The presented method is evaluated using the training and hidden test sets of the PhysioNet/CinC Challenge 2016. Also, the results are compared with the top five ranked submissions. The results indicate that the proposed method is effective in classifying heart sounds as normal versus abnormal recordings.

The most straightforward method for heart beat estimation is R-peak detection based on an electrocardiogram (ECG) signal. Current R-peak detection methods do not work properly when the ECG signal is contaminated or missing, which leads to the incorrect estimation of the heart rate. This raises the need for reliable algorithms which can locate heart beats in continuous long-term multimodal data, allowing robust analysis.In this paper, three peak detectors are evaluated for heart beat detection using various cardiovascular signals. One of the peak detectors is a new general peak detector (GPD) algorithm which is applicable on ECG and other pulsatile signals to compensate for the limitation of QRS detection. This peak detector algorithm is adaptive and independently finds amplitude characteristics for every recording, while not tuned for ECG or other pulsatile signals. Three strategies, which are different disciplines of detectors, are then proposed while the fusion method remains the same in all strategies. In the first strategy, the ECG and the lowest-indexed signal of general blood pressure (BP), arterial blood pressure (ART) and pulmonary arterial pressure (PAP) are processed through gqrs and wabp (from the PhysioNet library), respectively. In the second strategy, all beats in different signals are detected by GPD. In the third strategy, ECG and other signals are processed by gqrs and GPD, respectively. In all three strategies two criteria are used in order to fuse the detections. The first criterion is based on the number of candidate detections in a specific time period, based on which signals of interest are selected. The second fusion criterion is based on the regularity of the derived intervals between subsequent candidate detections. If the number of detections in ECG and one of BP, ART and PAP signals have reasonable physiological range, a new signal is generated in which they are coupled with each other. Heart beats can more easily be detected in noisy parts of these signals using the new coupled waveform. For instance, if ECG and BP are coupled, BP pulses make the real heart beats in noisy parts of ECG detectable and ECG R-peaks make the weak BP pulses detectable in the new waveform. The proposed peak detector is developed using the MIT/BIH arrhythmia database. Furthermore, heart beat detection strategies were evaluated using the train and test datasets of PhysioNet/CinC Challenge (2014), and the overall results of the strategies are compared.

0.811, 0.872 and 0.841, respectively.

The purpose of this study is to provide a new method for detecting fetal QRS complexes from non-invasive fetal electrocardiogram (fECG) signal. Despite most of the current fECG processing methods which are based on separation of fECG from maternal ECG (mECG), in this study, fetal heart rate (FHR) can be extracted with high accuracy without separation of fECG from mECG. Furthermore, in this new approach thoracic channels are not necessary. These two aspects have reduced the required computational operations. Consequently, the proposed approach can be efficiently applied to different real-time healthcare and medical devices. In this work, a new method is presented for selecting the best channel which carries strongest fECG. Each channel is scored based on two criteria of noise distribution and good fetal heartbeat visibility. Another important aspect of this study is the simultaneous and combinatorial use of available fECG channels via the priority given by their scores. A combination of geometric features and wavelet-based techniques was adopted to extract FHR. Based on fetal geometric features, fECG signals were divided into three categories, and different strategies were employed to analyze each category. The method was validated using three datasets including Noninvasive fetal ECG database, DaISy and PhysioNet/Computing in Cardiology Challenge 2013. Finally, the obtained results were compared with other studies. The adopted strategies such as multi-resolution analysis, not separating fECG and mECG, intelligent channels scoring and using them simultaneously are the factors that caused the promising performance of the method.