\n @ARTICLE{Rooijakkers2012,
\n author = {Rooijakkers, Michael J AND Rabotti, Chiara AND Oei, S Guid AND Mischi, Massimo},
\n title = {Low-complexity R-peak detection for ambulatory fetal monitoring},
\n abstract = {Non-invasive fetal health monitoring during pregnancy is becoming increasingly important because of the increasing number of high-risk pregnancies. Despite recent advances in signal-processing technology, which have enabled fetal monitoring during pregnancy using abdominal electrocardiogram (ECG) recordings, ubiquitous fetal health monitoring is still unfeasible due to the computational complexity of noise-robust solutions. In this paper, an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, without reducing the R-peak detection performance compared to the existing R-peak detection schemes. Validation of the algorithm is performed on three manually annotated datasets. With a detection error rate of 0.23%, 1.32% and 9.42% on the MIT/BIH Arrhythmia and in-house maternal and fetal databases, respectively, the detection rate of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.},
\n keywords = {electrocardiography, R-peak detection, abdominal measurements, fetal ECG, wavelet transform, adaptive signal processing},
\n pages = {1135 - 1150},
\n bookTitle = {Physiological Measurement},
\n volume = {33},
number = {7},
year = {2012},
month = {Jun.}