Mortality Prediction of ICU Cardiovascular Patient: Time-Series Analysis

It is estimated that millions of deaths occur annually, which can be prevented when early diagnosis and correct treatment are provided in the intensive care unit (ICU). In addition to monitoring and treating patients, the physician of the ICU has the task of predicting the outcome of patients and identifying them. They are also responsible for the separation of patients who use special ICUs. Because not necessarily all patients hospitalized in ICU benefit from this unit, and hospitalization in a few cases will only lead to an easier death. Therefore, developing an intelligent method that can help doctors predict the condition of patients in the ICU is very useful. This paper aims to predict the mortality of cardiovascular patients hospitalized in the ICU using cardiac signals. In the proposed method, the condition of patients is predicted 30 minutes before death using various features extracted from the electrocardiogram (ECG) and heart rate variability (HRV) signals and intelligent methods. The paper's results showed that combining morphological, linear, and nonlinear features can predict the mortality of patients with accuracy and sensitivity of 96.7±6.7% and 94.1±5.8%, respectively. As a result, accurate classification of diseases and correct prediction of patients by reducing unnecessary monitoring can help optimize ICU beds' use. According to new and advanced techniques and technologies, it is possible to predict and treat many diseases in ICU, leading to longer patient survival

Combining mathematical model for HRV mapping and machine learning to predict sudden cardiac death

Sudden cardiac death, a prominent cause of mortality, often occurs within a narrow time window of less than an hour. This study introduces a novel methodology with the aim of early prediction of sudden cardiac death. The proposed approach involves the extraction of diverse features from the ECG signal, including the calculation of angles between two vectors, the computation of triangle areas formed by consecutive points, the determination of the shortest distance to a 450 line, and their combinations. Additionally, a thresholding technique is proposed to identify the risk period and predict the occurrence of sudden death. To assess the performance of the algorithm, data from the MIT-BH Holter database were utilized. The results obtained demonstrate that the angle feature achieves an average sensitivity of 93.75% with five false alarms, the area feature achieves an average sensitivity of 88.75% with nine false alarms, the shortest distance feature achieves an average sensitivity of 86.25% with 12 false alarms, and the combined feature achieves an average sensitivity of 96.25% with three false alarms. Remarkably, unlike existing methodologies in the literature, this method exhibits high accuracy in predicting the development of the risk of sudden cardiac death (SCD) even up to 30 min prior to onset. As a consequence, it plays a critical role in diagnosing patients' conditions and facilitating timely interventions. Moreover, the results confirm the feasibility of predicting cardiac arrest solely based on geometric features derived from variations in heart rate variability (HRV) dynamics

A Review of the Methods for Sudden Cardiac Death Detection: A Guide for Emergency Physicians

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.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 sampling frequency of 256 samples per second are reviewed. Both types of data have normal and abnormal sampling 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.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.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

Classification of Breast Cancer Tumors Using Mammography Images Processing Based on Machine Learning: Breast Cancer Tumors Using Mammography Images

  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

Presenting an efficient approach based on novel mapping for mortality prediction in intensive care unit cardiovascular patients

Intensive care unit (ICU) experienced and skillful people in this field should be employed because the equipment, facilities, and admitted patients have more special conditions than other departments. Our goal provides the best quality according to the condition each patient and prevent many unnecessary costs for preventive treatment.In this paper, the proposed system will first receive the patient's vital signs, which are recorded by the ICU monitoring. After the necessary processing, in case of observing changes in the normal state, risk alarms are transmitted to the nursing station so that nurses become aware of this condition and take all equipment to return the patient to normal condition and prevent his death. The applied graph in this study examines patients at any moment and displays the patient's future condition in a schematic manner after precise analyses. In this algorithm, after calculating the R-R intervals in the electrocardiogram signal, RRIs are thrown into a risk plot (RP) by a projectile. Given the amount of projectile RRI, one of the stairs can host that amount. After a few moments by springs embedded under the stairs, the drain of RRIs is done by the kinetic energy stored in the springs towards the valley of life. If the accumulation of quantities in a stair is too much, the spring will not be able to project those RRIs. By examining this situation, we will introduce an index to determine the risk of death for all patients.The results of this paper show that when a person is in normal condition, there is no density in a certain stair and the ball or the projected RRIs are not limited to a stair. In general, the results of this paper show that the lower amount of RRI dispersion in the RP leads to greater risk of entry into the death range and as this amount decrease, an immediate consideration is required.In conclusion, if the precise prediction of the future condition of ICU patients is available to nurses and doctors, more facilities and equipment could be provided to save their lives. • We focused on nonlinear methods with new aspects to extract mentioned dynamics. • This method can reduce the number of ICU nurses and give the special facilities for high-risk patients. • Our results confirm that it is possible to predict mortality based on the dynamical characteristics of HRV. Keywords: Mortality prediction, ICU, Heart rate variability, Risk plot, Method name: Nonlinear Mappin

