Correlations of visfatin with severity of acute myocardial infarction, cardiovascular risk factors and atrial fibrillation after percutaneous coronary intervention.

Background: To investigate the relationship between visfatin level in the peripheral blood of patients with acute myocardial infarction (AMI) patients and the severity of AMI, cardiovascular risk factors and atrial fibrillation after percutaneous coronary intervention (PCI). Methods: A total of 37 AMI patients diagnosed and treated in our hospital were selected as experimental group, and 35 patients with normal coronary angiography were enrolled as control group. The general pathological data and occurrence of atrial fibrillation after PCI of all the patients were recorded in detail, and the content of indexes related to the severity of AMI and visfatin was measured. Moreover, the correlations of visfatin with the severity of AMI, cardiovascular risk factors and atrial fibrillation after PCI were explored.

Seizure-Cluster-Inception CNN (SciCNN): A Patient-Independent Epilepsy Tracking SoC With 0-Shot-Retraining.

Epilepsy tracking System-on-Chips (SoC) usually perform patient-specific classification to deal with the patient-to-patient seizure pattern variation from a surface electroencephalogram (EEG). However, the patient-specific classifier training requires the EEG signals from the target patients a priori, which involves costly and time-consuming hospitalization for the inpatient data collection. To address this issue, this paper presents a patient-independent epilepsy tracking SoC that is trained with pre-existing databases and can be directly deployed to the target patients without collecting their data and performing cumbersome patient-specific training beforehand. The proposed SoC adopts a Seizure-Cluster-Inception Convolutional Neural Network (SciCNN) Neural Processor (SNP) to reduce SRAM access rate by 179.05× with the Kernel-Wise Pipeline (KWP). The 22-Ch. SoC achieves event-based sensitivity of 90.3%/90.4%/83.3% and specificity of 93.6%/95.7%/88.6% on unseen patients from CHB-MIT database/EU database/local hospital patient, respectively.

A 3-Mbps, 802.11g-Based EMG Recording System With Fully Implantable 5-Electrode EMG Acquisition Device.

We have developed a 5-electrode recording system that combines an implantable electromyography (EMG) device package with transcutaneous inductive power transmission, near-infrared (NIR) transcutaneous data telemetry and 3 Mbps Wi-Fi data acquisition for chronic EMG recording in vivo. This system comprises a hermetically-sealed single-chip, 5-electrode Implantable EMG Acquisition Device (IEAD), a custom external powering and Implant Telemetry Module (ITM), and a custom Wi-Fi-based Raspberry Pi-based Data Acquisition (RaspDAQ) and relay device. The external unit (ITM and RaspDAQ) is powered entirely by a single battery to achieve the objective of untethered EMG recording, for the convenience of clinicians and animal researchers. The IEAD acquires intramuscular EMG signals at 17.85 ksps/electrode while being powered transcutaneously by the ITM using 22 MHz near-field inductive coupling. The acquired EMG data is transmitted transcutaneously via NIR telemetry to the ITM, which in turn, transfers the data to the RaspDAQ for relaying to a laptop computer for display and storage. We have also validated the complete system by acquiring EMG signals from rodents for up to two months. Following the explantation of the devices, we have also conducted failure and histological analysis on the devices and the surrounding tissue, respectively.