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PubMed1992

Noninvasive determination of baroreflex sensitivity in man by means of spectral analysis.

The spectral analysis technique was applied for noninvasive assessment of heart-rate baroreflex sensitivity (BRS). The coherence between fluctuation of blood pressure and heart rate at 0.1 Hz and at respiratory frequency is high. This fact enables the assessment of BRS by means of calculating the modulus (or gain) of the transfer function between variations in blood pressure and heart rate. The noninvasive continuous blood pressure registration according to Penáz was used. During voluntarily controlled breathing intervals, the amplitude of 0.1 Hz and respiratory peaks in the spectra of heart rate and blood pressure changed markedly. Nevertheless, the average sensitivity of the baroreflex (modulus) changed insignificantly. This result indicated that the stability of BRS can be advantageous for the use of BRS in clinical practice. The difference between the modulus at 0.1 Hz and at the breathing rate indicates that baroreflex is only one of the factors causing respiratory arrhythmia. We also compared the determination of BRS by spectral analysis with the following alternative method: both lower extremities were occluded for 5 minutes. The release of pressure in the occluding cuffs decreased blood pressure which was followed by a baroreceptor-mediated increase of heart rate. Both methods correlated, but more detailed analysis revealed the role of the low pressure receptors in BRS determined by spectral analysis.

Blood Pressure↗
PubMedDate not supplied

Myocardial wall motion evaluation using amplitude frequency spectral analysis.

Frequency spectral analysis of cardiac wall motion as calculated from the recorded video kymogram are demonstrated. Characteristic harmonics following the fundamental frequency of the cardiac rate have been demonstrated to have an alternating sequential energy distribution as measured from the free margin of the left ventricle of humans. The characteristic patterns occur in approximately 80% of the 50 patients evaluated. The correlation of these harmonics with physiologic events is not yet complete. Amplitude frequency spectral analysis offers an objective method for analysis of cardiac kymography not otherwise possible.

Adult↗
PubMed2002

[Application of AOTF in spectral analysis. 3. Application of AOTF in atomic emission spectral analysis].

An atomic emission spectrometer based on acousto-optic tunable filter (AOTF) was self-constructed and was used to evaluate its practical use in atomic emission analysis. The AOTF used was of model TEAF5-0.36-0.52-S (Brimrose, USA) and the frequency of the direct digital RF synthesizer ranges from 100 MHz to 200 MHz. ICP and PMT were used as light source and detector respectively. The software, written in Visual C++ and running on the Windows 98 platform, is of an utility program system having two data banks and multiwindows. The wavelength calibration was performed with 14 emission lines of Ca, Y, Li, Eu, Sr and Ba using a tenth-order polynomial for line fitting method. The absolute error of the peak position was less than 0.1 nm, and the peak deviation was only 0.04 nm as the PMT varied from 337.5 V to 412.5 V. The scanning emission spectra and the calibration curves of Ba, Y, Eu, Sc and Sr are presented. Their average correlation coefficient was 0.9991 and their detection limits were in the range of 0.051 to 0.97 micrograms.mL-1 respectively. The detection limit can be improved under optimized operating conditions. However, the spectral resolution is only 2.1 nm at the wavelength of 488 nm. Evidently, this poor spectral resolution would restrict the application of AOTF in atomic emission spectral analysis, unless an enhancing techniques is integrated in it.

Acoustics↗
PubMed1993

Assessing the severity of aortic valve stenosis by spectral analysis of cardiac murmurs (spectral vibrocardiography). Part II: Clinical aspects.

Assessing the severity of aortic stenosis remains an important clinical problem. The turbulent pressure fluctuations generated by the jet downstream of the stenotic valve produce vibrations in the aortic wall. These vibrations are transmitted through the chest to the skin surface, where they can be recorded as systolic ejection murmurs. The purpose of the present study was to estimate the transvalvular aortic pressure difference by spectral analysis of heart murmurs (spectral vibrocardiography). Forty-four patients with clinical signs of aortic stenosis underwent cardiac catheterization to measure the transvalvular pressure difference. In a double blind prospective study, precordial vibrations were measured prior to catheterization using a dedicated heart sound analyzer (Vibrocard 2000) to calculate the spectral ratio of murmur energy between 100-500 Hz and 20-500 Hz. Three different weighting filters were used to compensate for individual differences in the transthoracic attenuation of murmurs. The square root of the murmur energy ratio correlated linearly with the mean transvalvular pressure difference (r = 0.80, SEE = 13 mmHg) and with the peak transvalvular pressure difference (r = 0.81, SEE = 16 mmHg). The use of individual compensation filters improved the correlation. This study shows that it is possible to estimate the transvalvular pressure difference in patients with aortic valve stenosis by spectral analysis of heart murmurs. It is a fast, simple and cost effective technique, which requires less skill than conventional methods.

