Physiological measurement最新文献

Objective. Fetal and maternal health during pregnancy can be monitored with sensors such as Doppler or scalp fetal ECG. This study focuses on single-channel dry electrode maternal abdominal ECG (aECG) to extract fetal heart rate (fHR) using a low-complexity algorithm suitable for low-power wearables.Approach. A hybrid model combining machine learning, QRS masking, and data fusion was trained on two PhysioNet databases and synthetically generatedaECG. Model selection employed the Akaike criterion with data balancing and random sampling.Main results. The algorithm was tested on 80 recordings from the Computer in Cardiology Challenge 2013 (CCC) and the abdominal and direct fetal database (ADFD), augmented with 100 syntheticaECG. Performance for fetal QRS detection reachedPrecision=97.2(82.2)%,Specificity=99.8(93.8)%, andSensitivity=97.4(93.9)% on ADFD and CCC, respectively. Clinical validation used the Polar Electro Oy H10 dry-electrode device at the Maternity Hospital of Southwest Finland. Four subjects (gestational age39.8±1.3 weeks) were analyzed, with seven discarded. ForfHR, the mean absolute percentage error was1.9±1.0%, Availability79.6±3.9%, and coverage probabilityCP5=76.2%,CP10=87.5%.Significance. These results demonstrate the feasibility offHRmonitoring from dry-electrodeaECGtailored for low-power wearables. Signal quality in clinical subjects matched the lowest PhysioNet cases, confirming robustness under low signal-to-noise conditions.

Objective.the fetal electrocardiogram (FECG) is critical for monitoring fetal health, however, its extraction remains technically challenging due to strong interference from the maternal electrocardiogram (MECG) in abdominal electrocardiogram (AECG). Therefore, an attention-based generative adversarial network (AGAN) is proposed for source separation of FECG from single-lead AECG signals.Approach.the AGAN architecture uniquely combines two powerful techniques: GAN-style adversarial training for high-quality data generation and attention-based focus mechanisms for intelligent feature selection, leading to superior target signal extraction from complex mixtures. The innovation of the proposed method lies in addressing the amplitude bias issue in multi-objective learning tasks. This work innovatively employs the Hadamard product as the learning objective for the model, preventing the model from favoring high-amplitude components (e.g. MECG) while neglecting low-amplitude yet critical features (e.g. FECG).Main results.experimental results demonstrate that the proposed method can effectively and simultaneously separate both MECG and FECG components from single-lead AECG signals. When evaluated on the ADFECGDB, B2_LABOUR, and PCDB datasets, the proposed method demonstrated consistent performance, achieving the following SE, PPV, andF1 scores: 96.67%, 97.13%, and 96.90% on ADFECGDB; 95.90%, 96.56%, and 96.22% on B2_LABOUR; and 94.96%, 95.18%, and 95.06% on PCDB.Significance.this study presents a robust method for FECG extraction while simultaneously introducing an innovative data-driven framework for blind source separation problems.

Infrared thermography (IRT) is projected as an innovative and very promising tool for the observation of muscle response to fatigue. The aim of this study was to observe the skin temperature (Tsk) by thermography about acute muscle fatigue and delayed soreness (DOMS) following a exercise protocol until exhaustion in the triceps suralis. An open longitudinal descriptive observational study of the posterior leg region was performed in 73 healthy subjects. Data on age, sex, body mass index (BMI) and triceps suralis thermography pre, post and 24 h after maximum muscle fatigue physical exercise, as well as pressure-pain threshold (PPT) and pain sensation by Analogic visual scale (VAS scale) were recruited. Results showed significant difference in skin temperature over time (p= <0.001; η2p= 0.272), side (p= 0.021; η2p= 0.072) and a time x side interaction (p= 0.011; η2p= 0.061), as well as in PPT over time (p= <0.001; η 2 p= 0.328) and in the interaction between time and sex (p= 0.050; η 2 p= 0.041). and Vas scale over time (p= <0.001; η 2 p= 0.831). According to the results obtained, this technique could be a reliable method to evaluate DOMS. Exploring the integration of thermography with other modalities.

Objective: As cardiovascular diseases continue to rise, the accurate and convenient calculation of stroke volume (SV) and cardiac output (CO) has become an important topic. Studies have shown that electrical impedance tomography (EIT) can provide continuous non-invasive SV measurements. Despite its potential, a review of the various calculation methods for EIT-based SV and CO, along with their clinical utility, is lacking. Approach: A literature search was conducted on PubMed and Web of Science Core Collection. Full-text research articles in English were reviewed and discussed. Main results: In recent years, advancements in technology, clinical research, and intelligent algorithms have revealed EIT's substantial potential in SV monitoring. Significance: This article offers a review of the evolution of EIT technology in measuring SV, introducing various calculation methods, their advantages, challenges, and clinical applications. .

