECGs. While the proportion of ECG-based sex misclassifications is low, it is an interesting biomarker. Investigation of the causal pathway between misclassification and mortality uncovered new ECG features that might be associated with mortality. Increased emphasis on sex as a biological variable in artificial intelligence is warranted.
    Materialart: Online-Ressource
    ISSN: 2634-3916
    URL: Article
    DOI: 10.1093/ehjdh/ztac010
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2022
    ZDB Id: 3076078-1
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
    Online-Ressource
    Online-Ressource
    Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders
    Oxford University Press (OUP) ; 2022
    In:  European Heart Journal - Digital Health Vol. 3, No. 3 ( 2022-10-03), p. 390-404
    Details
    In: European Heart Journal - Digital Health, Oxford University Press (OUP), Vol. 3, No. 3 ( 2022-10-03), p. 390-404
    Kurzfassung: Deep neural networks (DNNs) perform excellently in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction (EF). Despite these promising developments, implementation is hampered by the lack of trustworthy techniques to explain the algorithms to clinicians. Especially, currently employed heatmap-based methods have shown to be inaccurate. Methods and results We present a novel pipeline consisting of a variational auto-encoder (VAE) to learn the underlying factors of variation of the median beat ECG morphology (the FactorECG), which are subsequently used in common and interpretable prediction models. As the ECG factors can be made explainable by generating and visualizing ECGs on both the model and individual level, the pipeline provides improved explainability over heatmap-based methods. By training on a database with 1.1 million ECGs, the VAE can compress the ECG into 21 generative ECG factors, most of which are associated with physiologically valid underlying processes. Performance of the explainable pipeline was similar to ‘black box’ DNNs in conventional ECG interpretation [area under the receiver operating curve (AUROC) 0.94 vs. 0.96], detection of reduced EF (AUROC 0.90 vs. 0.91), and prediction of 1-year mortality (AUROC 0.76 vs. 0.75). Contrary to the ‘black box’ DNNs, our pipeline provided explainability on which morphological ECG changes were important for prediction. Results were confirmed in a population-based external validation dataset. Conclusions Future studies on DNNs for ECGs should employ pipelines that are explainable to facilitate clinical implementation by gaining confidence in artificial intelligence and making it possible to identify biased models.
    Materialart: Online-Ressource
    ISSN: 2634-3916
    URL: Article
    DOI: 10.1093/ehjdh/ztac038
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2022
    ZDB Id: 3076078-1
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 4
    Online-Ressource
    Online-Ressource
    Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning : Proof-of-Concept in Phospholamban Gene Mutation Carriers
    Ovid Technologies (Wolters Kluwer Health) ; 2021
    In:  Circulation: Arrhythmia and Electrophysiology Vol. 14, No. 2 ( 2021-02)
    Details
    In: Circulation: Arrhythmia and Electrophysiology, Ovid Technologies (Wolters Kluwer Health), Vol. 14, No. 2 ( 2021-02)
    Kurzfassung: ECG interpretation requires expertise and is mostly based on physician recognition of specific patterns, which may be challenging in rare cardiac diseases. Deep neural networks (DNNs) can discover complex features in ECGs and may facilitate the detection of novel features which possibly play a pathophysiological role in relatively unknown diseases. Using a cohort of PLN (phospholamban) p.Arg14del mutation carriers, we aimed to investigate whether a novel DNN-based approach can identify established ECG features, but moreover, we aimed to expand our knowledge on novel ECG features in these patients. Methods: A DNN was developed on 12-lead median beat ECGs of 69 patients and 1380 matched controls and independently evaluated on 17 patients and 340 controls. Differentiating features were visualized using Guided Gradient Class Activation Mapping++. Novel ECG features were tested for their diagnostic value by adding them to a logistic regression model including established ECG features. Results: The DNN showed excellent discriminatory performance with a c-statistic of 0.95 (95% CI, 0.91–0.99) and sensitivity and specificity of 0.82 and 0.93, respectively. Visualizations revealed established ECG features (low QRS voltages and T-wave inversions), specified these features (eg, R- and T-wave attenuation in V2/V3) and identified novel PLN-specific ECG features (eg, increased PR-duration). The logistic regression baseline model improved significantly when augmented with the identified features ( P 〈 0.001). Conclusions: A DNN-based feature detection approach was able to discover and visualize disease-specific ECG features in PLN mutation carriers and revealed yet unidentified features. This novel approach may help advance diagnostic capabilities in daily practice.
