Design and Stability Analysis of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Pacemaker Controller in MATLAB Simulink.

We present the design and stability analysis of an adaptive neuro-fuzzy inference system (ANFIS)-based controller of a pacemaker in MATLAB Simulink. ANFIS uses learning and speed properties of fuzzy and neural networks. Based on body states and preprogrammed situations of patients (age and sex, etc.), heart rate and amplitude of pacing pulse are changed. Output signal that is fed backed from heart is compared to the reference fuzzy bases ANFIS signals. After designing ANFIS based controller, the stability of the proposed system has been tested in both the time (step response) and trequency (Bode diagram and Nichols chart) domains. In our previous study, the step response analyzed and compared with other works. For frequency domain, all the possible frequency analysis methods have been tested but because of nonlinear properties of ANFIS, after linearization, just the Bode diagram achieved good results. The step response results in time domain is compared with previous work's results including optimum heart pulse rate for each particular patient. In the frequency domain, the Bode diagram stability analysis showed gain and phase margin as follows: GM (dB) = 42.1 and PM (deg) = 100.

A dataset of a stimulated biceps muscle of electromyogram signal by using rossler chaotic equation.

Biological systems, composed of various interrelated components, are nonlinear systems. Improved disease diagnosis and the application of efficient treatment and therapeutic aids are the direct outcomes of possessing a deep understanding of such systems. Therefore, by employing diverse biological system simulations and subsequently analyzing their responses and characteristics, we can diagnose diseases. In this particular study, a novel stimulation method was utilized for the first time, employing the Rossler equation, to record the electromyogram (EMG) signals of the biceps muscle in ten participants. The presented dataset enables the extraction of biological, computational, and chaotic features, which can be utilized for disease classification and diagnosis. Furthermore, this dataset can be employed for the training, validation, and testing of neural networks.

Prediction of Biceps Muscle Electromyogram Signal Using a NARX Neural Network.

Background: This study was conducted to compare the response between the results of experimental data and the results achieved by the NARX neural network model to predict the electromyogram (EMG) signal on the biceps muscle in nonlinear stimulation conditions as a new stimulation model. Methods: This model is applied to design the controllers based on functional electrical stimulation (FES). To this end, the study was conducted in five stages, including skin preparation, placement of recording and stimulation electrodes, along with the position of the person to apply the stimulation signal and recording EMG, stimulation and recording of single-channel EMG signal, signal preprocessing, and training and validation of the NARX neural network. The electrical stimulation applied in this study is based on a chaotic equation derived from the Rossler equation and on the musculocutaneous nerve, and the response to this stimulation, i.e., the EMG signal, is from the biceps muscle as a single channel. The NARX neural network was trained, along with the stimulation signal and the response of each stimulation for 100 recorded signals from 10 individuals, and then validated and retested for trained data and new data after processing and synchronizing both signals. Results: The results indicate that the Rossler equation can create nonlinear and unpredictable conditions for the muscle, and we also can predict the EMG signal with the NARX neural network as a predictive model. Conclusion: The proposed model appears to be a good method to predict control models based on FES and to diagnose some diseases.

A Systematic Review of the Use of Commercial Wearable Activity Trackers for Monitoring Recovery in Individuals Undergoing Total Hip Replacement Surgery.

The innovation of wearable devices is advancing rapidly. Activity monitors can be used to improve the total hip replacement (THR) patients' recovery process and reduce costs. This systematic review assessed the body-worn accelerometers used in studies to enhance the rehabilitation process and monitor THR patients. Electronic databases such as Cochrane Database of Systematic Reviews library, CINAHL CompleteVR, Science Citation Index, and MedlineVR from January 2000 to January 2022 were searched. Due to inclusion criteria, fourteen eligible studies that utilised commercial wearable technology to monitor physical activity both before and after THR were identified. Their evidence quality was assessed with RoB 2.0 and ROBINS-I. This study demonstrates that wearable device technology might be feasible to predict, monitor, and detect physical activity following THR. They could be used as a motivational tool to increase patients' mobility and enhance the recovery process. Also, wearable activity monitors could provide a better insight into the individual's activity level in contrast to subjective self-reported questionnaires. However, they have some limitations, and further evidence is needed to establish this technology as the primary device in THR rehabilitation.

