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Spectral decomposition of the heart rate variability during whole night recordings was obtained, in order to assess the characteristic fluctuations in the heart rate and their spectral parameters during REM and NREM sleep stages. A time-varying autoregressive model was used in the analysis since it allows both real time and transitory episodes evaluation of spectral parameters of the heart rate variability...
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)...
The heart rate variability (HRV) signal is indicative of autonomic regulation of the heart rate (HR). It could be used as a noninvasive marker in monitoring the physiological state of an individual. Currently, the primary method of deriving the HRV signal is to acquire the electrocardiogram (ECG) signal, apply appropriate QRS detection algorithms to locate the R wave and its peak, find the RR intervals,...
Computer-aided bedside patient monitoring requires real-time analysis of vital functions. On-line Holter monitors need reliable and quick algorithms to perform all the necessary signal processing tasks. This paper presents the methods that were conceptualized and implemented at the development of such a monitoring system at Medical Clinic No. 4 of Targu-Mures. The system performs the following ECG...
Spectral decomposition of the heart rate variability during whole night recordings was obtained, in order to assess the characteristic fluctuations in the heart rate and their spectral parameters during REM and NREM sleep stages. A time-varying autoregressive model was used in the analysis since it allows both real time and transitory episodes evaluation of spectral parameters of the heart rate variability...
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)...
The heart rate variability (HRV) signal is indicative of autonomic regulation of the heart rate (HR). It could be used as a noninvasive marker in monitoring the physiological state of an individual. Currently, the primary method of deriving the HRV signal is to acquire the electrocardiogram (ECG) signal, apply appropriate QRS detection algorithms to locate the R wave and its peak, find the RR intervals,...
Computer-aided bedside patient monitoring requires real-time analysis of vital functions. On-line Holter monitors need reliable and quick algorithms to perform all the necessary signal processing tasks. This paper presents the methods that were conceptualized and implemented at the development of such a monitoring system at Medical Clinic No. 4 of Targu-Mures. The system performs the following ECG...
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