    Materialart: Online-Ressource
    ISSN: 1941-3149 , 1941-3084
    URL: Article
    DOI: 10.1161/CIRCEP.120.009056
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2021
    ZDB Id: 2425487-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 5
    Online-Ressource
    Online-Ressource
    Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy
    Oxford University Press (OUP) ; 2023
    In:  European Heart Journal Vol. 44, No. 8 ( 2023-02-21), p. 680-692
    Details
    In: European Heart Journal, Oxford University Press (OUP), Vol. 44, No. 8 ( 2023-02-21), p. 680-692
    Kurzfassung: This study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning–based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG criteria and QRSAREA. Methods and results A deep learning algorithm, trained on 1.1 million ECGs from 251 473 patients, was used to compress the median beat ECG, thereby summarizing most ECG features into only 21 explainable factors (FactorECG). Pre-implantation ECGs of 1306 CRT patients from three academic centres were converted into their respective FactorECG. FactorECG predicted the combined clinical endpoint of death, left ventricular assist device, or heart transplantation [c-statistic 0.69, 95% confidence interval (CI) 0.66–0.72], significantly outperforming QRSAREA and guideline ECG criteria [c-statistic 0.61 (95% CI 0.58–0.64) and 0.57 (95% CI 0.54–0.60), P & lt; 0.001 for both]. The addition of 13 clinical variables was of limited added value for the FactorECG model when compared with QRSAREA (Δ c-statistic 0.03 vs. 0.10). FactorECG identified inferolateral T-wave inversion, smaller right precordial S- and T-wave amplitude, ventricular rate, and increased PR interval and P-wave duration to be important predictors for poor outcome. An online visualization tool was created to provide interactive visualizations (https://crt.ecgx.ai). Conclusion Requiring only a standard 12-lead ECG, FactorECG held superior discriminative ability for the prediction of clinical outcome when compared with guideline criteria and QRSAREA, without requiring additional clinical variables. End-to-end automated visualization of ECG features allows for an explainable algorithm, which may facilitate rapid uptake of this personalized decision-making tool in CRT.
    Materialart: Online-Ressource
    ISSN: 0195-668X , 1522-9645
    URL: Article
    DOI: 10.1093/eurheartj/ehac617
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2023
    ZDB Id: 2001908-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 6
    Online-Ressource
    Online-Ressource
    Automatic Triage of 12‐Lead ECGs Using Deep Convolutional Neural Networks
    Ovid Technologies (Wolters Kluwer Health) ; 2020
    In:  Journal of the American Heart Association Vol. 9, No. 10 ( 2020-05-18)
    Details
    In: Journal of the American Heart Association, Ovid Technologies (Wolters Kluwer Health), Vol. 9, No. 10 ( 2020-05-18)
    Kurzfassung: The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized interpretation has not been able to reach physician‐level accuracy in detecting (acute) cardiac abnormalities. This study aims to develop and validate a deep neural network for comprehensive automated ECG triage in daily practice. METHODS AND RESULTS We developed a 37‐layer convolutional residual deep neural network on a data set of free‐text physician‐annotated 12‐lead ECG s. The deep neural network was trained on a data set with 336.835 recordings from 142.040 patients and validated on an independent validation data set (n=984), annotated by a panel of 5 cardiologists electrophysiologists. The 12‐lead ECG s were acquired in all noncardiology departments of the University Medical Center Utrecht. The algorithm learned to classify these ECG s into the following 4 triage categories: normal, abnormal not acute, subacute, and acute. Discriminative performance is presented with overall and category‐specific concordance statistics, polytomous discrimination indexes, sensitivities, specificities, and positive and negative predictive values. The patients in the validation data set had a mean age of 60.4 years and 54.3% were men. The deep neural network showed excellent overall discrimination with an overall concordance statistic of 0.93 (95% CI , 0.92–0.95) and a polytomous discriminatory index of 0.83 (95% CI , 0.79–0.87). CONCLUSIONS This study demonstrates that an end‐to‐end deep neural network can be accurately trained on unstructured free‐text physician annotations and used to consistently triage 12‐lead ECG s. When further fine‐tuned with other clinical outcomes and externally validated in clinical practice, the demonstrated deep learning–based ECG interpretation can potentially improve time to treatment and decrease healthcare burden.