Detection and Classification of COVID-19 by Lungs Computed Tomography Scan Image Processing using Intelligence Algorithm.

The latest World Health Organization statistics show that the number of people living with COVID-19 disease is now more than 42 million worldwide. Some diagnosis methods include detecting and observing clinical symptoms associated with the disease (fever, dry cough, shortness of breath, sore throat, and muscle fatigue). Some other methods, such as computed tomography (CT)-scan imaging from the lungs, are the more accurate diagnostic methods. In this study, we examine the types of abnormal COVID-19 can cause in the lungs of infected subjects and detect and classify this disease. In this paper, we used data from the lung's CT-scan images from the 79 participants. To do this, in this article, for processing CT-scan images of the lungs to diagnose and classification of the COVID-19 disease in men and women of different ages, for rapid diagnosis and high accuracy of this disease by the automatic classification algorithm is used. The final results showed that the proposed method could base on different categories (gender, age categories, and type of damage caused by COVID-19) with high detection and classification accuracy. The algorithm presented in this article has accurately identified the data of healthy subjects and patients with coronavirus.

Detection and Classification of Myocardial Infarction with Support Vector Machine Classifier Using Grasshopper Optimization Algorithm.

BACKGROUND: Providing a noninvasive, rapid, and cost-effective approach to diagnose of myocardial infarction (MI) is essential in the early stages of electrocardiogram (ECG) signaling. In this article, we proposed the new optimization method for support vector machine (SVM) classifier to MI classification. METHODS: After preprocessing ECG signal and noise removal, three features such as Q-wave integral, T-wave integral, and QRS-complex integral have been extracted in this study. After that, different statistical tests have evaluated the matrix of these features. To more accurately detect and classify the MI disease, optimizing the SVM classification parameters using the grasshopper optimization algorithm (GOA) was first used in this study (that called SVM-GOA). RESULTS: After applying the GOA on the SVM classifier for all three kernels, the final results of MI detection for sensitivity, specificity, and accuracy were 100% ± 0%, 100% ± 0%, and 100% ± 0%, respectively. The final results of different MI types' classification after applying the GOA on SVM for polynomial kernel were obtained 100% ± 0%, 97.37% ± 0%, and 94.2% ± 0.2% for sensitivity and specificity and accuracy, respectively. However, the results of both linear and RBF kernels that were used for the SVM classifier method have also shown a significant increase after using GOA. CONCLUSION: This article's results show the highly desirable effect of applying a GOA to optimize different kernel parameters used in the SVM classifier for accurate detection and classification of MI. The proposed algorithm's final results show that the proposed system has a relatively higher performance than other previous studies.

Electrophysiological modeling of the effect of potassium channel blockers on the distribution of stimulation wave in the human gastric wall cells.

The purpose of this study is to model the electrophysiological behavior of excitable membrane and wavefront propagation in the Stomach Wall in physiological and pharmacological states. The propagation of this wave is based on cellular electrophysiological activity and ionic channel properties. In this study, we arranged the stomach wall cells together using the Gap Junctions approach. Slow wave is generated by gastric pacemaker cells. This wave propagates via the interaction of cells with each other throughout the stomach wall. Potassium currents are one of the main factors in regulating the pattern of wavefront propagation. To investigate the effect of limiting the exchange of potassium currents from cell membranes, 10%, 50%, 90%, and complete blockade were applied on both non-inactivating potassium current (IKni) and fast-inactivating potassium current (IKfi). The results show that IKniion channel blockage has a considerable effect on the plateau phase in the propagation of the excitation wave. The maximum value of the action potential in the plateau phase in the excitation wave with complete obstruction from -40.92 mV in the physiological state reached -18.97 mV, which is about 54% higher than the physiological state. Also, compared to the physiological state, complete blockage of the I_Kfi causes a 15% increase in the slow-wave spike phase (from -36.72 mV to -31.36 mV). Using this model, the effect of ions in different phases of slow-wave can be investigated. In addition, by blocking ion channels, functional disorders and smooth muscle contraction can be improved.

An Automatic Prolongation Detection Approach in Continuous Speech With Robustness Against Speaking Rate Variations.

