# Applications

###### QT interval analysis

Applied databases:

• Simulated ECG Data [1, 2, 3]
• Athletic Dataset [2, 4]
• QTDB [1, 5]: The QT Database – PhysioNet
• DEFINITE [4]: Defibrillator in Non-Ischemic Cardiomyopathy Treatment Evaluation Trial
• PTBDB [1, 4, 3]: The PTB Diagnostic ECG Database – PhysioNet
• TQT#2 [4, 3]: Thorough QT Study # 2
• SHHS2 [6]: Sleep Heart Health Study provided by National Sleep Research Resource
[1] M. Schmidt, M. Baumert, A. Porta, H. Malberg, and S. Zaunseder, “Two-Dimensional Warping for One-Dimensional Signals—Conceptual Framework and Application to ECG Processing,” IEEE Transactions on Signal Processing, vol. 62, iss. 21, p. 5577–5588, 2014.
[Bibtex]
@article{schmidt_two-dimensional_2014,
title = {Two-{{Dimensional Warping}} for {{One-Dimensional Signals}}\textemdash{{Conceptual Framework}} and {{Application}} to {{ECG Processing}}},
author = {Schmidt, M. and Baumert, M. and Porta, A. and Malberg, H. and Zaunseder, S.},
year = {2014},
month = nov,
journal = {IEEE Transactions on Signal Processing},
volume = {62},
number = {21},
pages = {5577--5588},
issn = {1053-587X},
doi = {10.1109/TSP.2014.2354313},
abstract = {We propose a novel method for evaluating the similarity between two 1d patterns. Our method, referred to as two-dimensional signal warping (2DSW), extends the basic ideas of known warping techniques such as dynamic time warping and correlation optimized warping. By employing two-dimensional piecewise stretching 2DSW is able to take into account inhomogeneous variations of shapes. We apply 2DSW to ECG recordings to extract beat-to-beat variability in QT intervals (QTV) that is indicative of ventricular repolarization lability and typically characterised by a low signal-to-noise ratio. Simulation studies show high robustness of our approach in presence of typical ECG artefacts. Comparison of short-term ECG recorded in normal subjects versus patients with myocardial infarction (MI) shows significantly increased QTV in patients (normal subject 2.36 ms {$\pm$} 1.05 ms vs. MI patients 5.94 ms {$\pm$} 5.23 ms (mean {$\pm$} std), ). Evaluation of a standard QT database shows that 2DSW allows highly accurate tracking of QRS-onset and T-end. In conclusion, the two-dimensional warping approach introduced here is able to detect subtle changes in noisy quasi-periodic biomedical signals such as ECG and may have diagnostic potential for measuring repolarization lability in MI patients. In more general terms, the proposed method provides a novel means for morphological characterization of 1d signals.},
keywords = {Correlation,Cost function,Dynamic time warping,ECG,Electrocardiography,Heuristic algorithms,Physiology,QT,QT interval,QT variability,signal processing,Signal processing algorithms,two-dimensional warping,Vectors,warping},
file = {C\:\\Users\\martin\\Zotero\\storage\\BYDNA77L\\6891378.html}
}
[2] M. Baumert, M. Schmidt, S. Zaunseder, and A. Porta, “Effects of ECG Sampling Rate on QT Interval Variability Measurement,” Biomedical Signal Processing and Control, vol. 25, iss. Supplement C, p. 159–164, 2016.
[Bibtex]
@article{baumert_effects_2016,
title = {Effects of {{ECG}} Sampling Rate on {{QT}} Interval Variability Measurement},
author = {Baumert, Mathias and Schmidt, Martin and Zaunseder, Sebastian and Porta, Alberto},
year = {2016},
month = mar,
journal = {Biomedical Signal Processing and Control},
volume = {25},
number = {Supplement C},
pages = {159--164},
issn = {1746-8094},
doi = {10.1016/j.bspc.2015.11.011},
abstract = {Beat-to-beat variability of the QT interval (QTV) has been used as a marker of repolarization lability and sympathetic activation. The aim of this study was to establish ECG sampling rate requirements for reliable QT interval variability measurement. We measured QTV in high resolution simulated (1000Hz) and real ECG (1600Hz; in the supine position during rest and during sympathetic activation upon standing), using time and frequency domain metrics as well as measures of symbolic dynamics for complexity assessment. We successively halved the sampling rate and investigated its effect on the QTV metrics. Reduction in sampling rate below 400Hz and 500Hz, respectively, resulted in a significant overestimation of QTV variability and also affected complexity measurement of QTV. QTV increased during standing compared to the supine measurement. At 100Hz, the posture related change in QTV was completely masked by the measurement noise introduced by the low sampling rate. In conclusion, ECG sampling rates of 500Hz yields a reliable QTV measurement, while sampling rates of 200Hz and below should be avoided.},
keywords = {ECG,QT interval,Sampling rate},
file = {C\:\\Users\\martin\\Zotero\\storage\\FNRHHRQV\\Baumert et al. - 2016 - Effects of ECG sampling rate on QT interval variab.pdf;C\:\\Users\\martin\\Zotero\\storage\\KTG83Q7R\\Baumert et al. - 2016 - Effects of ECG sampling rate on QT interval variab.pdf;C\:\\Users\\martin\\Zotero\\storage\\HVRFX9R6\\S1746809415001962.html;C\:\\Users\\martin\\Zotero\\storage\\NQV5XCV6\\S1746809415001962.html;C\:\\Users\\martin\\Zotero\\storage\\RCMR79G7\\S1746809415001962.html}
}
[3] M. Schmidt, M. Baumert, H. Malberg, and S. Zaunseder, “Iterative Two-Dimensional Signal Warping—Towards a Generalized Approach for Adaption of One-Dimensional Signals,” Biomedical Signal Processing and Control, vol. 43, p. 311–319, 2018.