    Materialart: Online-Ressource
    ISSN: 2047-9980
    URL: Article
    DOI: 10.1161/JAHA.119.015138
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2020
    ZDB Id: 2653953-6
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 7
    Online-Ressource
    Online-Ressource
    Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks
    Oxford University Press (OUP) ; 2022
    In:  EP Europace Vol. 24, No. 10 ( 2022-10-13), p. 1645-1654
    Details
    In: EP Europace, Oxford University Press (OUP), Vol. 24, No. 10 ( 2022-10-13), p. 1645-1654
    Kurzfassung: While electrocardiogram (ECG) characteristics have been associated with life-threatening ventricular arrhythmias (LTVA) in dilated cardiomyopathy (DCM), they typically rely on human-derived parameters. Deep neural networks (DNNs) can discover complex ECG patterns, but the interpretation is hampered by their ‘black-box’ characteristics. We aimed to detect DCM patients at risk of LTVA using an inherently explainable DNN. Methods and results In this two-phase study, we first developed a variational autoencoder DNN on more than 1 million 12-lead median beat ECGs, compressing the ECG into 21 different factors (F): FactorECG. Next, we used two cohorts with a combined total of 695 DCM patients and entered these factors in a Cox regression for the composite LTVA outcome, which was defined as sudden cardiac arrest, spontaneous sustained ventricular tachycardia, or implantable cardioverter-defibrillator treated ventricular arrhythmia. Most patients were male (n = 442, 64%) with a median age of 54 years [interquartile range (IQR) 44–62], and median left ventricular ejection fraction of 30% (IQR 23–39). A total of 115 patients (16.5%) reached the study outcome. Factors F8 (prolonged PR-interval and P-wave duration, P  & lt; 0.005), F15 (reduced P-wave height, P = 0.04), F25 (increased right bundle branch delay, P = 0.02), F27 (P-wave axis P  & lt; 0.005), and F32 (reduced QRS-T voltages P = 0.03) were significantly associated with LTVA. Conclusion Inherently explainable DNNs can detect patients at risk of LTVA which is mainly driven by P-wave abnormalities.
    Materialart: Online-Ressource
    ISSN: 1099-5129 , 1532-2092
    URL: Article
    DOI: 10.1093/europace/euac054
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2022
    ZDB Id: 2002579-8
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 8
    Online-Ressource
    Online-Ressource
    Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review
    JMIR Publications Inc. ; 2023
    In:  JMIR Cardio Vol. 7 ( 2023-7-7), p. e44003-
    Details
    In: JMIR Cardio, JMIR Publications Inc., Vol. 7 ( 2023-7-7), p. e44003-
    Kurzfassung: Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments. Objective This scoping review aimed to provide a comprehensive overview of the current landscape of mobile ECG devices, including the technology used, intended clinical use, and available clinical evidence. Methods We conducted a scoping review to identify studies concerning mobile ECG devices in the electronic database PubMed. Secondarily, an internet search was performed to identify other ECG devices available in the market. We summarized the devices’ technical information and usability characteristics based on manufacturer data such as datasheets and user manuals. For each device, we searched for clinical evidence on the capabilities to record heart disorders by performing individual searches in PubMed and ClinicalTrials.gov, as well as the Food and Drug Administration (FDA) 510(k) Premarket Notification and De Novo databases. Results From the PubMed database and internet search, we identified 58 ECG devices with available manufacturer information. Technical characteristics such as shape, number of electrodes, and signal processing influence the capabilities of the devices to record cardiac disorders. Of the 58 devices, only 26 (45%) had clinical evidence available regarding their ability to detect heart disorders such as rhythm disorders, more specifically atrial fibrillation. Conclusions ECG devices available in the market are mainly intended to be used for the detection of arrhythmias. No devices are intended to be used for the detection of other cardiac disorders. Technical and design characteristics influence the intended use of the devices and use environments. For mobile ECG devices to be intended to detect other cardiac disorders, challenges regarding signal processing and sensor characteristics should be solved to increase their detection capabilities. Devices recently released include the use of other sensors on ECG devices to increase their detection capabilities.
