Sharif Digital Repository

Analysis of the heart rate (HR) variation due to respiration, known as respiratory sinus arrhythmia (RSA), is a method to assess the autonomic nervous system (ANS) function. In this paper a physiologically-based mathematical model consisting of the cardiorespiratory system and ANS control has been used in order to study the cardiovascular response (mean arterial blood pressure and HR) to breathing with different respiration rates and tidal volumes. Simulation results show that RSA has its maximal amplitude at the respiration frequency of 0.12 Hz and that RSA amplitude varies linearly by tidal volume. These results are in agreement with real data from the literature. In addition, the... 

In this paper, the Monte Carlo technique is used to determine the optical detection strategies in three-layered (maternal, amniotic fluid and fetal) tissue model. This model is utilized to estimate the transabdominal optical power and optimum source-detector (S-D) separation. Results based on the launching of 2 million photons with 1 mW optical power showed that the expected optical power output is in the range of 10 -6-10 -10 W/cm 2 depending on S-D separation. Considering the limit of the signal processing methods (such as adaptive noise cancelling) and the use of silicon photodetector, an S-D separation of 4 cm has been selected as a practical compromise between signal level and... 

We describe a method for analyzing the stochasticity in non-stationary data for the beat-to-beat fluctuations in the heart rates of healthy subjects, as well as those with congestive heart failure. The method analyzes the return time series of the data as a Markov process, and computes the Markov time scale, i.e., the time scale over which the data are a Markov process. We also construct an effective stochastic continuum equation for the return series. We show that the drift and diffusion coefficients, as well as the amplitude of the return time series for healthy subjects are distinct from those with CHF. Thus, the method may potentially provide a diagnostic tool for distinguishing healthy... 

Triboelectric nanogenerators (TENG) have gained prominence in recent years, and their structural design is crucial for improvement of energy harvesting performance and sensing. Wearable biosensors can receive information about human health without the need for external charging, with energy instead provided by collection and storage modules that can be integrated into the biosensors. However, the failure to design suitable components for sensing remains a significant challenge associated with biomedical sensors. Therefore, design of TENG structures based on the human body is a considerable challenge, as biomedical sensors, such as implantable and wearable self-powered sensors, have recently... 

Body weight is influenced by both food intake and energy expenditure. Acylated ghrelin enhances appetite, and its circulating level is suppressed by Growth Hormone. Data on the acylated ghrelin responses to exercise of different intensities in obese individuals are currently not available. This study examined the effects of an intermittent exercise protocol on acylated ghrelin levels and hunger ratings in obese people. Nine inactive male ran on the treadmill at 0900 with progressive intensities of 50, 60, 70, and 80% of VO2max for 10, 10, 5, and 2 min respectively. Blood samples were collected before the exercise at 0845 (-15 min as the resting values), after each workload (10, 23, 31, and... 

Acute hypotension episodes are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prediction of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this diagnosis. In this study new physiological time series are generated based on heart rate, systolic blood pressure, diastolic blood pressure and mean blood pressure time series. Statistical features of these time series are extracted and patients whom are exposed to acute hypotension episodes in future 1 hour time interval and whom are not, are classified based on these features and with the aid of... 

We present generalizations of the previously published artificial models for generating abnormal cardiac rhythms to provide simulations of coupled cardiovascular (CV) signals with different beat morphologies. Using a joint dynamical formulation, we generate the normal morphologies of the cardiac cycle using a sum of Gaussian kernels, fitted to real CV recordings. The joint inter-dependencies of CV signals are introduced by assuming the same angular frequency and a phase coupling. Abnormal beats are then specified as new dynamical trajectories. An ergadic first-order Markov chain is also used for switching between normal and abnormal beat types. Probability transitions can be learned from... 

The accuracy of Photoplethysmographic signals is often not adequate due to motional artifacts induced in the recording site. Over recent decades there has been a widespread effort to reduce these artifacts and different methods are used for this aim. Nevertheless there are still some contradictory results reported by different methods about their effectiveness in artifact reduction. In this paper, we aim to compare three of established methods for PPG noise reduction on a unique dataset. Among different reported methods, we have chosen Adaptive Noise Cancellation (ANC), Discrete Wavelet Transform (DWT) and a newly developed method Cycle-by-cycle Fourier Series Analysis (CFSA) for denoising.... 

