Arrhythmia classification using RR intervals: Improvement with sinusoidal regression feature - Leutheuser, Heike; Tobola, Andreas; Anneken, Lars; Gradl, Stefan; Lang, Nadine; Achenbach, Stephan; Eskofier, Björn; Arnold, Martin (12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2015)
Far too many people are dying from stroke or other heart related diseases each year. Early detection of abnormal heart rhythm could trigger the timely presentation to the emergency department or outpatient unit. Smartphones are an integral part of everyone;s life and they form the ideal basis for mobile monitoring and real-time analysis of signals related to the human heart. In this work, we investigated the performance of arrhythmia classification systems using only features calculated from the time instances of individual heart beats. We built a sinusoidal model using N (N = 10, 15, 20) consecutive RR intervals to predict the (N+1)th RR interval. The integration of the innovative sinusoidal regression feature, together with the amplitude and phase of the proposed sinusoidal model, led to an increase in the mean class-dependent classification accuracies. Best mean class-dependent classification accuracies of 90% were achieved using a Naïve Bayes classifier. Well-performing realtime analysis arrhythmia classification algorithms using only the time instances of individual heart beats could have a tremendous impact in reducing healthcare costs and reducing the high number of deaths related to cardiovascular diseases.

Automatic ECG Arrhythmia Detection in Real-Time on Android-based Mobile Devices - Leutheuser, Heike; Gottschalk, Tristan; Anneken, Lars; Struck, Matthias; Heuberger, Albert; Achenbach, Stephan; Eskofier, Björn; Arnold, Martin (Proceedings of International Conference on Mobile and Information Technologies in Medicine and Health, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.automa_72

Comparison of Real-Time Classification Systems for Arrhythmia Detection on Android-based Mobile Devices - Leutheuser, Heike; Gradl, Stefan; Kugler, Patrick; Anneken, Lars; Achenbach, Stephan; Eskofier, Björn; Arnold, Martin (IEEE EMBC 2014, 2014)

Early single center experience with a novel transcatheter anuloplasty system for the treatment of functional tricuspid regurgitation - Landendinger, M.; Smolka, S.; Tröbs, Monique; Anneken, Lars; Gaede, L.; Achenbach, Stephan; Arnold, Martin; Marwan, Mohamed (EUROPEAN HEART JOURNAL, 2020)
CRIS-Team WoS Importer:2021-02-12

Hypercalcemia mimicking myocardial infarction - Schmidt-Lauber, Christian; Anneken, Lars; Schödel, Johannes (Kidney International, 2019)
CRIS-Team Scopus Importer:2019-11-19

Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation - Qiu, Jingna; Oppelt, Maximilian; Nissen, Michael; Anneken, Lars; Breininger, Katharina; Eskofier, Björn (: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022)
Automated Electrocardiogram (ECG) classification using deep neural networks requires large datasets annotated by medical professionals, which is time-consuming and expensive. This work examines ECG augmentation as a method for enriching existing datasets at low cost. First, we introduce three novel augmentations: Limb Electrode Move and Chest Electrode Move both simulate a minor electrode mislocation during signal measurement, and Heart Vector Transform generates an ECG by modeling a rotated main heart axis. These techniques are then combined with nine time series signal augmentations from literature. Evaluation was performed on ICBEB, PTB-XL Diagnostic, PTB-XL Rhythm, and PTB-XL Form datasets. Compared to models trained without data augmentation, area under the receiver operating characteristic curve (AUC) was increased by 3.5%, 1.7%, 1.4% and 3.5%, respectively. Our experiments demonstrated that data augmentation can improve deep learning performance in ECG classification. Analyses of the individual augmenion effects established the efficacy of the three proposed augmentations.<br

Instantaneous P-and T-wave detection: Assessment of three ECG fiducial points detection algorithms - Leutheuser, Heike; Gradl, Stefan; Anneken, Lars; Lang, Nadine; Achenbach, Stephan; Eskofier, Björn; Arnold, Martin (13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2016)

Systematic Comparison of ECG Delineation Algorithm Performance on Smartwatch Data - Jäger, Katharina; Nissen, Michael; Flaucher, Madeleine; Graf, Luisa; Joanidopoulos, Joana; Anneken, Lars; Hübner, Hanna; Goossens, Chloe; Titzmann, Adriana; Pontones, Constanza; Fasching, Peter; Beckmann, Matthias; et al. (IEEE Access, 2024)
Cardiovascular diseases are the leading cause of global mortality, necessitating early detection and continuous monitoring for timely interventions. Smartwatches with electrocardiogram (ECG) recording capabilities enable real-time, at-home cardiac monitoring. Specific ECG characteristics can provide insights into cardiovascular diseases. The delineation of ECGs, which is the identification of fiducial points (such as onsets, offsets, and peaks), is a time-consuming task. Automated ECG delineation can enhance this process, but existing research comparing available algorithms is limited. Furthermore, to the best of our knowledge, none have addressed single-lead ECGs from smartwatches, which can be noisy and unfiltered. Thus, this study evaluates the best-performing open-source algorithm for single-lead and smartwatch ECG data. We used two public datasets (Lobachevsky University Database, QT Database) and two smartwatch datasets (SmartHeartWatch Dataset, SMART Start Dataset) including two devices (AppleWatch, Withings ScanWatch). Algorithms from three toolkits (NeuroKit, ECGKit, ECGdeli) were assessed based on the time deviation between algorithm outputs and reference annotations, sensitivity, true positives, and false negatives. Results were further evaluated against the Common Standards for Quantitative Electrocardiography (CSE) recommendations. ECGdeli outperformed the other algorithms. For QRS on- and offset, ECGkit shows comparable sensitivity, but otherwise lower scores. NeuroKit consistently shows lower sensitivity across all four data sets, however, the temporal deviation between detected point and reference was higher. Overall, sensitivity scores were higher for Apple Watch data compared to Withings ScanWatch data. This study demonstrates that segmentation algorithms are applicable to single-lead smartwatch ECG data, with ECGdeli being the most stable overall, and NeuroKit recommended for scenarios prioritizing the temporal accuracy of detected points.
CRIS-Team Scopus Importer:2024-11-15
Keywords: Cardiovascular Diseases; ECG Segmentation; Mobile Health; Wearable Devices

Textile Integrated Wearable Technologies for Sports and Medical Applications - Leutheuser, Heike; Lang, Nadine; Gradl-Trautvetter, Stefan; Struck, Matthias; Tobola, Andreas; Hofmann, Christian; Anneken, Lars; Eskofier, Björn (Smart Textiles, 2017)

Wireless LV endocardial stimulation for CRT: Acute and short term results after retrograde and transseptal implantation - Gruber, M.; Anneken, Lars; Feher, Marcel; Yovcheva, I.; Achenbach, Stephan; Arnold, Martin (EUROPEAN HEART JOURNAL, 2018)
CRIS-Team WoS Importer:2019-03-15