    Materialart: Online-Ressource
    ISSN: 2561-1011
    URL: Article
    DOI: 10.2196/44003
    Sprache: Englisch
    Verlag: JMIR Publications Inc.
    Publikationsdatum: 2023
    ZDB Id: 3003135-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 9
    Online-Ressource
    Online-Ressource
    Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms
    Oxford University Press (OUP) ; 2021
    In:  European Heart Journal - Digital Health Vol. 2, No. 3 ( 2021-09-30), p. 401-415
    Details
    In: European Heart Journal - Digital Health, Oxford University Press (OUP), Vol. 2, No. 3 ( 2021-09-30), p. 401-415
    Kurzfassung: Automated interpretation of electrocardiograms (ECGs) using deep neural networks (DNNs) has gained much attention recently. While the initial results have been encouraging, limited attention has been paid to whether such results can be trusted, which is paramount for their clinical implementation. This study aims to systematically investigate uncertainty estimation techniques for automated classification of ECGs using DNNs and to gain insight into its utility through a clinical simulation. Methods and results On a total of 526 656 ECGs from three different datasets, six different methods for estimation of aleatoric and epistemic uncertainty were systematically investigated. The methods were evaluated based on ranking, calibration, and robustness against out-of-distribution data. Furthermore, a clinical simulation was performed where increasing uncertainty thresholds were applied to achieve a clinically acceptable performance. Finally, the correspondence between the uncertainty of ECGs and the lack of interpretational agreement between cardiologists was estimated. Results demonstrated the largest benefit when modelling both epistemic and aleatoric uncertainty. Notably, the combination of variational inference with Bayesian decomposition and ensemble with auxiliary output outperformed the other methods. The clinical simulation showed that the accuracy of the algorithm increased as uncertain predictions were referred to the physician. Moreover, high uncertainty in DNN-based ECG classification strongly corresponded with a lower diagnostic agreement in cardiologist’s interpretation (P  & lt; 0.001). Conclusion Uncertainty estimation is warranted in automated DNN-based ECG classification and its accurate estimation enables intermediate quality control in the clinical implementation of deep learning. This is an important step towards the clinical applicability of automated ECG diagnosis using DNNs.
    Materialart: Online-Ressource
    ISSN: 2634-3916
    URL: Article
    DOI: 10.1093/ehjdh/ztab045
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2021
    ZDB Id: 3076078-1
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 10
    Online-Ressource
    Online-Ressource
    ECG-only explainable deep learning algorithm predicts the risk for malignant ventricular arrhythmia in phospholamban cardiomyopathy
    Elsevier BV ; 2024
    In:  Heart Rhythm ( 2024-2)
    Details
    In: Heart Rhythm, Elsevier BV, ( 2024-2)
    Materialart: Online-Ressource
    ISSN: 1547-5271
    URL: Article
    DOI: 10.1016/j.hrthm.2024.02.038
    Sprache: Englisch
    Verlag: Elsevier BV
    Publikationsdatum: 2024
    Standort Signatur Einschränkungen Verfügbarkeit
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