ORCID Profile
0000-0003-0793-3797
Current Organisation
UNSW Sydney
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Publisher: Salvia Medical Sciences Ltd
Date: 02-2022
Publisher: Informa UK Limited
Date: 21-01-2022
DOI: 10.1080/03091902.2022.2026504
Abstract: The purpose of this article is to diagnose respiratory apnoea in order to help the person avoid further possible risks. In this article, the ECG signal of 70 patients with sleep apnoea in the Physionet database with a s ling rate of 100 Hz is used. Data recording time is 7 to 10 h, the age range is 27 to 60 years, and weighs between 53 to 135 kg. In this article, using electrocardiogram signal processing, the time of occurrence of a respiratory attack on the patient during sleep is predicted. In order to achieve this goal, after generating the HRV signal from the ECG, time and frequency domain properties are extracted from the HRV signal. In the next step, according to statistical analysis, principal component analysis algorithm, and genetic algorithm, the best combination of features is selected in terms of differentiation between two different groups. In order to evaluate the capability of each feature in distinguishing between two attack and non-attack event intervals, the features are compared separately and in combination. The results show that in the HRV signal of people at risk for sleep apnoea, there are features in the vicinity of the attack that distinguish them from times far away from the attack. It was also shown that the feature combination method has a much greater ability to reveal this difference. The results of specificity, sensitivity, and accuracy obtained by combining the features were 99.77%, 97.38%, and 98.25%, respectively, which has a much higher performance than previous studies. Early detection enables the physician and the intensive care unit to take steps to prevent this from happening, which will save the patient's life.
Publisher: Walter de Gruyter GmbH
Date: 2019
Abstract: The purpose of this paper is to identify differences between abnormal and normal lung signals gathered by an EIT device, which is a new, non-invasive system that seeks the electrical conductivity and permittivity inside a body. Lung performances in patients are investigated using Phase Space Mapping technique on Electrical EIT signals. The database used in this paper contains 82 registered records of 52 in iduals with proper lung volume. The results of this paper show that as the delay parameter (τ) increases, the SD1 parameter of phase space mapping indicates a significant difference between normal and abnormal lung volumes. The value of the SD1 parameter with τ = 6 in the case that the lung volume is in a normal condition is 342.57 ± 32.75 while it is 156.71 ± 26.01 in non-optimal mode. This method can be used to identify the patients’ lung volumes with chronic respiratory illnesses and is an accurate assessment of the erse methods to treat respiratory system illnesses in addition to saving various therapeutic costs and dangerous consequences that are likely to occur by using improper treatment methods. It can also reduce the required treatment durations.
Publisher: Elsevier BV
Date: 2015
Publisher: Scientific and Academic Publishing
Date: 31-08-2012
Publisher: Universal Wiser Publisher Pte. Ltd
Date: 20-07-2021
DOI: 10.37256/AIE.222021
Publisher: AEPress, s.r.o.
Date: 2015
DOI: 10.4149/BLL_2015_081
Abstract: Obstructive sleep apnea (OSA) is a risk factor for hypertension, has effects on cardiovascular system and increases the sympathetic activity. The aim of the study was to evaluate the effectiveness of the non-linear Poincaré plot analysis to predict OSA based on polysomnography (PSG). The database of this study was collected by the sleep laboratory at the Philipps University in Marburg, Germany. It includes 24 PSG of men and women between 27-63 years old with obstructive and mixed sleep apnea. The start and end of apnea events in PSGs were marked. The Poincaré plots of pre-apneic phase including 4-1 minutes before apnea were evaluated. Wilcoxon test was used for statistical analysis. Poincaré analysis showed that the dynamics of chest and respiratory efforts changed two minutes before the apnea and SD1/SD2 ratios of these parameters significantly increased in the pre-apneic phase (p≤0.01). The SD1/SD2 ratio of nasal airflow did not show significant difference even in episodes close to apnea. Our results suggest that Poincaré plot parameters of PSG have the potential to be considered predictors of apnea with the ability to show the dynamic of changes, which could lead to pre-diagnosis or prediction of apnea about 2-3 minutes before its occurrence (Tab. 2, Fig. 4, Ref. 23).
