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Computational Electrocardiography

Kolloquium der Abteilung 8

Since 1942, when Goldberger introduced the 12-lead electrocardiography (ECG), this diagnostic principle has not been changed. Induced from mobile health monitoring, a fundamental change is required towards “computational ECG” (CECG), where big but noisy data volumes are processed in real time. 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. 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, smart homes enhanced with biosignal monitoring, or smart cars with, e.g., steering wheel embedded ECG sensors are seen as diagnostic spaces. In near future they 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 parameter computation, vi) physiological modeling, and vii) event prediction.




Deserno TM. Marx N. Computational electrocardiography: revisiting Holter ECG monitoring. Methods Inf Med. 2016; 55(4): 305-11.