Ali Ghaffari, Mohammad Reza Homaeinezhad, Yashar Ahmadi, Mostafa Rahnavard,
Volume 5, Issue 2 (6-2009)
Abstract
In this study, a mathematical model is developed based on algebraic equations which is capable of generating artificially normal events of electrocardiogram (ECG) signals such as P-wave, QRS complex, and T-wave. This model can also be implemented for the simulation of abnormal phenomena of electrocardiographic signals such as ST-segment episodes (i.e. depression, elevation, and sloped ascending or descending) and repolarization abnormalities such as T-Wave Alternans (TWA). Event parameters such as amplitude, duration, and incidence time in the conventional ECG leads can be a good reflective of heart electrical activity in specific directions. The presented model can also be used for the simulation of ECG signals on torso plane or limb leads. To meet this end, the amplitude of events in each of the 15-lead ECG waveforms of 80 normal subjects at MIT-BIH Database (www.physionet.org) are derived and recorded. Various statistical analyses such as amplitude mean value, variance and confidence intervals calculations, Anderson-Darling normality test, and Bayesian estimation of events amplitude are then conducted. Heart Rate Variability (HRV) model has also been incorporated to this model with HF/LF and VLF/LF waves power ratios. Eventually, in order to demonstrate the suitable flexibility of the presented model in simulation of ECG signals, fascicular ventricular tachycardia (left septal ventricular tachycardia), rate dependent conduction block (Aberration), and acute Q-wave infarctions of inferior and anterior-lateral walls are finally simulated. The open-source simulation code of above abnormalities will be freely available.
A. Jelodar, M. Soleimani, S. H. Sedighy,
Volume 16, Issue 2 (6-2020)
Abstract
A new four elements compact antenna array is presented and discussed to achieve enhanced phase resolution without sacrificing the array output power. This structure inspired by the Ormia Ochracea’s coupled ears. The analogy between this insect acute directional hearing capabilities and the electrically compact antenna array is used to enhance the array sensitivity to direction of arrival estimation of an electromagnetic wave. This four elements biomimetic compact array is composed of four strongly coupled antenna elements and two external coupling networks which are designed to enhance the phase resolutions between all antenna element outputs without decrease in the array output power. In other words, this four elements compact array extracts the same power level from the incident EM wave compared with regular array, while the output phase sensitivity is significantly enhanced. The simulation results confirm the advantages of this new compact array compared with the previously reported ones in the literature.
Z. Najafniya, Gh. Karimi, Mahnaz Ranjbar,
Volume 17, Issue 3 (9-2021)
Abstract
Neural synchronization is considered as a key role in several neurological diseases, such as Parkinson’s and Epilepsy’s disease. During these diseases, there is increased synchronization of massive numbers of neurons. In addition, evidences show that astrocytes modulate the synaptic interactions of the neuronal population. The Astrocyte is an important part of a neural network that can be involved in the desynchronization of the neuronal population. In this paper, we design a new analog neuromorphic circuit to implement the effect of astrocyte in the desynchronization of neural networks. The simulation results demonstrate that the astrocyte circuit as a feedback path can be desynchronized to a synchronized neural population. In this circuit, as a first step, the population of twenty neurons is synchronized with the same input currents. Next, by involving an astrocyte feedback circuit, the synchronization of the neural network is disturbed. Then, the neuronal population will be desynchronized. The proposed circuit is designed and simulated using HSPICE simulator in 0.35 μm standard CMOS technology.
Elahe Moradi,
Volume 20, Issue 4 (11-2024)
Abstract
With the intricate interplay between clinical and pathological data in coronary heart disease (CHD) diagnosis, there is a growing interest among researchers and healthcare providers in developing more accurate and reliable predictive methods. In this paper, we propose a new method entitled the robust artificial neural network classifier (RANNC) technique for the prediction of CHD. The dataset CHD in this paper has imbalanced data, and in addition, it has some outlier values. The dataset consists of information related to 4240 samples with 16 attributes. Due to the presence of outliers, a robust method has been used to scale the dataset. On the other hand, due to the imbalance of CHD data, three data balancing methods, including Random Over Sampling (ROS), Synthetic Minority Over Sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN) approaches, have been applied to the CHD data set. Also, six artificial intelligence algorithms, including LRC, DTC, RFC, KNNC, SVC, and ANN, have been evaluated on the considered dataset with criteria such as precision, accuracy, recall, F1-score, and MCC. The RANNC, leveraging ADASYN to address data imbalance and outliers, significantly improved CHD diagnostic accuracy and the reliability of healthcare predictive models. It outperformed other artificial intelligence methods, achieving precision, accuracy, recall, F1-score, and MCC scores of 95.57%, 96.90%, 99.70%, 97.59%, and 93.42%, respectively.
