Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa^-1) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices.

Due to advances in telemedicine, mobile medical care, wearable health monitoring, and electronic skin, great efforts have been directed to non-invasive monitoring and treatment of disease. These processes generally involve disease detection from interstitial fluid (ISF) instead of blood, and transdermal drug delivery. However, the quantitative extraction of ISF and the level of drug absorption are greatly affected by the individual’s skin permeability, which is closely related to the properties of the stratum corneum (SC). Therefore, measurement of SC impedance has been proposed as an appropriate way for assessing individual skin differences. In order to figure out the current status and research direction of human SC impedance detection, investigations regarding skin impedance measurement have been reviewed in this paper. Future directions are concluded after a review of impedance models, electrodes, measurement methods and systems, and their applications in treatment. It is believed that a well-matched skin impedance model and measurement method will be established for clinical and point-of care applications in the near future.

Today, excessive psychological stress has become a universal threat to humans. That stress can heavily affect work and study when a person repeatedly is exposed to high stress. If that exposure is long enough, it can even cause cardiovascular disease and cancer. Therefore, both monitoring and managing of stress is imperative to reduce the bad outcomes from excessive psychological stress. Conventional monitoring methods firstly extract the characteristics of the RR interval of an electrocardiogram (ECG) from a time domain and a frequency domain, then use machine learning models, like SVM, random forest, and decision tree, to distinguish the level of that stress. The biggest limitation of using these methods is that at least one minute of ECG data and other signals are indispensable to ensure the high accuracy of the results. This will greatly affect the real-time application of the models. To satisfy real-time detection of stress with high accuracy, we proposed a framework based on deep learning technology. The proposed monitoring framework is based on convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM). To evaluate the performance of this network, we conducted the experiments applying conventional methods. The data for the 34 subjects were collected on the server platform created by the group at the Institute of Psychology of the Chinese Academy of Sciences and our group. The accuracy of the proposed framework was up to 0.865 on three levels of stress using a 10 s ECG signal, a 0.228 improvement compared with conventional methods. Therefore, our proposed framework is more suitable for real-time applications

Emotion recognition is receiving significant attention in research on health care and Human-Computer Interaction (HCI). Due to the high correlation with emotion and the capability to affect deceptive external expressions such as voices and faces, Electroencephalogram (EEG) based emotion recognition methods have been globally accepted and widely applied. Recently, great improvements have been made in the development of machine learning for EEG-based emotion detection. However, there are still some major disadvantages in previous studies. Firstly, traditional machine learning methods require extracting features manually which is time-consuming and rely heavily on human experts. Secondly, to improve the model accuracies, many researchers used user-dependent models that lack generalization and universality. Moreover, there is still room for improvement in the recognition accuracies in most studies. Therefore, to overcome these shortcomings, an EEG-based novel deep neural network is proposed for emotion classification in this article. The proposed 2D CNN uses two convolutional kernels of different sizes to extract emotion-related features along both the time direction and the spatial direction. To verify the feasibility of the proposed model, the pubic emotion dataset DEAP is used in experiments. The results show accuracies of up to 99.99% and 99.98 for arousal and valence binary classification, respectively, which are encouraging for research and applications in the emotion recognition field.

Objectives: Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for sepsis 6 hours ahead on the basis of clinical electronic health records. Data Sources: Challenge data are released by PhysioNet/Computing in Cardiology Challenge 2019 and obtained from ICU patients in three separate hospital systems. Part of the data from two datasets, including 40,336 subjects, are publicly available, and the remaining are used as hidden test set. A normalized utility score defined by the organizing committee is used for model performance evaluation. Study Selection: The supervised machine learning is applied to tackle this challenge. Specifically, we establish the prediction model under the framework of ensemble learning by integrating the artificial features based on clinical prior knowledge of sepsis with deep features automatically extracted by long short-term memory neural network. Data Extraction: Forty clinical variables, including eight vital signs, 26 laboratory values, and six demographics, were measured and recorded once an hour for each individual, and the binary label (0 or 1) was simultaneously provided for each item. Data Synthesis: The proposed model was evaluated by 30-fold cross-validation. The sensitivity, specificity, and normalized utility score were 0.641 ± 0.022, 0.844 ± 0.007, and 0.401 ± 0.019 on publicly available datasets, respectively. The final normalized utility score our team (UCAS_DataMiner) has obtained was 0.313 on full hidden test set (0.406, 0.373, and –0.215 on test set A, B, and C, respectively). Conclusions: We realized a 6-hour ahead early-onset prediction of sepsis on the basis of clinical electronic health record by ensemble learning. The results indicated the proposed model functioned well in the early prediction of sepsis. In particular, ensemble learning had a significant (p < 0.01) improvement than any single model in performance.