In recent years, many methods have been introduced for supporting the diagnosis of stuttering for automatic detection of prolongation in the speech of people who stutter. However, less attention has been paid to treatment processes in which clients learn to speak more slowly. The aim of this study was to develop a method to help speech-language pathologists (SLPs) during diagnosis and treatment sessions. To this end, speech signals were initially parameterized to perceptual linear predictive (PLP) features. To detect the prolonged segments, the similarities between successive frames of speech signals were calculated based on correlation similarity measures. The segments were labeled as prolongation when the duration of highly similar successive frames exceeded a threshold specified by the speaking rate. The proposed method was evaluated by UCLASS and self-recorded Persian speech databases. The results were also compared with three high-performance studies in automatic prolongation detection. The best accuracies of prolongation detection were 99 and 97.1% for UCLASS and Persian databases, respectively. The proposed method also indicated promising robustness against artificial variation of speaking rate from 70 to 130% of normal speaking rate.

Modeling the virus-immune system interactions in the peripheral bloodstream of HIV infected individuals using a cellular automata model with considering the effects of antiretroviral therapy.

BACKGROUND: According to the World Health Organization, by the end of last year, about 37 million people throughout the world were diagnosed with AIDS and millions of people die each year from this disease. OBJECTIVE: To develop an appropriate model which depicts the mechanism of the dynamics involved in the interactions between HIV and immune system in peripheral bloodstream of HIV infected individuals by considering the phenomena of virus mutation and taking into account the role of latently infected cells in speared of infection and considering the effects of antiretroviral drugs and occurrence of drug resistance in our model in order to assess the results obtained from applying different therapeutic methods. METHODS: Two-dimensional CA model with Moor neighboring was developed. Various agents which they were referring to peripheral bloodstream particles of HIV infected individuals were defined. Then the biological rules were extracted from both expert knowledge and the authoritative articles. The extracted rules were applied for updating the states of these agents. The effects of using antiretroviral drug treatment were considered by applying drug's effectiveness of both of protease and reverse transcriptase inhibitors as two separate inputs of model. RESULTS: Time evolution curves of concentrations of defined agents were shown as our results. In case of considering no treatment, our results showed that concentrations of healthy CD4+T cells reached the threshold of AIDS after a bout 250 weeks. By applying monotherapy method, the concentrations of these cells remained on the threshold of AIDS for a long time and applying combined antiretroviral therapy (cART) method leaded to increase the concentration of these cells 20% upper than threshold of AIDS. Also, by applying monotherapy and cART compared with no treatment, the concentrations of infected CD4+T cells 10% and 40% decreased further, respectively and for the level of viral load, leads to a reduction of almost 55% and 90%, respectively. Belated treatment, comparison with early treatment, caused almost 10% reduce (increase) in steady state concentrations of healthy (infected) cells.

Prediction of epileptic seizures based on heart rate variability.

BACKGROUND: Until now, different approaches have been published to resolve the problem of predicting epileptic seizures. The results are reminiscent of a substantial need for improvements in these methods to reach the stage of the clinical application. Our aim is to develop a reliable epileptic seizure prediction algorithm based on the Heart Rate Variability (HRV) analysis. METHODS: We analyzed the HRV of sixteen epileptic patients with a total of 170 seizures, to predict the occurrence of seizures based on the dynamic changes of Electrocardiogram (ECG) during the pre-ictal period. Time and frequency-domain features were computed forthe consecutive time windows with a length of five minutes. An adaptive decision threshold method was used for raising alarms. Predictions were made when selected features exceeded the decision thresholds. RESULTS: For the seizure occurrence period (SOP) of 4:30 minutes, and intervention time (IT) of 110 Sec, the presented method showed an average sensitivity of 78.59%, and average false prediction rate of 0.21/Hr, which indicates that the system has superiority to the random predictor. CONCLUSION: The proposed approach shows a potential in the monitoring of epileptic patients and improving their life quality. The overall performance of the algorithm is a step forward for clinical implementation.

Classification of ictal and seizure-free HRV signals with focus on lateralization of epilepsy.