Respiratory activity influences electrocardiographic measurements (ECG) in various ways. Therefore, extraction of respiratory information from ECG, namely ECG-derived respiratory (EDR), can be used as a promising noninvasive method to monitor respiration activity. In this paper, an automatic EDR extraction system using single-lead ECG is proposed. Respiration effects on ECG are categorized into two different models: additive and multiplicative based models. After selection of a proper model for each subject using a proposed criterion, gaussian process (GP) and phase space reconstruction area (PSRArea) are introduced as new methods of EDR extraction for additive and multiplicative models,... 

Background and Objective: Blood flow variation during cardiac cycle is the main mechanism of atherosclerotic development which is dependent on. Methods: The present work mainly tends to investigate stenosis effect in dynamic curvature of coronary artery. This paper presents numerical investigations on wall shear stress profiles in three-dimensional pulsatile flow through curved stenotic coronary arteries for both static and dynamic model. In order to do so, three-dimensional models related to the curved arteries with two degrees of stenosis (30% and 50%). Results: Lower amount of wall shear stress is found near the inner wall of artery distal to the plaque region (stenosis) and in both... 

A fast algorithm based on the nonlinear dynamical model for the electrocardiogram (ECG) is presented for the precise extraction of the characteristic points of these signals with baseline drift. Using the adaptive bionic wavelet transform, the baseline wander is removed efficiently. In fact by the means of the bionic wavelet transform, the resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential, which results in a better baseline wander cancellation. At the next step the parameters of the model are chosen to have the least square error with the original ECG. Determining... 

This paper adopts a holistic approach to stress detection issues in software and hardware phases and aims to develop and evaluate a specific low-power and low-cost sensor using physiological signals. First, a stress detection model is presented using a public data set, where four types of signals, temperature, respiration, electrocardiogram (ECG), and electrodermal activity (EDA), are processed to extract 65 features. Using Kruskal-Wallis analysis, it is shown that 43 out of 65 features demonstrate a significant difference between stress and relaxed states. K nearest neighbor (KNN) algorithm is implemented to distinguish these states, which yields a classification accuracy of 96.0 ± 2.4%. It... 

In this paper, a novel nonlinear joint dynamical model is presented, which is based on a set of coupled ordinary differential equations of motion and a Gaussian mixture model representation of pulsatile cardiovascular (CV) signals. In the proposed framework, the joint interdependences of CV signals are incorporated by assuming a unique angular frequency that controls the limit cycle of the heart rate. Moreover, the time consequence of CV signals is controlled by the same phase parameter that results in the space dimensionality reduction. These joint equations together with linear assignments to observation are further used in the Kalman filter structure for estimation and tracking. Moreover,... 

A bilateral impedance controller is presented to enable robot-assisted surgery of a beating heart. For this purpose, two desired impedance models are designed and realized for the master and slave robots interacting with the operator (surgeon) and the environment (heart tissue), respectively. The impedance models are designed such that (a) the slave robot complies with the oscillatory motion of the beating heart and (b) the surgeon perceives the non-oscillatory portion of the slave/heart contact force at the master robot implying arrested-heart surgery. These performance goals are achieved via appropriate adjustment of the impedance model parameters without any measurement or estimation of... 

Objective: A novel electrocardiogram (ECG) classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. Methods: The proposed solution employs a novel architecture consisting of wavelet transform and multiple long short-term memory (LSTM) recurrent neural networks (see Fig. 1). Results: Experimental evaluations show superior ECG classification performance compared to previous works. Measurements on different hardware platforms show the proposed algorithm meets timing requirements for continuous and real-time execution on wearable devices. Conclusion: In contrast to many compute-intensive deep-learning based approaches, the... 

Background: Remote cardiac monitoring and screening have already become an integral telemedicine component. The wide usage of several different wireless electrocardiography (ECG) devices warrants a validation study on their accuracy and reliability. Methods: Totally, 300 inpatients with the Nabz Hooshmand-1 handheld ECG device and the GE MAC 1200 ECG system (as the reference) were studied to check the accuracy of the devices in 1 and 6-limb lead performance. Simultaneous 10-second resting ECGs were assessed for the most common ECG parameters in lead I. Afterward, 6-lead ECGs (limb leads), were performed immediately and studied for their morphologies. Results: Of the 300 patients, 297 had...