Adult↗
PubMed1995

[The spectral analysis of heart rate variability. A comparative study between nonparametric and parametric spectral analysis in short series].

OBJECTIVE: to compare parametric (AR) and non parametric (FFT) spectral analysis results obtained in 512 beats series. INTERVENTIONS: 104 healthy subjects with normal physical examination and electrocardiogram were studied. The Ecg was recorded at rest, with controlled breathing at 15 cycles/min., and sampled at 300 Hz. The spectral VLF, LF and HF were calculated with FFT algorithm. For the same series, an auto-regressive analysis (AR) with optimized choice of the order of the model (AIC criterion) have been computed, VLF, LF and HF components were identified by AR spectral decomposition. RESULTS: In both groups, athletes and sedentary, there were no statistically differences between VLF, LF, HF and LF/HF spectral indices computed by the two methods. CONCLUSION: the results suggest that with controlled breathing it does not seems to exist any advantage in the use of AR spectral analysis to compute spectral components of heart rate variability, which is much more laborious that fixed bands non parametric FFT analysis.

Adolescent↗
PubMed1999

Fetal magnetocardiogram recordings and Fourier spectral analysis.

Power spectral analysis of fetal magnetocardiogram (FMCG) data was evaluated in 64 pregnancies, using the non-invasive one channel superconducting quantum interference device (DC-SQUID), in order to investigate the power spectral amplitude distribution in the frequency range between 2 and 3 Hz. In all cases with normal and uncomplicated pregnancies, the data from the fetal heart and specifically the QRS complexes, were identifiable and unaffected by any maternal cardiac activity and furthermore the power spectral amplitudes, which varied between 120 and 350 fT/Hz, were directly related to gestational age.

Journal Article↗
PubMed1982

Diagnosis of carotid stenosis by bruit spectral analysis.

Bruit spectral analysis provides an improved noninvasive method of quantifying the severity of carotid stenosis, and the method of Lees estimates the residual luminal diameter. Although the analysis required is complex, the test can be easily performed using the Spectraview, a microcomputer-based device which records and processes the sound information. The present study evaluated 44 cervical bruits in patients and found that in 84 percent of the vessels the estimated luminal size was within 1 mm of the angiographic lumen. Although substantial experience is required to obtain the maximum diagnostic accuracy, work with the technique during 18 months indicates that is provides more information from arterial bruits thn traditional phonoangiography.

Aged↗
PubMed1998

[The measurement of baroreflex sensitivity in stress-induced hypertensive rats by spectral analysis].

Simultaneous spectral analysis of short-term systolic blood pressure variability (SBPV) and heart rate variability (HRV) was applied to determine the change of baroreflex sensitivity (BRS) in Sprague-Dawley (SD) rats. The modulus of the transfer function between fluctuations in systolic pressure and beat-to-beat interval would be an appropriate quantification of BRS. The animal experiment was performed on two groups of rats: normotensive rats and stress-induced hypertensive rats (SIHR). V1-receptor antagonist d(CH2)5Tye(Me) AVP of arginine vasopressin (AVP) was microinjected into intracerebroventricle. The results showed that, in base-line condition before administration, although the BRS in very low frequency band (0-0.035 Cycle/Beat, VLF), low frequency band (0.035-0.12 Cycle/Beat, LF), high frequency band (0.12-0.32 Cycle/Beat, HF) and total BRS(the sum of the three bands) were all decreased, the BRS in VLF(P < 0.05) and LF(P < 0.01) decreased significantly by statistics; and that, to the SIHR, the BRS in VLF was significantly lower after AVP-V1 administration than in base-line condition, while to normotensive rats, the BRS did not change. It indicates that the facilitating effect of AVP on the beroreflex in SIHR is mainly due to V1-receptor in central nerve system. In summary, the transfer function between SBPV and beat-to-beat interval fluctuation could be an index of BRS. This method can be developed for future clinical application.

Animals↗
PubMed1999

A comparison of period amplitude analysis and FFT power spectral analysis of all-night human sleep EEG.

