Defibrillation is the most effective method of treating ventricular fibrillation(VF), this paper introduces wearable automatic external defibrillators based on embedded system which includes EGG measurements, bioelectrical impedance measurement, discharge defibrillation module, which can automatic identify VF signal, biphasic exponential waveform defibrillation discharge. After verified by animal tests, the device can realize EGG acquisition and automatic identification. After identifying the ventricular fibrillation signal, it can automatic defibrillate to abort ventricular fibrillation and to realize the cardiac electrical cardioversion.
Animals ; Defibrillators ; Electric Countershock ; Electric Impedance ; Equipment Design ; Heart ; Humans ; Monitoring, Physiologic ; instrumentation ; Ventricular Fibrillation ; therapy

Objective To explore the establishment of a rabbit model of atrial fibrillation by wireless telemetering and stimulation technology.Method An implantable telemetering stimulator which was independently designed and devel-oped was hypodermically implanted in New Zealand rabbits.The implantable telemetering stimulator was made with the core development and design of MSP single-chip microcomputer of TI Corporation ( Texas Instruments) and RF wireless trans-ceiver chip CC2250 of TI Corporation.The design of the implantation system was optimized to cater to the exploratory ex-periment to establish atrial fibrillation model of New Zealand rabbits.The implanter was implanted into the abdominal sub-cutaneous tissue of the New Zealand rabbits, the collecting electrodes were placed in the oxter subcutaneous tissues of the left and the right upper extremities, and the two stimulating electrodes were sutured at the left auricle and the left atrium. The signals were collected and stimulated by the wireless transceiver.The I-lead ECG electrical signals were continuously monitored on the body surface by a Powerlab physiological recorder.High frequency ( >20 Hz) suprathreshold stimulus ( intensity 2 mA, pulse width 1 ms) was emitted by specialized stimulation software of a computer program by the interval ( stimulating for 2 s and pausing for 2 s) .In case of atrial fibrillation during intervals, the stimulation could be stopped by hand.In case of sinus rhythm, the stimulation could be continued.Results The implantable telemetering stimulator can work stably in vivo ( including collecting stimulated electrocardio signal and emitting stimulations) for 30 days.Atrial fibril-lation can be induced after stimulating in vivo of the New Zealand rabbits for 3 weeks, with a duration of >48 h.Conclu-sions Applying implantable telemetering stimulator can build a New Zealand istead of beagles model of atrial fibrillation which is more consistent with welfare optimization and substitution principle for laboratory animals.

The rapid atrial pacing model is one of the most popular atrial fibrillation animal models. In this paper, a novel implementation of wireless implantable stimulating and ECG monitoring system is described based on the requirements of rapid atrial pacing model. Hardware circuits and software structure of the system are introduced. And test outcomes through in-vitro simulation and in-vivo animal models are presented. After verified by animal tests, the system can be used to initiate and monitor chronic atrial fibriation in real time.
Animals ; Atrial Fibrillation ; diagnosis ; Electrocardiography ; instrumentation ; Heart Atria ; physiopathology ; Models, Animal ; Monitoring, Physiologic ; instrumentation ; Prostheses and Implants ; Software

Defibrillation is the most effective method of treating ventricular fibrillation(VF), this paper introduces wearable automatic external defibrillators based on embedded system which includes ECG measurements, bioelectrical impedance measurement, discharge defibrilation module, which can automatic identify VF signal, biphasic exponential waveform defibrilation discharge. After verified by animal tests, the device can realize ECG acquisition and automatic identification. After identifying the ventricular fibrilation signal, it can automatic defibrilate to abort ventricular fibrilation and to realize the cardiac electrical cardioversion.

In this paper, we described an ultra-low power, wearable ECG system capable of long term monitoring and mass storage. This system is based on micro-chip PIC18F27J13 with consideration of its high level of integration and low power consumption. The communication with the micro-SD card is achieved through SPI bus. Through the USB, it can be connected to the computer for replay and disease diagnosis. Given its low power cost, lithium cells are used to support continuous ECG acquiring and storage for up to 15 days. Meanwhile, the wearable electrodes avoid the pains and possible risks in implanting. Besides, the mini size of the system makes long wearing possible for patients and meets the needs of long-term dynamic monitoring and mass storage requirements.
Electrocardiography, Ambulatory ; instrumentation ; Signal Processing, Computer-Assisted ; instrumentation

The rapid atrial pacing model is one of the most popular atrial fibrillation animal models. In this paper, a novel implementation of wireless implantable stimulating and ECG monitoring system is described based on the requirements of rapid atrial pacing model. Hardware circuits and software structure of the system are introduced. And test outcomes through in-vitro simulation and in-vivo animal models are presented. Affter verified by animal tests, the system can be used to initiate and monitor chronic atrialfibriation in real time.

Objective:To understand the epidemiological characteristics of COVID-19 monitoring cases in Yinzhou district based on health big data platform to provide evidence for the construction of COVID-19 monitoring system.Methods:Data on Yinzhou COVID-19 daily surveillance were collected. Information on patients’ population classification, epidemiological history, COVID-19 nucleic acid detection rate, positive detection rate and confirmed cases monitoring detection rate were analyzed.Results:Among the 1 595 COVID-19 monitoring cases, 79.94% were community population and 20.06% were key population. The verification rate of monitoring cases was 100.00%. The total percentage of epidemiological history related to Wuhan city or Hubei province was 6.27% in total, and was 2.12% in community population and 22.81% in key population ( P<0.001). The total COVID-19 nucleic acid detection rate was 18.24% (291/1 595), and 53.00% in those with epidemiological history and 15.92% in those without ( P<0.001).The total positive detection rate was 1.72% (5/291) and the confirmed cases monitoring detection rate was 0.31% (5/1 595). The time interval from the first visit to the first nucleic acid detection of the confirmed monitoring cases and other confirmed cases was statistically insignificant ( P>0.05). Conclusions:The monitoring system of COVID-19 based on the health big data platform was working well but the confirmed cases monitoring detection rate need to be improved.

During the prevention and control of the COVID-19 epidemic, identifying and controlling the source of infection has become one of the most important prevention and control measures to curb the epidemic in the absence of vaccines and specific therapeutic drugs. While actively taking traditional and comprehensive "early detection" measures, Yinzhou district implemented inter-departmental data sharing through the joint prevention and control mechanism. Relying on a healthcare big data platform that integrates the data from medical, disease control and non-health sectors, Yinzhou district innovatively explored the big data-driven COVID-19 case finding pattern with online suspected case screening and offline verification and disposal. Such effort has laid a solid foundation and gathered experience to conduct the dynamic and continuous surveillance and early warning for infectious disease outbreaks more effectively and efficiently in the future. This article introduces the exploration of this pattern in Yinzhou district and discusses the role of big data-driven disease surveillance in the prevention and control of infectious diseases.