Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient
  Pipeline for Multi-Label Classification
Amnon Bleich Antje Linnemann Benjamin Jaidi Björn H Diem Tim OF Conrad
Implantable Cardiac Monitor (ICM) devices are demonstrating as of today, the
fastest-growing market for implantable cardiac devices. As such, they are
becoming increasingly common in patients for measuring heart electrical
activity. ICMs constantly monitor and record a patient's heart rhythm and when
triggered - send it to a secure server where health care professionals (denote
HCPs from here on) can review it. These devices employ a relatively simplistic
rule-based algorithm (due to energy consumption constraints) to alert for
abnormal heart rhythms. This algorithm is usually parameterized to an
over-sensitive mode in order to not miss a case (resulting in relatively high
false-positive rate) and this, combined with the device's nature of constantly
monitoring the heart rhythm and its growing popularity, results in HCPs having
to analyze and diagnose an increasingly growing amount of data. In order to
reduce the load on the latter, automated methods for ECG analysis are nowadays
becoming a great tool to assist HCPs in their analysis. While state-of-the-art
algorithms are data-driven rather than rule-based, training data for ICMs often
consist of specific characteristics which make its analysis unique and
particularly challenging. This study presents the challenges and solutions in
automatically analyzing ICM data and introduces a method for its classification
that outperforms existing methods on such data. As such, it could be used in
numerous ways such as aiding HCPs in the analysis of ECGs originating from ICMs
by e.g. suggesting a rhythm type.