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Clusteroluminescence (CL) materials without largely conjugated structures have gained significant attention due to their unique photophysical properties and potential in bioimaging. However, low luminescence efficiency and short emission wavelength limit their development. This work designs three luminogens with CL properties (CLgens) by introducing n‐electron‐involved through‐space conjugation (TSC) into diarylmethane. Apart from single‐photon excited long‐wavelength (686 nm) and high‐efficiency (29 %) CL, two‐photon clusteroluminescence (TPCL) is successfully achieved in such small luminogens with only two isolated heteroatomic units. TSC stabilized in the aggregate state has been proven to realize efficient spatial electron delocalization similar to conventionally conjugated compounds. Encouraged by the excellent TPCL properties, two‐photon imaging of blood vessels in vivo and biocompatibility verification utilizing CLgens are also achieved. This work illustrates the essential role of TSC in promoting nonlinear optical properties of CLgens and may facilitate further design and development of the next generation of bioprobes with excellent biocompatibility. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is the most common persistent arrhythmia, and it is crucial to develop generalizable automatic AF detection methods. However, supervised AF detection is often limited in performance due to the difficulty in obtaining labeled data. To address the gap between limited labeled data and the requirements for model robustness and generalization in single-lead ECG AF detection, we proposed a semi-supervised contrastive learning method named MLMCL for AF detection. The MLMCL method utilizes the multi-level feature representations of the encoder to perform multiple contrastive learning to fully exploit temporal consistency, channel consistency, and label consistency. Meanwhile, it combines labeled and unlabeled data for pre-training to obtain robust features for downstream tasks. In addition, it uses the domain knowledge in the field of AF diagnosis for domain knowledge augmentation to generate hard samples and improve the distinguishability of ECG representations. In the cross-dataset testing mode, MLMCL had better performance and good stability on different test sets, demonstrating its effectiveness and robustness in the AF detection task. The comparison results with existing studies show that MLMCL outperformed existing methods in external tests. The MLMCL method can be extended and applied to multi-lead scenarios and has reference significance for the development of contrastive learning methods for other arrhythmia. [ABSTRACT FROM AUTHOR]

Heartbeat inter-beat interval (IBI) extraction is a crucial technology for unobtrusive vital sign monitoring, yet its precision and robustness remain challenging. A promising approach is fusing heartbeat signals from different types of unobtrusive sensors. This paper introduces HSF-IBI, a novel and universal framework for unobtrusive IBI extraction using heterogeneous sensor fusion. Specifically, harmonic summation (HarSum) is employed for calculating the average heart rate, which in turn guides the selection of the optimal band selection (OBS), the basic sequential algorithmic scheme (BSAS)-based template group extraction, and the template matching (TM) procedure. The optimal IBIs are determined by evaluating the signal quality index (SQI) for each heartbeat. The algorithm is morphology-independent and can be adapted to different sensors. The proposed algorithm framework is evaluated on a self-collected dataset including 19 healthy participants and an open-source dataset including 34 healthy participants, both containing heterogeneous sensors. The experimental results demonstrate that (1) the proposed framework successfully integrates data from heterogeneous sensors, leading to detection rate enhancements of 6.25 % and 5.21 % on two datasets, and (2) the proposed framework achieves superior accuracy over existing IBI extraction methods, with mean absolute errors (MAEs) of 5.25 ms and 4.56 ms on two datasets. [ABSTRACT FROM AUTHOR]

Pulse signals can serve as important indicators of one's cardiovascular condition. However, capturing signals with stable morphology using radar under varying measurement periods remains a significant challenge. This paper reports a non-contact arterial pulse measurement method based on mmWave radar, with stable signals achieved through a range–angle focusing algorithm. A total of six subjects participated in the experiment, and the results showed that, under different measurement times, the pulse morphology of the same body part for each subject had good consistency, reaching a correlation of over 0.84, and four selected pulse signs remained stable. This is a quantitative assessment revealing a high correlation in pulse morphology measured by radar over different periods. In addition, the influence of array size and measurement distance was analyzed, providing a reference of array selection for research work with different requirements. This work offers an effective reference framework for long-term pulse measurement using radar technology. [ABSTRACT FROM AUTHOR]