فهرس المقالات Heartbeat

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  1 - Heartbeat ECG Tracking Systems Using Observer Based Nonlinear Controller
  Mina Ali Akbar Semnani Ahmadreza Vali Seyed Mahdi Hakimi vahid behnamgol
  In this paper, an observer based sliding mode method is used to control the heart rhythm system. For this purpose, nonlinear and uncertain dynamics of a sick human heart are considered. The output of the three main parts of the heart is assumed to be the output of the c أکثر

  In this paper, an observer based sliding mode method is used to control the heart rhythm system. For this purpose, nonlinear and uncertain dynamics of a sick human heart are considered. The output of the three main parts of the heart is assumed to be the output of the controlled system and the electrical signal applied to the three main parts of the heart is also considered as the input vector. Hence the controller is designed using the MIMO sliding mode method. The control signals applied to the three points of the heart are determined in such a way that the electrocardiogram signal behaves desirable. An observer is also used to estimate unmeasurable state variables and uncertain functions of the heart. Continuous approximation method has been used to produce smooth control signals and remove chattering. The simulation results show the good performance of the proposed control system to control the heart rate behavior of a person with tachycardia disease. تفاصيل المقالة
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  2 - Real-Time CANFIS and ANFIS Based Pacemaker Controller Design and Analysis
  Asghar Dabiri Aghdam Nader Dabanloo Fereidoun Nooshiravan Keivan Maghooli
  This paper describes ANFIS introduced by R. Jang et al. ANFIS actually is an offline method in fuzzy control systems. First, a fuzzy file called FIS (Fuzzy Inference System) is designed that relates the input and output of the system by membership functions that are opt أکثر

  This paper describes ANFIS introduced by R. Jang et al. ANFIS actually is an offline method in fuzzy control systems. First, a fuzzy file called FIS (Fuzzy Inference System) is designed that relates the input and output of the system by membership functions that are optimized during the learning process. Input and output learning data are given to the ANFIS (MATLAB command line or ANFSI utility) and the output file is used to test or predict new input data. We can then construct a SIMULINK file to simulate the control system. This simulation is not real-time and if the environmental or input conditions are changed, the output will be altered because the FIS file is fixed and not adapted to input variations. The library of online ANFIS and CANFIS introduced does not have that problem and easily learns the online training data and then can mitigate the output in real-time. To avoid the unsuitable patient data itself as training data, we should use a healthy person ECG (heart rate) data in memory to train our fuzzy system and then switch the input data from healthy data to the patient original heart rate as input data. If the heartbeat falls below (60 bpm that is called Bradycardia) or exceeds (100bpm that is called Tachycardia) from a predetermined value. The online controller will switch the controller to healthy data and will stimulate heart muscles at a right beat rate (70-75 bpm). To distinguish tachycardia from body natural states like running, practicing, walking, sleeping and resting, MEMS accelerometer and in some situations, gyros are used. The Bode diagram stability shows gain and phase margin as follows: GM (dB)= 42.1 and PM (deg) = 100. FIS file is saved after an acceptable rms error (0.38). The simulation results of unity step input response (Rise time, settling time, overshoot) will be demonstrated in chapter 4. The overshoot was less than 2 percent and rise time of 2 seconds with settling time of less than 2 seconds. The parameters have been shown for 60 and 72 and 85 bpm. تفاصيل المقالة