Analysis of Heart Rate Dynamics Before and During Meditation

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

Human-like evaluation by facial attractiveness intelligent machine

Facial attractiveness is an important factor in social interactions and has been widely studied in psychology and neuroscience. This paper presents a novel approach to the problem of predicting facial attractiveness using machine learning and computer vision techniques. Our main objective is to investigate whether an intelligent machine can learn and accurately predict facial attractiveness based on objective rules in facial features.To achieve this, we collected datasets of facial images and corresponding attractiveness rankings for women. We then utilized various machine learning methods, including k-nearest neighbors (KNN) and support vector regression (SVR), to train a predictor model that learned from these datasets to provide a human-like assessment of facial attractiveness. The model used facial feature parameters, such as symmetry and proportion, as input to determine the attractiveness ranking as output.We evaluated the performance of our trained predictor model using several metrics, including the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). The best performance was achieved using the KNN algorithm during the testing phase, with R2=0.9902, RMSE=0.0056, and MAPE=0.0856. It indicated a significant improvement in the accuracy of facial attractiveness prediction compared to previous studies.Our results demonstrate that an intelligent machine can learn and predict facial attractiveness based on objective rules in facial features, providing a promising approach for ranking facial attractiveness. In comparison to previous studies in this area, our approach shows significant improvement in accuracy, with a correlation coefficient higher than that of human ratings. This work has significant implications for the fields of psychology, neuroscience, and computer science, as it provides a new perspective on the concept of facial attractiveness and its quantification using machine learning

Intelligent Diagnosis of Actinic Keratosis and Squamous Cell Carcinoma of the Skin, Using Linear and Nonlinear Features Based on Image Processing Techniques

Introduction: Most skin cancers are treatable in the early stages; thus, an early and rapid diagnosis can be very important to save patients’ lives. Today, with artificial intelligence, early detection of cancer in the initial stages is possible. Method: In this descriptive-analytical study, a computerized diagnostic system based on image processing techniques was presented, which is much more helpful for the patient. In this method, dermoscopic images of actinic keratosis and squamous cell carcinoma were improved by preprocessing techniques and the potential noises were removed. Then, segmentation was performed using the thresholding method to separate the lesion from the underlying skin. Thereafter, from the segmented area, texture, shape, and color information and features were extracted. Finally, the feature reduction method and support vector machine (SVM) were used to evaluate the proposed method qualitatively and quantitatively. Results: The data in this study included 100 samples of actinic keratosis images and 100 samples of squamous cell carcinoma. The results of the present study showed that using the genetic algorithm method together with the support vector machine method could help identify the type of skin cancer with 99.7 ± 0.4% accuracy. Conclusion: The effect of different tissue features in diagnosing the type of lesion showed an increase in the amount and variety of features extracted from the samples would lead to better training and more accurate analysis of the system

A predictive model of death from cerebrovascular diseases in intensive care units

Objective: This study aimed to explore the mortality prediction of patients with cerebrovascular diseases in the intensive care unit (ICU) by examining the important signals during different periods of admission in the ICU, which is considered one of the new topics in the medical field. Several approaches have been proposed for prediction in this area. Each of these methods has been able to predict mortality somewhat, but many of these techniques require recording a large amount of data from the patients, where recording all data is not possible in most cases; at the same time, this study focused only on heart rate variability (HRV) and systolic and diastolic blood pressure. Methods: The ICU data used for the challenge were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) Clinical Database. The proposed algorithm was evaluated using data from 88 cerebrovascular ICU patients, 48 men and 40 women, during their first 48 hours of ICU stay. The electrocardiogram (ECG) signals are related to lead II, and the sampling frequency is 125 Hz. The time of admission and time of death are labeled in all data. In this study, the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of HRV and blood pressure. To predict the patient's future condition, the combination of features extracted from the return mapping generated by the HRV signal, such as angle (α), area (A), and various parameters generated by systolic and diastolic blood pressure, including DBPMax−Min SBPSD have been used. Also, to select the best feature combination, the genetic algorithm (GA) and mutual information (MI) methods were used. Paired sample t-test statistical analysis was used to compare the results of two episodes (death and non-death episodes). The P-value for detecting the significance level was considered less than 0.005. Results: The results indicate that the new approach presented in this paper can be compared with other methods or leads to better results. The best combination of features based on GA to achieve maximum predictive accuracy was m (mean), LMean, A, SBPSVMax, DBPMax-Min. The accuracy, specificity, and sensitivity based on the best features obtained from GA were 97.7%, 98.9%, and 95.4% for cerebral ischemia disease with a prediction horizon of 0.5–1 hour before death. The d-factor for the best feature combination based on the GA model is less than 1 (d-factor = 0.95). Also, the bracketed by 95 percent prediction uncertainty (95PPU) (%) was obtained at 98.6. Conclusion: The combination of HRV and blood pressure signals might increase the accuracy of the prediction of the death episode and reduce the minimum hospitalization time of the patient with cerebrovascular diseases to determine the future status