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.
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.
SUMMARYThe aim of this study is to describe a new false-alarm probability (FAP) bounded unified framework for segmentation of the phonocardiogram (PCG) signal sounds registered by an electronic stethoscope board. To meet this end, first the original PCG signal is pre-processed by application of an appropriate bandpass finite-duration impulse response (FIR) filter and then by implementation of à trous discrete wavelet transform (DWT) to the filtered signal for extracting several dyadic scales. Then, after choosing a proper scale, a fixed sample size sliding window is moved on the selected scale and in each slide, six feature vectors namely summation of the nonlinearly amplified Hilbert transform, summation of absolute first order differentiation, summation of absolute second-order differentiation, curve length, area and variance of the excerpted segment are calculated. Then, all feature trends are normalized and utilized to construct a newly proposed principal components analyzed geometric index (PCAGI) (to be used as the segmentation decision statistic (DS)) by application of a linear orthonormal projection. Next, using an adaptive smoothing filter (ASF), the obtained metric is modulated and freed from the fast fluctuations occurring in the vicinity of events onset and offset locations which consequently results in enhancement of edge detection accuracy. Later, histogram parameters of the filtered DS metric are used for the regulation of the -level Neyman-Pearson classifier for FAP-bounded delineation of the PCG events. To assess the performance quality of the proposed PCG segmentation algorithm, the method was applied to all 85 records of Nursing Student Heart Sounds database including stenosis, insufficiency, regurgitation, gallop, septal defect, sound split, rumble, murmur, clicks, friction rub and snap disorders with different sampling frequencies. The method was also applied to the records obtained from an electronic stethoscope board designed for fulfillment of this study in the presence of high-level power-line noise and external disturbing sounds and as a result, no false positive or false negative errors were detected. High robustness against measurement noises of the electronic stethoscopes, acceptable detection-segmentation accuracy of PCG events in the presence of severe heart valvular and arrhythmic dysfunctions within a tolerable computational burden (processing time) and having no parameter dependency on the acquisition sampling frequency can be mentioned as important merits and capabilities of the proposed PCAGI-based PCG events detection-segmentation algorithm.
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