  Electrocardiogram (ECG) signal is the most commonly used non-invasive tool in
the assessment of cardiovascular diseases. Segmentation of the ECG signal to
locate its constitutive waves, in particular the R-peaks, is a key step in ECG
processing and analysis. Over the years, several segmentation and QRS complex
detection algorithms have been proposed with different features; however, their
performance highly depends on applying preprocessing steps which makes them
unreliable in real-time data analysis of ambulatory care settings and remote
monitoring systems, where the collected data is highly noisy. Moreover, some
issues still remain with the current algorithms in regard to the diverse
morphological categories for the ECG signal and their high computation cost. In
this paper, we introduce a novel graph-based optimal changepoint detection
(GCCD) method for reliable detection of R-peak positions without employing any
preprocessing step. The proposed model guarantees to compute the globally
optimal changepoint detection solution. It is also generic in nature and can be
applied to other time-series biomedical signals. Based on the MIT-BIH
arrhythmia (MIT-BIH-AR) database, the proposed method achieves overall
sensitivity Sen = 99.76, positive predictivity PPR = 99.68, and detection error
rate DER = 0.55 which are comparable to other state-of-the-art approaches.
