In this paper, the method of power spectral estimation is used to analyze the heart rate variability (HRV) signals for 15 heroin addicts and 15 healthy persons. The analysis result shows that there is a significant difference of the locations of the high-frequency peaks between the power spectra of heroin addicts' HRV signals. It means that the locations for heroin addicts lie in 0.437 +/- 0.064 Hz and the locations for healthy persons lie in 0.325 +/- 0.052 Hz.
Adolescent ; Adult ; Autonomic Nervous System ; physiopathology ; Female ; Heart Rate ; physiology ; Heroin Dependence ; physiopathology ; Humans ; Male ; Signal Processing, Computer-Assisted

Using wavelet transforms method, time-frequency characteristics of pulse signals from 15 heroin addicts and 15 healthy persons were analyzed. According to 3-D and contour plots used to display discrete dyadic wavelet transforms, the significant difference of time-frequency characteristics between the signals of heroin addicts and healthy persons were revealed. A primary criterion was also obtained,with the criterion, 15 heroin addicts were entirely identified, while two healthy subjects were misidentified. The research result shows that the wavelet-based multiresolution analysis is a very effective method to extract characteristics of pulse signals. It is valuable to the diagnosis and therapy for heroin addicts.
Adult ; Algorithms ; Diagnosis, Computer-Assisted ; Female ; Fourier Analysis ; Heroin Dependence ; diagnosis ; physiopathology ; Humans ; Male ; Medicine, Chinese Traditional ; Pulse ; Signal Processing, Computer-Assisted

Using the common algorithm and the Mellin algorithm of a continuous wavelet transform, we analyzed the pulse signals of 15 heroin addicts and 15 normal persons. With the use of two algorithms, every pulse signal was processed under 4 scales. From the analyzed results, we found that there was significant difference of wavelet transform coefficients in the time interval 0.2 to approximately 0.4 seconds between the heroin addicts and normal persons. In this paper, the critical parameter used to classify heroin addicts and normal persons is given to every algorithm. The research result of this paper shows that the continuous wavelet transform is really an effective method for processing pulse signals.
Algorithms ; Diagnosis, Computer-Assisted ; Heroin Dependence ; physiopathology ; Humans ; Medicine, Chinese Traditional ; Pulse ; Signal Processing, Computer-Assisted