Implantable and Flexible Electronics for In vivo Brain Activity Recordings

Applications of flexible electronics related to cardiocerebral vascular system

Ensuring accessible and high-quality healthcare worldwide requires field-deployable and affordable clinical diagnostic tools with high performance. In recent years, flexible electronics with wearable and implantable capabilities have garnered significant attention from researchers, which functioned as vital clinical diagnostic-assisted tools by real-time signal transmission from interested targets in vivo. As the most crucial and complex system of human body, cardiocerebral vascular system together with heart-brain network attracts researchers inputting profuse and indefatigable efforts on proper flexible electronics design and materials selection, trying to overcome the impassable gulf between vivid organisms and rigid inorganic units. This article reviews recent breakthroughs in flexible electronics specifically applied to cardiocerebral vascular system and heart-brain network. Relevant sensor types and working principles, electronics materials selection and treatment methods are expounded. Applications of flexible electronics related to these interested organs and systems are specially highlighted. Through precedent great working studies, we conclude their merits and point out some limitations in this emerging field, thus will help to pave the way for revolutionary flexible electronics and diagnosis assisted tools development.

Materials for Implantable Surface Electrode Arrays: Current Status and Future Directions

Surface electrode arrays are mainly fabricated from rigid or elastic materials, and precisely manipulated ductile metal films, which offer limited stretchability. However, the living tissues to which they are applied are nonlinear viscoelastic materials, which can undergo significant mechanical deformation in dynamic biological environments. Further, the same arrays and compositions are often repurposed for vastly different tissues rather than optimizing the materials and mechanical properties of the implant for the target application. By first characterizing the desired biological environment, and then designing a technology for a particular organ, surface electrode arrays may be more conformable, and offer better interfaces to tissues while causing less damage. Here, the various materials used in each component of a surface electrode array are first reviewed, and then electrically active implants in three specific biological systems, the nervous system, the muscular system, and skin, are described. Finally, the fabrication of next-generation surface arrays that overcome current limitations is discussed.

Model Construction of Using Physiological Signals to Detect Mental Health Status

BACKGROUND

Mental health is a direct indicator of human mental activity, and it also affects all aspects of the human body. It plays a very important role in monitoring human mental health.

OBJECTIVES

To design a mental health state detection model based on physiological signals to detect human mental health.

METHODS

For the detection of mental health, the sliding window method is used to divide the physiological signal dataset and the corresponding time into several segments and then calculate the physiological signal data in the sliding window for each physiological signal to form a sequence of characteristic values; according to the heart rate variability of the physiological signal, the heart rate variability (HRV) is extracted from the interval spectrum waveform: through the discrete trend analysis in statistics, the change characteristics of the ECG signal are analyzed, and the sequence statistical indicators of the physiological signal are calculated. With the help of a support vector machine used for the significant accuracy with less computation power, the physiological signals of the mental state are classified, and the discriminant function of the mental health state signals is normalized. A mental health state detection model is constructed according to the index system, the optimal solution of the model is obtained through the optimization function, and the mental health state detection is completed.

RESULT

The detection error of the proposed model is less which improves the detection accuracy and is less time consuming.

CONCLUSION

The detection model using physiological signals is proposed to evaluate the mental health status. As compared to the other detection models, its detection time is short and method error is always less than 2% which shows its accuracy and effectiveness.