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
