[Research progress on clinical evaluation index of brain function after cardiopulmonary resuscitation].

The ultimate goal of cardiac arrest-cardiopulmonary resuscitation (CA-CPR) is to reduce brain damage and promote neurological recovery. Although the return of spontaneous circulation (ROSC) has improved, the proportion of patients who survive to discharge is very low, so how to evaluate the recovery of brain function after resuscitation is particularly important in clinical work. From a clinical perspective, although early prognostic indicators are not perfect, identifying high-risk features may help clinicians determine the severity of brain injury caused by a patient's potential course of disease.

Multiscale-temporal Feature Extraction and boundary confusion alleviation for VAG-based fine-grained multi-grade osteoarthritis deterioration monitoring.

Background And Objective: Multi-grade osteoarthritis (OA) deterioration monitoring in the daily paradigm using Vibroarthrography (VAG) is very challenging due to two difficulties: (1) the composition of VAG signals is complex in the daily paradigm where friction is intensified because of weight-bearing movements. (2) VAG signal samples near the decision boundary of adjacent deterioration grades are easy to be misclassified. The majority of existing works only focus on the binary classification of OA, providing inadequate assistance in instructing physicians to develop treatment plans based on the presence or absence of OA.

Vibroarthrography-based Knee Lesions Location via Multi-Label Embedding Learning.

Vibration arthrography (VAG) signals are widely utilized for knee pathology recognition due to their non-invasive and radiation-free nature. While most studies focus on determining knee health status, few have examined using VAG signals to locate knee lesions, which would greatly aid physicians in diagnosis and patient monitoring. To address this, we propose using Multi-Label classification (MLC) to efficiently locate different types of lesions within a single input.

Cross-subject EMG hand gesture recognition based on dynamic domain generalization.

Electromyography (EMG) signal based cross-subject gesture recognition methods reduce the influence of individual differences using transfer learning technology. These methods generally require calibration data collected from new subjects to adapt the pre-trained model to existing subjects. However, collecting calibration data is usually trivial and inconvenient for new subjects.

Online Unsupervised Domain Adaptation via Reducing Inter- and Intra-Domain Discrepancies.

Unsupervised domain adaptation (UDA) transfers knowledge from a labeled source domain to an unlabeled target domain on cross-domain object recognition by reducing a distribution discrepancy between the source and target domains (interdomain discrepancy). Prevailing methods on UDA were presented based on the premise that target data are collected in advance. However, in online scenarios, the target data often arrive in a streamed manner, such as visual image recognition in daily monitoring, which means that there is a distribution discrepancy between incoming target data and collected target data (intradomain discrepancy).

Alleviating Feature Confusion in Cross-Subject Human Activity Recognition via Adversarial Domain Adaptation Strategy.

Sensor-based Human Activity Recognition (HAR) plays an important role in health care. However, great individual differences limit its application scenarios and affect its performance. Although general domain adaptation methods can alleviate individual differences to a certain extent, the performance of these methods is still not satisfactory, since the feature confusion caused by individual differences tends to be underestimated.

PPG-based Biometric Identification: Discovering and Identifying a New User.

The convenience of Photoplethysmography (PPG) signal acquisition from wearable devices makes it becomes a hot topic in biometric identification. A majority of studies focus on PPG biometric technology in a verification application rather than an identification application. Yet, in the identification application, it is an inevitable problem in discovering and identifying a new user.

Cross-subject EEG-based Emotion Recognition Using Adversarial Domain Adaption with Attention Mechanism.

Cross-subject EEG-based emotion recognition (ER) is a rewarding work in real-life applications, due to individual differences between one subject and another subject. Most existing studies focus on training a subject-specific ER model. However, it is time-consuming and unrealistic to design the customized subject-specific model for a new subject in cross-subject scenarios.

End-to-End Versatile Human Activity Recognition with Activity Image Transfer Learning.

Transfer learning is a common solution to address cross-domain identification problems in Human Activity Recognition (HAR). Most existing approaches typically perform cross-subject transferring while ignoring transfers between different sensors or body parts, which limits the application scope of these models. Only a few approaches have been made to design a versatile HAR approach (cross-subject, cross-sensor and cross-body-part).

Online Cross-subject Emotion Recognition from ECG via Unsupervised Domain Adaptation.

Performing cross-subject emotion recognition (ER) using electrocardiogram (ECG) is challenging, since inter-subject discrepancy (caused by individual differences) between source and target subjects (new subjects) may hinder the generalization for new subjects. Recently, some ER methods based on unsupervised domain adaptation (UDA) are proposed to address inter-subject discrepancy. However, when being applied for online scenarios with time-varying ECG, existing methods may suffer performance degradation due to neglecting intra-subject discrepancy (caused by time-varying ECG) within target subjects, or need to re-train ER model, leading to time-and resource-consuming.

Reducing bias to source samples for unsupervised domain adaptation.

Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while labels are only available in the source domain. Lots of works in UDA focus on finding a common representation of the two domains via domain alignment, assuming that a classifier trained in the source domain can be generalized well to the target domain. Thus, most existing UDA methods only consider minimizing the domain discrepancy without enforcing any constraint on the classifier.

Multi-label Arrhythmia Classification from Fixed-length Compressed ECG Segments in Real-time Wearable ECG Monitoring.

Recently, classification from compressed physiological signals in compressed sensing has been successfully applied to cardiovascular disease monitoring. However, in real-time wearable electrocardiogram (ECG) monitoring, it is very difficult to directly obtain the heartbeats information from compressed ECG signals. Thus arrhythmia classification from compressed ECG signals has to be handled in fixed-length segments instead of individual heartbeats.