[Bibtex]
@article{schmidt_iterative_2018,
title = {Iterative Two-Dimensional Signal Warping\textemdash{{Towards}} a Generalized Approach for Adaption of One-Dimensional Signals},
author = {Schmidt, Martin and Baumert, Mathias and Malberg, Hagen and Zaunseder, Sebastian},
year = {2018},
month = may,
journal = {Biomedical Signal Processing and Control},
volume = {43},
pages = {311--319},
issn = {1746-8094},
doi = {10.1016/j.bspc.2018.03.016},
abstract = {The assessment of subtle morphological changes in noisy signals is a common challenge in the field of biomedical signal processing. Concerning the electrocardiogram (ECG), it may yield novel risk factors for cardiac mortality. Here, we describe an iterative two-dimensional signal warping algorithm (i2DSW), which morphological analyses even in case of noise ratios. i2DSW adapts a generalized iterative template adaptation process that yields a more flexible template and allows for better fitting of subtle variations of signal shapes. Moreover, the template segmentation is not dependent on signal morphology. We test its performance, by measuring beat-to-beat repolarization variability in simulated and clinical ECG. Simulation studies show higher robustness of i2DSW in presence of typical ECG artefacts compared to previously proposed methods including the existing two-dimensional warping technique (26\% improvement). Comparison of short-term ECG recorded in normal subjects versus patients with myocardial infarction (MI) confirmed increased repolarization variability in MI patients (p\,\<\,0.0001). Results obtained with long-term ECG show improved waveform adaptation of i2DSW (overall 19\%, up to 33\%). The assessment of subtle morphological changes by i2DSW may yield novel and more robust risk factors for cardiac mortality. By avoiding a fixed template segmentation, the generalized design of i2DSW has the potential to be also powerful in the application to other quasi-periodic signals.},
keywords = {2DSW,ECG,i2DSW,QT interval,QT variability,Warping},
file = {C\:\\Users\\martin\\Zotero\\storage\\YEVSWUAU\\Schmidt et al. - 2018 - Iterative two-dimensional signal warping—Towards a.pdf;C\:\\Users\\martin\\Zotero\\storage\\FD9Q2M38\\S1746809418300740.html}
}
[4] M. Schmidt, M. Baumert, H. Malberg, and S. Zaunseder, “QT Interval Extraction by Two-Dimensional Signal Warping,” in Biomedical Engineering / Biomedizinische Technik, {Hannover}, 2014, p. 154–158.
[Bibtex]
@inproceedings{schmidt_qt_2014,
title = {{{QT}} Interval Extraction by Two-Dimensional Signal Warping},
booktitle = {Biomedical {{Engineering}} / {{Biomedizinische Technik}}},
author = {Schmidt, Martin and Baumert, Mathias and Malberg, Hagen and Zaunseder, Sebastian},
year = {2014},
month = oct,
volume = {59},
pages = {154--158},
publisher = {{Walter de Gruyter}},
doi = {10.1515/bmt-2014-4069},
abstract = {We propose a novel two-dimensional warping technique to match two one-dimensional patterns. Our approach, referred to as two-dimensional signal warping (2DSW), extends the basic ideas of known warping techniques such as dynamic time warping and correlation optimized warping. By employing two-dimensional piecewise stretching 2DSW is able to capture inhomogeneous variations of one-dimensional signals in direction of abscissa and ordinate. In order to prove the applicability of our method we apply 2DSW for tracking changes in beat-to-beat variability in QT intervals (QTV). Using simulated data we demonstrate the robustness of our approach. Analysis of long-term ECG from the DEFINITE trial on circadian rhythms demonstrated the sensitivity of the proposed algorithm to track changes in the QT interval. Using repeated measure ANOVA and Holm\textendash Bonferroni corrected paired Student's t-test for post-hoc analysis we found statistically significant differences in QTV between 1 a.m. to 8 a.m. and 10 a.m. to 11 p.m.},
isbn = {1862-278X}
}
[5] S. Zaunseder, M. Schmidt, H. Malberg, and M. Baumert, “Measurement of QT Variability by Two-Dimensional Warping,” in 2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO), 2014, p. 163–164.