Publisher: Elsevier BV
Date: 06-2023
Publisher: International Information and Engineering Technology Association
Date: 31-08-2021
DOI: 10.18280/TS.380410
Abstract: This paper aims to explore the essence of facial attractiveness from the viewpoint of geometric features toward the classification and identification of attractive and unattractive in iduals. We present a simple but useful feature extraction for facial beauty classification. Evaluation of facial attractiveness was performed with different combinations of geometric facial features using the deep learning method. In this method, we focus on the geometry of a face and use actual faces for our analysis. The proposed method has been tested on, image database containing 60 images of men's faces (attractive or unattractive) ranging from 20-50 years old. The images are taken from both frontal and lateral position. In the next step, principle components analysis (PCA) was applied to feature a reduction of beauty, and finally, the neural network was used for judging whether the obtained analysis of various faces is attractive or not. The results show that one of the indexes in identifying facial attractiveness base of science, is the values of the geometric features in the face, changing facial parameters can change the face from unattractive to attractive and vice versa. The experimental results are based on 60 facial images, high accuracy of 88%, and Sensitivity of 92% is obtained for 2-level classification (attractive or not).
Publisher: Springer Singapore
Date: 2022
Publisher: AEPress, s.r.o.
Date: 2020
DOI: 10.4149/BLL_2020_107
Publisher: Elsevier BV
Date: 2023
Publisher: Scientific Research Publishing, Inc.
Date: 2009
Publisher: Institute of Advanced Engineering and Science
Date: 12-2020
DOI: 10.11591/IJAAS.V9.I4.PP326-332
Abstract: span Face Detection plays a crucial role in identifying in iduals and criminals in Security, surveillance, and footwork control systems. Face Recognition in the human is superb, and pictures can be easily identified even after years of separation. These abilities also apply to changes in a facial expression such as age, glasses, beard, or little change in the face. This method is based on 150 three-dimensional images using the Bosphorus database of a high range laser scanner in a Bogaziçi University in Turkey. This paper presents powerful processing for face recognition based on a combination of the salient information and features of the face, such as eyes and nose, for the detection of three-dimensional figures identified through analysis of surface curvature. The Trinity of the nose and two eyes were selected for applying principal component analysis algorithm and support vector machine to revealing and classification the difference between face and non-face. The results with different facial expressions and extracted from different angles have indicated the efficiency of our powerful processing. /span
Publisher: Informa UK Limited
Date: 30-03-2016
DOI: 10.3109/03091902.2016.1139201
Abstract: Intensive care unit (ICU) patients are at risk of in-ICU morbidities and mortality, making specific systems for identifying at-risk patients a necessity for improving clinical care. This study presents a new method for predicting in-hospital mortality using heart rate variability (HRV) collected from the times of a patient's ICU stay. In this paper, a HRV time series processing based method is proposed for mortality prediction of ICU cardiovascular patients. HRV signals were obtained measuring R-R time intervals. A novel method, named return map, is then developed that reveals useful information from the HRV time series. This study also proposed several features that can be extracted from the return map, including the angle between two vectors, the area of triangles formed by successive points, shortest distance to 45° line and their various combinations. Finally, a thresholding technique is proposed to extract the risk period and to predict mortality. The data used to evaluate the proposed algorithm obtained from 80 cardiovascular ICU patients, from the first 48 h of the first ICU stay of 40 males and 40 females. This study showed that the angle feature has on average a sensitivity of 87.5% (with 12 false alarms), the area feature has on average a sensitivity of 89.58% (with 10 false alarms), the shortest distance feature has on average a sensitivity of 85.42% (with 14 false alarms) and, finally, the combined feature has on average a sensitivity of 92.71% (with seven false alarms). The results showed that the last half an hour before the patient's death is very informative for diagnosing the patient's condition and to save his/her life. These results confirm that it is possible to predict mortality based on the features introduced in this paper, relying on the variations of the HRV dynamic characteristics.
Publisher: Elsevier BV
Date: 08-2018
Publisher: IEEE
Date: 19-05-2021
Publisher: Elsevier BV
Date: 02-2023
Publisher: Informa UK Limited
Date: 02-04-2020
Publisher: International Association of Online Engineering (IAOE)
Date: 12-04-2022
DOI: 10.3991/IJOE.V18I05.29197
Abstract: Abstract— Using intelligent methods to identify and classify a variety of diseases, in particular cancer, has gained tremendous attention today. Tumor classification plays an important role in medical diagnosis. This study's goal was to classify breast cancer (BC) tumors using software-based numerical techniques. To determine whether breast cancer masses are benign or malignant, we used MATLAB version 2020b to build a neural network. In the first step, the features of the training images and their output classes were used to train the network. Optimal weights were obtained after several repetitions, and the network was trained to produce the best result in the test phase after several repetitions. Because of using effective and accurate features, the method suggested here, which was based on an artificial neural network, delivered the diagnostic accuracy, specificity, and sensitivity of 100%, 100%, and 100%, respectively, to discern benign from malignant BC tumors, showing a better performance compared to previously proposed methods. One of the challenges for imaging-based diagnostic techniques in medicine is the difficulty of processing dense tissues. Breast cancer is one of the most common progressive diseases among females. Early diagnosis makes treatment easier and more effective. Using AI-based methods for automated diagnosis purposes can be valuable and have a reduced error rate because accurate diagnosis by manual means is time-consuming and error-prone.
