Well-established technologies to analyze biological signals including rhythmic heartbeat are available and accessible to scholars. However, stronger empirical evidence is required to justify the use of these technologies as practical tools in the field of biomedicine. Here we conducted analyses of heartbeat interval time series using an analytical technology developed across three decades—detrended fluctuation analysis (DFA)— to verify the power-law/scaling characteristics of signals that fluctuate in a regular, irregular, or erratic manner. We believe that DFA is a useful tool because it can quantify the heart condition by a scaling exponent, with a value of one (1) set as the default for a healthy state. This baseline value can be compare...
The fractal structure of heart rate is usually quantified by estimating a short-term ( inverted ques...
We analyse the heartbeat interval time series in this chapter. Our time series analysis concepts and...
Considering the highly nonlinear and non stationary features of the ECG signal proven by latest rese...
We made our own DFA (detrended fluctuation analysis) program. We applied it for checking characteris...
We analyzed heartbeat-intervals by using our own program of detrended fluctuation analysis (DFA). "A...
How to quantify the complexity of a physiological signal is a crucial issue for verifying the underl...
We analyzed the heartbeat interval to test the possibility that the detrended fluctuation analysis (...
Detrended fluctuation analysis (DFA), suitable for the analysis of nonstationary time series, has co...
The aim of this study was to make a method for an early detection of malfunction, e.g., abnormal vib...
We analyzed the heartbeat interval by the detrended fluctuation analysis (DFA) in models and humans....
Detrended fluctuation analysis (DFA) has been shown to be a useful tool for diagnosis of patients wi...
Chaos and fractal based measurements, such as Detrended Fluctuation Analysis (DFA), have been widely...
PACS. 05.45.Tp – Time series analysis. PACS. 05.40.-a – Fluctuation phenomena, random processes, noi...
We analyzed the heartbeat interval by the detrended fluctuation analysis (DFA) in models and humans....
Stress has not been fully defined in terms of neuroscience. But, it might be possible to quantify it...
The fractal structure of heart rate is usually quantified by estimating a short-term ( inverted ques...
We analyse the heartbeat interval time series in this chapter. Our time series analysis concepts and...
Considering the highly nonlinear and non stationary features of the ECG signal proven by latest rese...
We made our own DFA (detrended fluctuation analysis) program. We applied it for checking characteris...
We analyzed heartbeat-intervals by using our own program of detrended fluctuation analysis (DFA). "A...
How to quantify the complexity of a physiological signal is a crucial issue for verifying the underl...
We analyzed the heartbeat interval to test the possibility that the detrended fluctuation analysis (...
Detrended fluctuation analysis (DFA), suitable for the analysis of nonstationary time series, has co...
The aim of this study was to make a method for an early detection of malfunction, e.g., abnormal vib...
We analyzed the heartbeat interval by the detrended fluctuation analysis (DFA) in models and humans....
Detrended fluctuation analysis (DFA) has been shown to be a useful tool for diagnosis of patients wi...
Chaos and fractal based measurements, such as Detrended Fluctuation Analysis (DFA), have been widely...
PACS. 05.45.Tp – Time series analysis. PACS. 05.40.-a – Fluctuation phenomena, random processes, noi...
We analyzed the heartbeat interval by the detrended fluctuation analysis (DFA) in models and humans....
Stress has not been fully defined in terms of neuroscience. But, it might be possible to quantify it...
The fractal structure of heart rate is usually quantified by estimating a short-term ( inverted ques...
We analyse the heartbeat interval time series in this chapter. Our time series analysis concepts and...
Considering the highly nonlinear and non stationary features of the ECG signal proven by latest rese...