OBJECTIVE: Epileptic onsets often affect the autonomic function of the body during a seizure, whether it is in ictal, interictal or post-ictal periods. The different effects of localization and lateralization of seizures on heart rate variability (HRV) emphasize the importance of autonomic function changes in epileptic patients. On the other hand, the detection of seizures is of primary interests in evaluating the epileptic patients. In the current paper, we analyzed the HRV signal to develop a reliable offline seizure-detection algorithm to focus on the effects of lateralization on HRV. MATERIALS AND METHODS: We assessed the HRV during 5-min segments of continuous electrocardiogram (ECG) recording with a total number of 170 seizures occurred in 16 patients, composed of 86 left-sided and 84 right-sided focus seizures. Relatively high and low-frequency components of the HRV were computed using spectral analysis. Poincaré parameters of each heart rate time series considered as non-linear features. We fed these features to the Support Vector Machines (SVMs) to find a robust classification method to classify epileptic and non-epileptic signals. Leave One Out Cross-Validation (LOOCV) approach was used to demonstrate the consistency of the classification results. RESULTS: Our obtained classification accuracy confirms that the proposed scheme has a potential in classifying HRV signals to epileptic and non-epileptic classes. The accuracy rates for right-sided and left-sided focus seizures were obtained as 86.74% and 79.41%, respectively. CONCLUSIONS: The main finding of our study is that the patients with right-sided focus epilepsy showed more reduction in parasympathetic activity and more increase in sympathetic activity. It can be a marker of impaired vagal activity associated with increased cardiovascular risk and arrhythmias. Our results suggest that lateralization of the seizure onset zone could exert different influences on heart rate changes. A right-sided seizure would cause an ictal tachycardia whereas a left-sided seizure would result in an ictal bradycardia.

Pre-ictal heart rate variability assessment of epileptic seizures by means of linear and non-linear analyses.

OBJECTIVE: The purpose of the present study was to analyze the effects of epilepsy on the autonomic control of the heart in pre-ictal phase in order to find an algorithm of early detection of seizure onset. METHODS: Overall 133 epileptic seizures were analyzed from 12 patients with epilepsy (seven males and five females; mean age 43.91 years, SD: 10.16) participated in this study. Single lead electrocardiogram recordings of epileptic patients were compiled. 240, 90-30, 30-10 and 5 minutes heart rate variability (HRV) signals of preseizure were chosen for analysis of heart rate. As HRV signals are non-stationary, a set of time and frequency domain features (Mean HR, Triangular Index, LF, HF, LF/HF) and nonlinear parameters (SD1, SD2 and SD2/SD1 indices derived from Poincare plots) extracted from HRV is analyzed. Statistical analysis was performed using paired sample t-test for comparisons of the segments and differences between pre-ictal segments were evaluated by Tukey tests. RESULTS: There was slight tachycardia in segments near the seizure (30 minutes before: 85.3517 bpm, 5 minutes before: 119.3630.82 bpm, p=0.0207) which significantly differ from baseline in segments far from seizure (240 minutes before: 66.5211.7 bpm). Also there was significant increase in LF/HF ratio (30 minutes before: 1.10.22, 5 minutes before: 2.120.5, p=0.0332) and SD2/SD1 ratio (30 minutes before: 1.20.15, 5 minutes before: 2.030.55, p=0.0431) when compared to segments far from the seizure (240 minutes before: 0.780.24 and 0.780.14) respectively. Although there was about decrease of triangular index in segments near the seizure the percentage of decrease was not comparable to segments far from the seizure. CONCLUSION: Significant changes of HRV parameters in pre-ictal (5 minutes before the seizure) are obviously higher in comparison to interictal baseline. Pre-ictal significant changes of HRV suggesting that this time can be considered as prediction time for designing an algorithm of early detection of seizure onset based on HRV.

Rule-based Method for Extent and Localization of Myocardial Infarction by Extracted Features of ECG Signals using Body Surface Potential Map Data.

In this study, a method for determining the location and extent of myocardial infarction using Body Surface Potential Map data of PhysioNet challenge 2007 database is presented. This data is related to four patients with myocardial infarction. We used two patients as training set to determine rules and two other patients as testing set of the proposed model. First, T-wave amplitude, T-wave integral, Q-wave amplitude and R-wave amplitude as four features of ECG signals were extracted. Then we defined several rules and proper thresholds for localization and determining the extent of myocardial infarction. To determine the precise location and extent of myocardial infarction, 17-segment standard model of left ventricle was used. Finally, overall accuracy of this method was shown with SO, CED and EPD parameters. We obtained 1.16, 1 and 5.3952 for SO, CED and EPD, respectively, in our test data. Two main advantages of this method are simplicity and high accuracy.