Vikas Patidar - Academia.edu (original) (raw)
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Islamic Azad University, Science and Research Branch
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Papers by Vikas Patidar
2018 15th IEEE India Council International Conference (INDICON), 2018
This paper presents a classification method for arrhythmia using probabilistic neural network, ba... more This paper presents a classification method for arrhythmia using probabilistic neural network, based on unique combination of three Electrocardiogram (ECG) features; heart rate, Auto Regressive (AR) coefficients & spectral entropy (SE). Heart rate has been very critical parameter for the detection of life-threatening arrhythmia. The purpose of this paper is to develop a Probabilistic Neural Network (PNN) based algorithm for improved detection and broader classification of cardiac arrhythmia. The results show that the unique combination of ECG features considered in this work provides more accurate and robust classification of arrhythmias.
—In this paper, we develop a complete mathematical model of a shape memory alloy (SMA) wire actua... more —In this paper, we develop a complete mathematical model of a shape memory alloy (SMA) wire actuated by an electric current and a bias spring. The operation of the SMA actuator involves different physical phenomena, such as heat transfer, phase transformation with temperature hysteresis, stress–strain variations and electrical resistance variation accompanying the phase transformation. We model each of these phenomena in a modular fashion. A key feature of the proposed model is that one or more of its modules can be extended to fit other SMA applications. At the heart of the proposed model is a differential hysteresis model capable of representing minor hysteresis loops. We generate the temperature profile for the hysteresis model using lumped parameter analysis. We extend the variable sublayer model to represent actuator strain and electrical resistance. This model can be used to develop a position control system for the actuator. Simulation results from the model are found to be in good agreement with experimental data.
The use of multiple sensors can potentially improve the performance of rate-responsive pacemakers... more The use of multiple sensors can potentially improve the performance of rate-responsive pacemakers. The design of algorithms to combine sensor inputs is complicated by the lack of explicit performance criteria. The design requirements often take the form of imprecise verbal statements by physicians, engineers, and marketing experts. A set of expert rules was developed to describe an algorithm combining activity and minute ventilation sensor inputs. The rules were reduced into a fonnal hierarchical rule base. The rule base was translated into a hierarchical fuzzy logic expert system. The antecedents and consequents of the rules were assigned monotonic membership functions. The resulting algorithm met the required end points and interpolated smoothly between them. The results of several tests of the algorithm showed that the rules derived from the stated requirements were not violated.
2018 15th IEEE India Council International Conference (INDICON), 2018
This paper presents a classification method for arrhythmia using probabilistic neural network, ba... more This paper presents a classification method for arrhythmia using probabilistic neural network, based on unique combination of three Electrocardiogram (ECG) features; heart rate, Auto Regressive (AR) coefficients & spectral entropy (SE). Heart rate has been very critical parameter for the detection of life-threatening arrhythmia. The purpose of this paper is to develop a Probabilistic Neural Network (PNN) based algorithm for improved detection and broader classification of cardiac arrhythmia. The results show that the unique combination of ECG features considered in this work provides more accurate and robust classification of arrhythmias.
—In this paper, we develop a complete mathematical model of a shape memory alloy (SMA) wire actua... more —In this paper, we develop a complete mathematical model of a shape memory alloy (SMA) wire actuated by an electric current and a bias spring. The operation of the SMA actuator involves different physical phenomena, such as heat transfer, phase transformation with temperature hysteresis, stress–strain variations and electrical resistance variation accompanying the phase transformation. We model each of these phenomena in a modular fashion. A key feature of the proposed model is that one or more of its modules can be extended to fit other SMA applications. At the heart of the proposed model is a differential hysteresis model capable of representing minor hysteresis loops. We generate the temperature profile for the hysteresis model using lumped parameter analysis. We extend the variable sublayer model to represent actuator strain and electrical resistance. This model can be used to develop a position control system for the actuator. Simulation results from the model are found to be in good agreement with experimental data.
The use of multiple sensors can potentially improve the performance of rate-responsive pacemakers... more The use of multiple sensors can potentially improve the performance of rate-responsive pacemakers. The design of algorithms to combine sensor inputs is complicated by the lack of explicit performance criteria. The design requirements often take the form of imprecise verbal statements by physicians, engineers, and marketing experts. A set of expert rules was developed to describe an algorithm combining activity and minute ventilation sensor inputs. The rules were reduced into a fonnal hierarchical rule base. The rule base was translated into a hierarchical fuzzy logic expert system. The antecedents and consequents of the rules were assigned monotonic membership functions. The resulting algorithm met the required end points and interpolated smoothly between them. The results of several tests of the algorithm showed that the rules derived from the stated requirements were not violated.