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Methods Inf Med 2016; 55(04): 305-311
DOI: 10.3414/ME15-05-0009
Original Articles
Schattauer GmbH

Computational Electrocardiography: Revisiting Holter ECG Monitoring

Thomas M. Deserno
1   Aachen University of Technology (RWTH), Department of Medical Informatics, Aachen, Germany
,
Nikolaus Marx
2   Department of Cardiology – Uniklinik RWTH Aachen, Aachen, Germany
› Author Affiliations
Fundings This work has been partially funded by the European Foundation for the Study of Diabetes (EFSD), grant number 74550-94555 and by the Excellence Initiative of the German federal and state governments, grant number OPBF074.
Further Information

Publication History

received: 22 July 2015

accepted: 07 July 2015

Publication Date:
08 January 2018 (online)

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Summary

Background: Since 1942, when Goldberger introduced the 12-lead electrocardiography (ECG), this diagnostic method has not been changed.

Objectives: After 70 years of technologic developments, we revisit Holter ECG from recording to understanding.

Methods: A fundamental change is foreseen towards “computational ECG” (CECG), where continuous monitoring is producing big data volumes that are impossible to be inspected conventionally but require efficient computational methods. We draw parallels between CECG and computational biology, in particular with respect to computed tomography, computed radiology, and computed photography. From that, we identify technology and methodology needed for CECG.

Results: Real-time transfer of raw data into meaningful parameters that are tracked over time will allow prediction of serious events, such as sudden cardiac death. Evolved from Holter’s technology, portable smartphones with Bluetooth-connected textile-embedded sensors will capture noisy raw data (recording), process meaningful parameters over time (analysis), and transfer them to cloud services for sharing (handling), predicting serious events, and alarming (understanding). To make this happen, the following fields need more research: i) signal processing, ii) cycle decomposition; iii) cycle normalization, iv) cycle modeling, v) clinical param -eter computation, vi) physiological modeling, and vii) event prediction.

Conclusions: We shall start immediately developing methodology for CECG analysis and understanding.

Keywords

Automated ECG analysis - signal processing - computational biology - mobile health - big data