Objective: Autonomous Sensory Meridian Response (ASMR) is a tingling sensation induced while attending to specific sounds, including whispering, tapping, scratching, or other soft, repetitive noises. While previous studies focused on low arousal-positive emotions such as relaxation and calmness, this study explores a broader range of emotions elicited by ASMR auditory stimuli, including happiness, sadness, and disgust.

Approach: The proposed study collects the multi-modal physiological data from Electroencephalography (EEG), Photoplethysmography (PPG), and Electrodermal Activity (EDA) via wearable bio-sensors from 23 ASMR-experiencing participants while exposed to different ASMR-inducing auditory stimuli. It employs the rmANOVA test on the collected physiological responses and self-reported ratings for quantitative analysis and results in a significant difference between the emotions induced from the four audio stimuli, i.e., Happy from A1, Sad from A2, Calm from A3, Disgust from A4, and the neutral state. The proposed study also applies deep learning classifiers, Artificial Neural Network (ANN), and Convolution Neural Network (CNN) to the collected multi-modal physiological data to classify the four induced emotions from the ASMR auditory stimuli using the dimensions of arousal, valence, and dominance.

Main results: The classification accuracy results from ANN, and CNN prove an excellent success rate of 96.12% and 74.25% with multi-modal Valence-Arousal-Dominance (VAD) for ANN and CNN, respectively, in classifying the four emotions induced by ASMR stimuli. And the statistical rmANOVA test results indicated distinctions among the four emotions, as the p-values exceeded the significance threshold of 0.05.

Significance: The results highlight the effectiveness of multi-modal physiological signals and deep learning in reliably classifying ASMR-induced emotions, contributing to advancements in emotion recognition for mental health and therapeutic applications.

Objective: Wearable devices with embedded photoplethysmography (PPG) enable continuous non-invasive monitoring of cardiac activity, offering a promising strategy to reduce the global burden of cardiovascular diseases. However, monitoring during daily life introduces motion artifacts that can compromise the signals. Traditional signal decomposition techniques often fail with severe artifacts. Deep learning denoisers are more effective but have poorer interpretability, which is critical for clinical acceptance. This study proposes a framework that combines the advantages of both signal decomposition and deep learning approaches.

Approach: We leverage algorithm unfolding to integrate prior knowledge about the PPG structure into a deep neural network, improving its interpretability. A learned convolutional sparse coding model encodes the signal into a sparse representation using a learned dictionary of kernels that capture recurrent morphological patterns. The network is trained for denoising using the PulseDB dataset and a synthetic motion artifact model from the literature. Performance is benchmarked with PPG during daily activities using the PPG-DaLiA dataset and compared with two reference deep learning methods.

Main results: On the synthetic dataset, the proposed method, on average, improved the signal-to-noise ratio (SNR) from -7.06 dB to 11.23 dB and reduced the heart rate mean absolute error (MAE) by 55%. On the PPG-DaLiA dataset, the MAE decreased by 23%. The proposed method obtained higher SNR and comparable MAE to the reference methods.

Significance: Our method effectively enhances the quality of PPG signals from wearable devices and enables the extraction of meaningful waveform features, which may inspire innovative tools for monitoring cardiovascular diseases.

Objective.Cardiovascular disease (CVD) causes severe global health threat, and electrocardiogram (ECG) is crucial for early CVD diagnosis. Recently, two popular deep learning methods, that is, convolutional neural network (CNN) and long short-term memory (LSTM) network are studied for ECG modeling and CVD diagnosis, but CNN adopts fixed kernels, thereby reducing efficiency and introducing noise, and LSTM struggles with local feature correlations.Approach.This study proposes an adaptive CNN-LSTM (aCNN-LSTM) fusion network for ECG diagnosis. An adaptive convolutional kernel is newly designed, which can dynamically adjust size based on local signal variance. Smaller kernels optimize efficiency in stationary segments, while larger kernels extract diverse features in non-stationary regions. The adaptive features from aCNN are further fed into LSTM to capture temporal relationships. Finally, a spatial-temporal fusion mechanism is used and a multi-class classification is achieved via the output layer.Main results.Experiments on the PTB-XL dataset show that the proposed aCNN-LSTM net outperforms CNN, LSTM, and CNN-LSTM in diagnosis performance: its overall accuracy reaches 89.89%, macro-averageF1-score is 0.9640, and weighted-averageF1-score is 0.9698.Significance.This method enhances the efficiency and accuracy of automatic ECG diagnosis, and provides reliable technical support for early CVD screening in clinical and primary medical settings.

