  The processing of ECG signal provides a wealth of information on cardiac
function and overall cardiovascular health. While multi-lead ECG recordings are
often necessary for a proper assessment of cardiac rhythms, they are not always
available or practical, for example in fetal ECG applications. Moreover, a wide
range of small non-obtrusive single-lead ECG ambulatory monitoring devices are
now available, from which heart rate variability (HRV) and other health-related
metrics are derived. Proper beat detection and classification of abnormal
rhythms is important for reliable HRV assessment and can be challenging in
single-lead ECG monitoring devices. In this manuscript, we modeled the heart
rate signal as an adaptive non-harmonic model and used the newly developed
synchrosqueezing transform (SST) to characterize ECG patterns. We show how the
proposed model can be used to enhance heart beat detection and classification
between normal and abnormal rhythms. In particular, using the Massachusetts
Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and
the Association for the Advancement of Medical Instrumentation (AAMI) beat
classes, we trained and validated a support vector machine (SVM) classifier on
a portion of the annotated beat database using the SST-derived instantaneous
phase, the R-peak amplitudes and R-peak to R-peak interval durations, based on
a single ECG lead. We obtained sensitivities and positive predictive values
comparable to other published algorithms using multiple leads and many more
features.
