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Analysis of heart rate variability is a valuable method to investigate the sympathetic and parasympathetic function of the autonomic nervous system. Although such analyses can provide quantitative estimates of autonomic neural activity, simultaneous recording of neural activities and ECG will allow more direct investigation of neural modulation of heart rate. We developed a method that allows direct...
Accurate processing of electrocardiogram (ECG) signals requires a sensitive and robust QRS detection method. In this study, three methods are quantitatively compared using a similar algorithm structure but applying different transforms to the differentiated ECG. The three transforms used are the Hilbert transformer, the squaring function, and a second discrete derivative stage. The first two have...
Time-varying autoregressive modeling may consider the driving noise variance as a constant. In this work, the properties of the autoregressive driving noise variance of heart rate variability, with different stationary physiological conditions (resting in supine and sitting; exercise) are obtained. The effect of constant variance consideration for ramp exercise and recovery (a nonstationary condition)...
Accurate processing of electrocardiogram (ECG) signals requires a sensitive and robust QRS detection method. In this study, three methods are quantitatively compared using a similar algorithm structure but applying different transforms to the differentiated ECG. The three transforms used are the Hilbert transformer, the squaring function, and a second discrete derivative stage. The first two have...
Analysis of heart rate variability is a valuable method to investigate the sympathetic and parasympathetic function of the autonomic nervous system. Although such analyses can provide quantitative estimates of autonomic neural activity, simultaneous recording of neural activities and ECG will allow more direct investigation of neural modulation of heart rate. We developed a method that allows direct...
Beat-to-beat changes in cardiac signals or heart rate variability (HRV) are controlled by the two branches of autonomic nervous system (ANS) in a very complex manner. Although traditional HRV (tHRV) analysis has shown to provide information on cardiac ANS control, it often fails to isolate the effect of two branches in HRV signals. This problem becomes more obvious especially at low respiratory rates...
Time-varying autoregressive modeling may consider the driving noise variance as a constant. In this work, the properties of the autoregressive driving noise variance of heart rate variability, with different stationary physiological conditions (resting in supine and sitting; exercise) are obtained. The effect of constant variance consideration for ramp exercise and recovery (a nonstationary condition)...
Beat-to-beat changes in cardiac signals or heart rate variability (HRV) are controlled by the two branches of autonomic nervous system (ANS) in a very complex manner. Although traditional HRV (tHRV) analysis has shown to provide information on cardiac ANS control, it often fails to isolate the effect of two branches in HRV signals. This problem becomes more obvious especially at low respiratory rates...
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