Publisher: Informa UK Limited
Date: 05-10-2023
Publisher: Scientific Research Publishing, Inc.
Date: 2009
Publisher: International Association of Online Engineering (IAOE)
Date: 13-08-2020
DOI: 10.3991/IJOE.V16I09.14485
Abstract: class="Abstract" Sudden cardiac death (SCD) is an unexpected death of a person with or without knowing cardiac causes are often occurring in less than an hour after the incidence of symptoms. In the case of physicians' knowledge of this incident, they can make appropriate decisions for the patients at-risk and reduce the number of such deaths significantly. The purpose of this paper is to examine different methods for predicting sudden cardiac death using electrocardiogram (ECG) signal from 1998 to recent years that can contribute to researchers to become familiar with the wide range of research conducted in this field. class="Abstract" In this paper, studies using various methods to predict sudden cardiac death that has applied the data from the Physionet and MIT-BIH databases with a s ling frequency of 256 s les per second are reviewed. Both types of data have normal and abnormal s ling labels and the data recording time varies from a few seconds to minutes. In the field of SCD prediction, various studies have addressed the processing of the electrocardiogram (ECG) signal as well as the heart rate variability (HRV) signal in different domains, including time, time-frequency, and nonlinear domain. In time-domain processing the statistical characteristics of time signal such as the mean and standard deviation of heart rate, the mean and standard deviation of RR intervals, and Root Mean Square of the Successive Differences (RSSD) are used. Also, in the frequency domain, the power spectral density (PSD) of the signal energy is used in a very-low-frequency band, low-frequency band and, high-frequency band. Similarly, in the nonlinear domain, features such as Poincare plot, detrended fluctuation analysis (DFA), common entropy, wavelet transform coefficients (WTC), and features of the recursive graph including Lmax, Lmean, correlation dimension (CD), etc. are used. In all of the proposed algorithms so far, researchers have been trying to inform the sudden death alarm in a larger interval than the time of death by separating the signals into different time periods and extracting various features. class="Abstract" To evaluate the results of the proposed methods, each of the researchers analyzed the a-few-minute intervals before the SCD. Different classification methods are available to identify the efficiency of the proposed algorithm, such as support vector machine (SVM), multilayer perceptron neural network (MLP), radial base function neural network (RBF), k-nearest neighbor (KNN) and mixture expert (ME). The use of features introduced in different domains and different classifiers has led to the observation of different horizons of prediction in various studies. The results of these predictions are often free from the interpretations of clinical symptoms, and their maximum presented time with acceptable validity eventually reaches 4 minutes before the event, which is not an acceptable time for people who have attacked outside the hospital. Accordingly, the most prominent of these evaluations is the mixture expert methodology in which the best feature extraction methods are used in a new method for selecting the optimal feature space locally. This method makes it possible to select different features every minute before the event by choosing the optimal features for each one-minute interval of the signal as an episode which increases the prediction time from 4 minutes before the death to 12 minutes and allows the interpretation of clinical symptoms in terms of multiplication of the presence of the features per minute. class="Abstract" Given the non-linear nature of the HRV signal and the similarity of the ECG signal in many time intervals, the use of the HRV signal has become more popular among scholars. The analysis of various studies shows that by approaching the time of death, linear features (time and frequency) can be predictive of death according to the sensible behavior and variations in patients’ signal. Instead of moving away from the death interval, the use of chaotic and non-linear features is more effective. Therefore, a more precise selection of features in this area can be useful for increasing the horizon of prediction of death.
Publisher: Elsevier BV
Date: 2018
Publisher: Horizon Research Publishing Co., Ltd.