Objective.Prompt identification of haematomas is crucial for effective clinical treatment. Magnetic induction phase shift technology (MIPS), known for its portability, non-contact nature, and affordability, is limited by the weak signal induced by cerebral hemorrhage leading to poor sensitivity, which is urgent to be improved.Approach. Tracer of magnetic nanoparticles is introduced to produce robust induced magnetic field. A symmetrical gradiometer coil is used as the receiving coil to nullify the effect of primary magnetic field generated by the excitation coil, which is designed as a Helmholtz coil.Main results.In vitroexperiments showcase the remarkably improved sensitivity and stability of the detection system, with magnetic nanoparticles notably boosting the MIPS signal for hemorrhage. Moreover,in vivoexperiments employing a rabbit autologous blood cerebral hemorrhage model reveal that with a hemorrhage volume of 2 ml, the experimental group with employed magnetic nanoparticles increased the MIPS signal change by 23-fold compared to the control group without magnetic nanoparticles.Significance. The sensitivity of MIPS for hemorrhage detection is significantly improved compared to traditional method. The magnetic nanoparticle-enhanced MIPS detection technique holds promise as an optimal solution for real-time, non-invasive bedside monitoring for cerebral hemorrhage.

Objective.Ultra-endurance cycling offers a natural laboratory for studying physiological responses under sustained extreme load. Continuous in-race monitoring is rarely reported. The aim of this study was to investigate the feasibility of a multimodal framework of physiological parameters including metabolic, cardiovascular, and muscle-mechanical patterns during an ultra-endurance event.Approach.This study stress-tests a multimodal framework of physiological parameters of a 58-year-old male athlete during the Race Across America (RAAM) 2024, covering 4933 km in 11 d from Oceanside, California, to Atlantic City, New Jersey. Parameters included energy expenditure, continuous blood glucose levels, heart rate, power output, passive muscle stiffness and resting tone, as well as sleep times.Main results.The multimodal monitoring toolkit proved feasible and provided continuous, physiological measurements throughout the RAAM, enabling the observation of the following physiological changes: The athlete lost 2.3 kg of total weight and had an estimated energy deficit of 21 169 kcal. Blood glucose levels decreased over the course of the RAAM (0.92 mg dl^-1d^-1,p< 0.001), with an increased time spent below 100 mg dl^-1(p< 0.001). Heart rate during cycling progressively decreased, stabilising at a plateau of 94 bpm. Power output-to-heart rate ratio initially dropped until day 7 before peaking on day 11. Mean passive muscle stiffness and resting tone increased during the race compared to baseline levels, with distinct response patterns observed between two leg muscles and one lower back muscle. The total sleep deficit was 65 h during the RAAM.Significance.Continuous, multimodal in-race physiological monitoring during the RAAM proved feasible and operationally useful, enabling real-time adjustments to pacing, fuelling and recovery. This framework offers a field-deployable template for ultra-endurance events. Future research should focus on larger, multi-participant studies and long-term follow-up to characterise the physiological responses to extreme endurance.

Objective.Deep learning for continuous physiological signals, such as electrocardiography or oximetry, has achieved remarkable success in supervised learning scenarios where training and testing data are drawn from the same distribution. However, when evaluating real-world applications, models often fail to generalize due to distribution shifts between the source domain on which the model was trained and the target domain where it is deployed. A common and particularly challenging shift often encountered in reality is where the source and target domain supports do not fully overlap. In this paper, we propose a novel framework, named Deep Unsupervised Domain adaptation using variable nEighbors (DUDE), to address this challenge.Approach.We introduce a new type of contrastive loss between the source and target domains using a dynamic neighbor selection strategy, in which the number of neighbors for each sample is adaptively determined based on the density observed in the latent space. We use multiple real-world datasets as source and target domains, with target domains that included demographics, ethnicities, geographies, and comorbidities that were not present in the source domain.Main results.The experimental results demonstrate superior DUDE performance compared to baselines and with an improvement of up to 16% over the original Nearest-Neighbor Contrastive Learning of Visual Representations strategy.Significance.Our contribution provides evidence on the potential of using DUDE to bridge the crucial gap of domain adaptation in medicine, potentially transforming patient care through more precise and adaptable diagnostic tools.