The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
As technology evolves, its consumers gain considerable advantages to bring prosperity for all humankind. It is so in medical environment. Even though there always has been standards in hospital or other related medical sites, it is possible for people with the help of technology to study about simple theory of pathology and find another mechanism to meet those standards, i.e., to create new method...
This paper presents the canonical correlation analysis (CCA) method for dimensional reduction of sleep apnea features extracted from the electrocardiogram single lead. The feature extraction belong to the linear and nonlinear techniques of variance of heart rhythm or heart rate variability and respiratory waveform from electrocardiography signal. These are benefit to evaluate the sleep apnea in noninvasive...
The Hurst Exponent of the time series plays a crucial role to differentiate between a sleep apnea patient from a normal patient. They are anti-persistent in nature and the former has more self similarity compared to a normal patient. It has been established that they are AR process and non stationary and Semblance analysis also suggests strong correlation both positive and negative between them. We...
It is now known that multiscale entropy has the potential to distinguish certain pathological time series clearly and reliably from the corresponding healthy series. However, the implications of this parameter for Heart Rate Variability (HRV) have not been studied extensively. Also, as reported by other studies, the Poincare plots of the R-R interval series of a human subject's ECG signal (which too...
Standard Symbolic Aggregation Approximation (SAX) is at the core of many effective time series data mining algorithms. Its combination with Bag-of-Patterns (BoP) has become the standard approach with state-of-the-art performance on standard datasets. However, standard SAX with the BoP representation might neglect internal temporal correlation embedded in the raw data. In this paper, we proposed time...
Heart rate variability (HRV) has been studied as a non-invasive technique to characterize the autonomic nervous system (ANS) regulation of the heart. Non-linear methods based on chaos theory have been used during the last decades as markers for risk stratification. However, interpretation of these nonlinear methods in terms of sympathetic and parasympathetic activity is not fully established. In this...
Detecting incident anomalies within temporal data - time series becomes useful in a variety of applications. In this paper, anomalies in time series are divided into two categories, namely amplitude anomalies and shape anomalies. A unified framework supporting the detection of both types of anomalies is introduced. A fuzzy clustering is employed to reveal the available structure within time series...
Sample entropy (SampEn) is a popular complexity measure in HRV analysis. SampEn is estimated by fixing the values of the embedding dimension "m" and distance threshold "r" and traditionally SampEn is calculated with m=2 and r=0.2 times the standard deviation of the series. Attempts to extend the estimates to different (m, r) pairs are hampered by the high computational burden of...
Electrocardiography (ECG) is a transthoracic interpretation of the electrical activity of the human's heart over time captured and is highly irregular, random, and variable from person to person. Recently, the literature has revealed that this kind of signal is, in fact, chaotic. Because of people's ECGs are extremely hard to be artificially duplicated, this paper intends to investigate the way of...
A method to construct a predictive time series index based on QTc-intervals is proposed in this paper. Monitored electrocardiography (ECG) data is converted into a root mean square of successive difference (RMSSD) trend-line by first finding the QT intervals [1] and then using [2]. The trend-line is then used as a priori in extrapolating the predictive trend. The next unknown RMSSD is extrapolated...
Drowsiness during driving causes traffic accidents frequently. It is therefore desirable to detect the drowsiness. Drowsiness occurs when the parasympathetic nerve activity becomes dominant. Related researches show that high frequency (HF) component of heart rate variability (HRV) on an electrocardiogram (ECG) has certain correlation with drowsiness by reflecting the parasympathetic nerve activity...
Nonlinear dynamics has been playing an outstanding role in the study of heart in the last decades. It brought many parameters that improved the diagnostic methods, as the Correlation Dimension or Lyapunov Exponents. In this work we propose a new of these parameters, The Higher Reconstruction Step (HRS), an extension of the Time Lag obtained from the Autocorrelation Function (ACF). We collected R-R...
Accurate identification of chaotic behavior from a deterministic periodic process is a difficult but significant issue as underlying dynamics in the data determines the sequent analysis techniques. Since both chaotic and periodic time series can have the similar waveform and spectrum the commonly used approaches for detecting chaotic behavior have limitation. So in this paper, we present an alternative...
Hilbert-Huang transform (HHT) is composed of the empirical mode decomposition (EMD) as the first step of the procedure and Hilbert spectral analysis (HSA) as the second step. It is a recent tool in the analysis of signals originating from nonlinear processes as well as nonstationary signals. Empirical mode decomposition produces a set of intrinsic mode functions and the core idea is based on the assumption...
We discuss the merits of adaptive statistical models for biosignals in a daily life context. Processing of this type of signals poses a number of challenges. First, it is clear that an adaptive model is needed to tailor for the differences in physiology between individuals, as well as adapt to someone's current physiological state. Second, in a daily life setting we use unobtrusive measurement devices,...
In recent decades many research effort has been expended in the field of noninvasive, continuous blood pressure (BP) estimation by cardiovascular surrogate parameters, mainly the pulse transit time (PTT). Due to differences in the measurement setup and in the consideration of important physiological aspects, however, there is a multitude of inconsistent statements about the BP tracking capabilities...
Detrended fluctuation analysis (DFA) has been shown to be a useful tool for diagnosis of patients with cardiac diseases. The scaling exponents obtained with DFA are an indicator of power-law correlations in signal fluctuation, independently of signal amplitude and external trends. In this work, an approach based on DFA was proposed for analyzing heart rate variability (HRV) by means of RR series....
We know, nowadays, that the dynamics of cardio respiratory system is extremely complex and chaotic. Any modification in the systems dynamics can be a sign of a disease. This work investigates the presence of modifications in the dynamics of the cardiac rhythms through the observations in the dynamics of the attractor built from RR series. For each series, the decorrelation time was measured in order...
This paper proposes the Stationarity Index, a measure of the similarities of the auto correlation integral of a section of a time series and the cross correlation of that section with others of the same time series. This measure of similarity is a measure of the stationarity of the time series and therefore can be used not only to detect nonstationarity but to also quantify it. The index is then successfully...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.