Date: 09-2013
Publisher: Elsevier BV
Date: 12-2022
Publisher: Wiley
Date: 27-03-2023
DOI: 10.1002/HBM.26282
Abstract: Approximately 2%–3% of the world population suffers from obsessive–compulsive disorder (OCD). Several brain regions have been involved in the pathophysiology of OCD, but brain volumes in OCD may vary depending on specific OCD symptom dimensions. The study aims to explore how white matter structure changes in particular OCD symptom dimensions. Prior studies attempt to find the correlation between Y‐BOCS scores and OCD patients. However, in this study, we separated the contamination subgroup in OCD and compared directly to healthy control to find regions that exactly related to contamination symptoms. To evaluate structural alterations, diffusion tensor imaging was acquired from 30 OCD patients and 34 demographically matched healthy controls. Data were processed using tract‐based spatial statistics (TBSS) analysis. First, by comparing all OCD to healthy controls, significant fractional anisotropy (FA) decreased in the right anterior thalamic radiation, right corticospinal tract, and forceps minor observed. Then by comparing the contamination subgroup to healthy control, FA decreases in the forceps minor region. Consequently, forceps minor plays a central role in the pathophysiology of contamination behaviors. Finally, other subgroups were compared to healthy control and discovered that FA in the right corticospinal tract and right anterior thalamic radiation is reduced.
Publisher: Universal Wiser Publisher Pte. Ltd
Date: 05-11-2021
Abstract: About one percent of the world's population suffers from epilepsy. A patient with epilepsy must be diagnosed early and accurately if they are to have any chance of being treated successfully. One method for diagnosing epilepsy is by carefully analyzing the Electroencephalogram (EEG) signal. We propose a method for signal processing EEG signals that detect epilepsy based on time-frequency features extracted from the signal and used as input for a neural network classifier. With the help of a convolutional neural network with deep learning, better and more efficient features were obtained and an accurate diagnosis was provided. It resulted in a significant difference between the two in iduals upon analysis of the EEG signal. As compared with the previous method, the proposed technique distinguishes between healthy and epileptic signals with specificity 98 ± 2%, sensitivity 99 ± 0.7%, accuracy 98 ± 0.6%, and F-score 98 ± 0.5%. It is possible to use EEG signal analysis to detect the onset of seizures, especially in infants, as an effective tool to diagnose cases of suspected clinical signs of seizure onset.
Publisher: IEEE
Date: 02-2019
Publisher: IEEE
Date: 11-2010
Publisher: AEPress, s.r.o.
Date: 2023
DOI: 10.4149/BLL_2023_037
Publisher: Elsevier BV
Date: 12-2019
Publisher: BiomedGrid LLC
Date: 16-03-2021
Publisher: International Association of Online Engineering (IAOE)
Date: 20-05-2021
DOI: 10.3991/IJOE.V17I05.11133
Abstract: Heart rate is one of the most important vital signs. People usually face high tension in routine life, and if we found an effective method to control the heart rate, it would be very desirable. One of the goals of this paper is to examine changes in heart rate before and during meditation. Another goal is that what impact could have meditation on the human heartbeat. To heart rate analysis before and during meditation, available heart rate signals have been used for the Physionet database that contains 10 normal subjects and 8 subjects that meditation practice has been done on them. In this paper, first is paid to extract linear and nonlinear characteristics of heart rate and then is paid to the best combination of features to identify two intervals before and during meditation using MLP and SVM classifiers with the help of sensitivity, specificity and accuracy measurements. The achieved results in this paper showed that choosing the best combination of a feature to make a meaningful difference between two intervals before and during meditation includes two-time features (Mean HR, SDNN), a frequency feature ( ), and three nonlinear characteristics ( ). Also, using the support vector machine had better results than the MLP neural network. The sensitivity, specificity, and accuracy of the mean and standard deviation obtained respectively like 92.73 0.23, 89.05 0.67, 89.97 0.23 by using MLP and respectively like 95.96 0.09, 93.80 0.16, and 94.90 0.14 by using SVM. As a result, using meditation can reduce the stress and anxiety of patients by effects on heart rate, and the treatment process speeds up and have an important role in improving the performance of the system.
Publisher: Research Square Platform LLC
Date: 03-01-2023
DOI: 10.21203/RS.3.RS-2427034/V1
Abstract: One of the important units of the hospital is the intensive care unit, which provides specialized services to patients with acute conditions. In this paper, we introduce an instrument that can improve ICU services to patients, differentiate critical patients, optimize ICU beds, and examine the severity of their disease. To predict the mortality rate, Physiont's database for computing in cardiology challenge (CinC) was used. The survival rate of patients hospitalized in the ICU was 85.33% out of 300. Among the patients, the acute physiology and chronic health evaluation (APACHE) IV scores ranged from 18 to 90, averaging 57.43±18.6. Using the APACHE IV tool, the predicted mortality rate was 24.19%, and the observed mortality rate was 35.67%. Calculations indicated that the average score of APACHE IV for the survivors was 46.84, and for the non-survivors, it was 69.35. As a result, APACHE IV proved to be a valuable tool for nurses and doctors to predict the future conditions of patients and how they will respond to the treatment process.
Publisher: AEPress, s.r.o.
