Reliable P wave detection is necessary for accurate and automatic electrocardiogram (ECG) analysis. Currently, methods for P wave detection in physiological conditions are well-described and efficient. However, methods for P wave detection during pathology are not generally found in the literature, or their performance is insufficient. This work introduces a novel method, based on a phasor transform, as well as innovative rules that improve P wave detection during pathology. These rules are based on the extraction of a heartbeats' morphological features and knowledge of heart manifestation during both physiological and pathological conditions. To properly evaluate the performance of the proposed algorithm in pathological conditions, a standard database with a sufficient number of reference P wave positions is needed. However, such a database did not exist. Thus, ECG experts annotated 12 chosen pathological records from the MIT-BIH Arrhythmia Database. These annotations are publicly available via Physionet. The algorithm performance was also validated using physiological records from the MIT-BIH Arrhythmia and QT databases. The results for physiological signals were Se = 98.42% and PP = 99.98%, which is comparable to other methods. For pathological signals, the proposed method reached Se = 96.40% and PP = 85.84%, which greatly outperforms other methods. This improvement represents a huge step towards fully automated analysis systems being respected by ECG experts. These systems are necessary, particularly in the area of long-term monitoring.
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http://dx.doi.org/10.1038/s41598-019-55323-3 | DOI Listing |
Phys Rev Lett
December 2024
Brookhaven National Laboratory, Condensed Matter Physics and Materials Science Division, Upton, New York 11973, USA.
We present a protocol for detecting multipartite entanglement in itinerant many-body electronic systems using single-particle Green's functions. To achieve this, we first establish a connection between the quantum Fisher information and single-particle Green's functions by constructing a set of witness operators built out of single electron creation and destruction operators in a doubled system. This set of witness operators is indexed by a momentum k.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Université de Genève, Département de Physique Théorique and Gravitational Wave Science Center (GWSC), 24 quai Ernest Ansermet, 1211 Genève 4, Switzerland.
Joint gravitational-wave and γ-ray burst (GRB) observations are among the best prospects for standard siren cosmology. However, the strong selection effect for the coincident GRB detection, which is possible only for sources with small inclination angles, induces a systematic uncertainty that is currently not accounted for. We show that this severe source of bias can be removed by inferring the a priori unknown electromagnetic detection probability directly from multimessenger data.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Sun Yat-sen University, School of Physics and Astronomy, Zhuhai 519082, China.
Vortex states of photons, electrons, and other particles are freely propagating wave packets with helicoidal wave fronts winding around the axis of a phase vortex. A particle prepared in a vortex state carries a nonzero orbital angular momentum projection on the propagation direction, a quantum number that has never been exploited in experimental particle and nuclear physics. Low-energy vortex photons, electrons, neutrons, and helium atoms have been demonstrated in experiment and found numerous applications, and there exist proposals of boosting them to higher energies.
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
January 2025
School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, Changzhou University, Changzhou, P.R. China.
Slow eye movements (SEMs) are a reliable physiological marker of drivers' sleep onset, often accompanied by EEG alpha wave attenuation. A parallel multimodal 1D convolutional neural network (PM-1D-CNN) model is proposed to classify SEMs. The model uses two parallel 1D-CNN blocks to extract features from EOG and EEG signals, which are then fused and fed into fully connected layers for classification.
View Article and Find Full Text PDFMater Horiz
January 2025
Institute of Biomass and Function Materials & National Demonstration Centre for Experimental Light Chemistry Engineering Education, College of Bioresources Chemistry and Materials Engineering, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China.
Intelligent electronic textiles have important application value in the field of wearable electronics due to their unique structure, flexibility, and breathability. However, the currently reported electronic textiles are still challenged by issues such as their biocompatibility, photothermal conversion, and electromagnetic wave contamination. Herein, a multifunctional biomass-based conductive coating was developed using natural carboxymethyl starch (CMS), dopamine and polypyrrole (PPy) and then further employed for constructing multifunctional intelligent electronic textiles.
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