Sharif Digital Repository

Low and high valence were induced in 20 male volunteers using two groups of pictures stimuli. Heart response was compared between two groups from RR series extracted from recorded ECG measurements. Mean heart rate and heart rate variability measures including time, frequency and Poincare domain were extracted. The results revealed that HRV triangular index, SDNN and SD2 were the only statistically significant parameters between groups (p<0.05). Mean heart rate and power in LF and HF bands were also different between low and high valence groups however level of significance was not reached. © 2015 CCAL  

Analysis of heart rate variability (HRV) is commonly used for characterization of autonomic nervous system. As high frequency (HF, known as the respiratory-related) component of HR, overlaps with the typical low frequency (LF) band when the respiratory rate is low, a reference signal for HF variations would help in better discriminating the LF and HF components of HR. The present study proposes a model for time-varying separation of HRV components as well as estimation of HRV parameters using respiration information. An autoregressive moving average with exogenous input (ARMAX) model of HRV is considered with a parametrically modeled respiration signal as the input. The model parameters are... 

In this paper we introduce two methods for measuring irregularities in human heartbeat time series (HHTS). First we consider the multi-fractal structure of HHTS to distinguish healthy individuals and from those with congestive heart failure. In this way we modify the Extended Self-Similarity (ESS) method and apply it to HHTS. Our second approach is based on the recursive method, which we use to predict the duration of the next heartbeat by considering a few previous ones. We use standard physiological data and show that these approaches lead to very satisfactory methods to resolve the healthy and CHF individuals. These methods can be used potentially in portable electronic heart alarm... 

An essential requirement for performing robotic assisted surgery on a freely beating heart is a prediction algorithm which can estimate the future trajectory of the heart in the varying heart rate (HR) conditions of real surgery with a high accuracy. In this study, a hybrid amplitude modulation- (AM) and autoregressive- (AR) based algorithm was developed to enable estimating the global and local oscillations of the beating heart, raised from its major and minor physiological activities. The AM model was equipped with an estimator of the heartbeat frequency to compensate for the HR variations. The RMS of the prediction errors of the hybrid algorithm was in the range of 165–361 μm for the... 

There has been an increasing interest in telemonitoring thanks to the availability of new technologies for data transmission and processing with better performances and lower costs. In this paper, we try to develop and implement the HRV signal processing into a Field Programmable Gate Array (FPGA). The hardware implementing algorithm was developed in Verilog Hardware Description Language (HDL). In designed hardware, after defining the number of samples in the input, we extract and analyses the Triangular Phase Space Mapping (TPSM), a novel method for representation of heart rate. The performance of the system was tested using MATLAB and validated based on the input signals  

We present generalizations of our previously published artificial models for generating multi-channel ECG so that the simulation of abnormal rhythms is possible. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are then specified either as new dipoles, or as perturbations of the existing dipole. Switching between normal and abnormal beat types is achieved using a hidden Markov model (HMM). Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes form beat-to-beat... 

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...