[Bibtex]
@inproceedings{zaunseder_measurement_2014,
title = {Measurement of {{QT}} Variability by Two-Dimensional Warping},
booktitle = {2014 8th {{Conference}} of the {{European Study Group}} on {{Cardiovascular Oscillations}} ({{ESGCO}})},
author = {Zaunseder, Sebastian and Schmidt, Martin and Malberg, Hagen and Baumert, Mathias},
year = {2014},
month = may,
pages = {163--164},
doi = {10.1109/ESGCO.2014.6847570},
abstract = {This contribution presents a novel warping method, two-dimensional signal warping (2DSW), for tracking beat-to-beat changes in time intervals from electrocardiograms. To evaluate the efficiency of 2DSW to capture subtle changes in the QT interval we apply 2DSW to the Physionet QT database. It is shown that 2DSW allows highly accurate tracking of QRS-onset and T-end, which renders the method useful for future clinical applications, in particular beat-to-beat variability analysis of ECG features.},
keywords = {2DSW,Algorithm design and analysis,bioelectric potentials,Databases,ECG features,electrocardiograms,electrocardiography,Electrocardiography,Europe,high accurate tracking,medical signal processing,Oscillators,physionet QT database,QRS-onset,QT variability measurement,Standards,time intervals,Time measurement,two-dimensional signal warping method,variability analysis},
file = {C\:\\Users\\martin\\Zotero\\storage\\R7N5YCWD\\6847570.html}
}
[6] M. Schmidt, M. Baumert, T. Penzel, H. Malberg, and S. Zaunseder, “Nocturnal Ventricular Repolarization Lability Predicts Cardiovascular Mortality in the Sleep Heart Health Study,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 316, iss. 3, p. H495-H505, 2018.
[Bibtex]
@article{schmidt_nocturnal_2018,
title = {Nocturnal Ventricular Repolarization Lability Predicts Cardiovascular Mortality in the {{Sleep Heart Health Study}}},
author = {Schmidt, Martin and Baumert, Mathias and Penzel, Thomas and Malberg, Hagen and Zaunseder, Sebastian},
year = {2018},
month = dec,
journal = {American Journal of Physiology-Heart and Circulatory Physiology},
volume = {316},
number = {3},
pages = {H495-H505},
issn = {0363-6135},
doi = {10.1152/ajpheart.00649.2018},
abstract = {The objective of the present study was to quantify repolarization lability and its association with sex, sleep stage, and cardiovascular mortality. We analyzed polysomnographic recordings of 2,263 participants enrolled in the Sleep Heart Health Study (SHHS-2). Beat-to-beat QT interval variability (QTV) was quantified for consecutive epochs of 5 min according to the dominant sleep stage [wakefulness, nonrapid eye movement stage 2 (NREM2), nonrapid eye movement stage 3 (NREM3), and rapid eye movement (REM)]. To explore the effect of sleep stage and apnea-hypopnea index (AHI) on QT interval parameters, we used a general linear mixed model and mixed ANOVA. The Cox proportional hazards model was used for cardiovascular disease (CVD) death prediction. Sex-related differences in T wave amplitude (P {$<$} 0.001) resulted in artificial QTV differences. Hence, we corrected QTV parameters by T wave amplitude for further analysis. Sleep stages showed a significant effect (P {$<$} 0.001) on QTV. QTV was decreased in deep sleep compared with wakefulness, was higher in REM than in NREM, and showed a distinct relation to AHI in all sleep stages. The T wave amplitude-corrected QTV index (cQTVi) in REM sleep was predictive of CVD death (hazard ratio: 2.067, 95\% confidence interval: 1.105\textendash 3.867, P {$<$} 0.05) in a proportional hazards model. We demonstrated a significant impact of sleep stages on ventricular repolarization variability. Sex differences in QTV are due to differences in T wave amplitude, which should be corrected for. Independent characteristics of QTV measures to sleep stages and AHI showed different behaviors of heart rate variability and QTV expressed as cQTVi. cQTVi during REM sleep predicts CVD death.NEW \& NOTEWORTHY We demonstrate here, for the first time, a significant impact of sleep stages on ventricular repolarization variability, quantified as QT interval variability (QTV). We showed that QTV is increased in rapid eye movement sleep, reflective of high sympathetic drive, and predicts death from cardiovascular disease. Sex-related differences in QTV are shown to be owing to differences in T wave amplitude, which should be corrected for.},
file = {C\:\\Users\\martin\\Zotero\\storage\\ZB2VCUM6\\Schmidt et al. - 2018 - Nocturnal ventricular repolarization lability pred.pdf;C\:\\Users\\martin\\Zotero\\storage\\5PZP78R6\\ajpheart.00649.html}
}