Date: 2023
DOI: 10.4149/BLL_2023_070
Publisher: AEPress, s.r.o.
Date: 2022
DOI: 10.4149/BLL_2022_064
Publisher: Springer Science and Business Media LLC
Date: 07-01-2021
Publisher: Research Square Platform LLC
Date: 16-11-2020
DOI: 10.21203/RS.3.RS-108217/V1
Abstract: Background: This article aimed to explore the mortality prediction of cerebrovascular patients in the intensive care unit (ICU) by examining the important signals associated with these patients during different periods of admission in the intensive care unit, which is considered as one of the new topics in the medical field. Several approaches have been proposed for prediction in this area that each of these methods has been able to predict the mortality somewhat, but many of these techniques require the recording of a large amount of data from the patients, where the recording of all data is not possible in most cases while this article focuses only on the heart rate variability (HRV) and systolic and diastolic blood pressure. Methods: In this paper, using the information obtained from the electrocardiogram (ECG) signal and blood pressure with the help of vital signal processing methods, how to change these signals during the patient's hospitalization will be initially checked. Then, the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of heart rate variability and blood pressure. To implement this paper, 80 recorded data from cerebral ischemic patients admitted to the intensive care unit, including ECG signal recording, systolic and diastolic blood pressure, and other physiological parameters are collected. Time of admission and time of death are labeled in all data. Results: The results indicate that the use of the new approach presented in this article can be compared with other methods or leads to better results. The accuracy, specificity, and sensitivity based on the novel features were, respectively, 97.7, 98.9, and 95.4 for cerebral ischemia disease with a prediction horizon of 0.5-1 hours before death. Conclusion: The perspective of the prediction horizons and the patients' length of stay with a new approach was taken into account in this article. The higher the prediction horizon, the nurses or associates of patients have more time to carry out therapeutic measures. To determine the patient's future status and analysis of the ECG signal and blood pressure, at least 7.8 hours of hospitalization is required, which has had a significant reduction compared with other methods.
Publisher: Zarqa University
Date: 2022
Abstract: In addition to the devastating effects of anxiety and stress on the development and exacerbation of the cardiovascular disease, lack of stress control increases drivers' risk of accidents. This paper aims to identify the stress of drivers in various driving situations to warn the driver to control the tense conditions during driving. In order to detect stress while driving, we used the heart signals in the Physionet database. To analyze the conditions of the electrocardiogram (ECG) under various driving situations, linear and non-linear features were used. The characteristics of the RRIs are the only able to identify driver stress in different driving modes relative to rest periods, while the return mapping features, in addition to identifying driver stress while resting, have the ability to identify stress between different driving positions also brought. The results showed that driver's stress level during driving in city 1 and highway 1 with a P-value of 0.028 and also in city 3 and highway 2 with a P-value of 0.041 can be distinguished. The accuracy obtained from the proposed detection method is 98±2% for 100 iterations. The result indicated an efficiency of our proposed method and enhanced the reliability.
Publisher: AEPress, s.r.o.
Date: 2017
DOI: 10.4149/BLL_2017_001
Publisher: World Scientific Pub Co Pte Lt
Date: 05-2019
DOI: 10.1142/S0219519419500027
Abstract: Electrocardiogram (ECG) signals containing very important information about the cardiac are one of the most common tools for physicians in the diagnosis of various types of cardiac diseases. Low accuracy in positioning, limitation of time accuracy, the similarity of signals between some diseases and normal signals and probability of missing some aspect of data are the defect aspects of this method. Importance of cardiac signals and defects of current methods in diagnosis show the need of substituting a new method to show the activity of cardiac. One of the most dangerous defections is ischemia, which corrects and on time diagnose could avoid the latter effect of it. Each of common methods for diagnosis of this illness has their own advantages and disadvantages. In this paper, we consider describing a non-invasive method for ischemic episode detection based on mapping of ECG signals. With this method, we can present the signals with virtual colors and facilitate the diagnosis of ischemic disease. So, a new method of 12-lead cardiac presentation is described that in fact present the 12-lead signals in two images. The result of this paper will present the indicators of sensitivity, specificity and accuracy in the context of disease diagnosis. This paper proposed a novel ECG imaging algorithm for classifying the normal and ischemic signals and 95.35% specificity, 96.79% sensitivity and 95.76% accuracy were achieved which are very much promising compared to the other methods and doctor’s accuracy.
Publisher: CMV Verlag
Date: 06-2021
Location: Iran (Islamic Republic of)
Location: Iran (Islamic Republic of)
No related grants have been discovered for Dr Mohammad Karimi Moridani.