Zero-cross and zero-derivative period amplitude analysis (PAA) data were compared with power spectral analysis (PSA) data obtained with the fast Fourier transform in all-night sleep EEG from 10 subjects. Although PAA zero-cross-integrated amplitude showed good agreement with PSA power in 0.3-2 Hz, zero-cross analysis appears relatively ineffective in measuring 2-4 Hz and above waves. However, PAA zero-derivative measures of peak-trough amplitude correlated well with PSA power in 2-4 Hz. Thus, while PAA appears able to measure the entire EEG spectrum, the analytic technique should be changed from zero cross to zero derivative at about 2 Hz in human sleep EEG. PAA and PSA both demonstrate robust and interrelated across-night oscillations in three frequency bands: delta (0.3-4 Hz); sigma (12-16 Hz); and fast beta (20-10 Hz). The frequencies between delta and sigma, and between sigma and fast beta, did not show clear across-night oscillations using either method, and the two methods showed lower epoch-to-epoch agreement in these intermediate bands. The causes of this reduced agreement are not immediately clear, nor is it obvious which method gives more valid results. We believe that the three strongly oscillating frequency bands represent fundamental properties of the human sleep EEG that provide important clues to underlying physiological mechanisms. These mechanisms are more likely to be understood if their dynamic properties are preserved and measured naturalistically rather than being forced into arbitrary sleep stages or procrustean models. Both PAA and PSA can be employed for such naturalistic studies. PSA has the advantages of applying the same analytic method across the EEG spectrum and rests on more fully developed theory. Combined zero-cross and zero-derivative PAA demonstrates EEG oscillations that closely parallel those observed with spectral power, and the PAA measures do not rely on assumptions about the spectral composition of the signal. In addition, both PAA techniques can measure the relative contributions of wave amplitude and incidence to total power: These waveform characteristics represent different biological processes and respond differentially to a wide range of experimental conditions.

Adult↗
PubMed1984

[Evaluation of the degree of carotid stenosis by spectral analysis of the Doppler signal. Comparison of the results of spectral analysis, angiography and anatomo-pathology].

With the single Doppler spectrum analysis, one can appreciate the degree of the carotid stenosis according to the importance of the haemodynamic disturbances induced by the stenosis. The purpose of our study is to show the possibilities and the limitations of this method. Spectrum disturbances were classified in 5 grades, each of them being related to the importance of the stenosis. The degree of stenosis has been evaluated by the C.W. Doppler spectrum analysis, the angiographies and the anatomical study of the endarteriectomies. We considered that we had a perfect concordance between the results of the different methods when the degree of stenosis measured on the angiographies or on the endarteriectomies was compatible with the spectrum analysis classification: grade I: stenosis inferior to 40% (in area), 23% (in diameter); grade II: stenosis ranging between 40 and 60% in area (23 and 40 in diameter); grade III: stenosis of 60 to 75% in area (40 to 50% in diameter) and of particular shape (extended plaque); grade IV: stenosis ranging between 60 and 90% in area (40 to 70% in diameter); grade V: stenosis higher than 90% in area (70% in diameter). The confrontation of spectrum analysis and angiographic date concerns 58 bifurcations. We got a perfect correlation in 93% of the cases. The confrontation of the spectrum analysis method and the anatomical study of the endarteriectomies concerns 38 bifurcations. We got a perfect correlation in 92% of the cases. The appreciation of the carotid stenosis degree is now performed in routine at the Hospital. For some patients, the endarteriectomy has been decided from the clinical and the spectrum analysis data, and an electro-encephalogram with compression. However these date are generally completed with an angiography with venous punction.

Carotid Arteries↗
PubMed1993

Assessing the severity of aortic valve stenosis by spectral analysis of cardiac murmurs (spectral vibrocardiography). Part I: Technical aspects.

Assessing the severity of aortic stenosis remains an important clinical problem. The turbulent pressure fluctuations generated by the confined jet down-stream of the stenotic valve produce vibrations in the aortic wall. These vibrations are transmitted through the chest to the skin surface, where they can be measured as systolic ejection murmurs. The purpose of the present study was to find the relationship between the severity of aortic valve stenosis and the frequency content of the precordial systolic murmurs, and to evaluate the transthoracic attenuation of murmurs and its variation from patient to patient. Twenty-four patients with clinical signs of aortic stenosis underwent cardiac catheterization to measure the peak transvalvular pressure difference. The mean energy density spectrum of the measured systolic precordial murmurs was calculated and the murmur energy ratio between 100-500 Hz and 20-500 Hz was correlated to the transvalvular pressure difference. The inter-individual variability of the transthoracic attenuation was evaluated by calculating the transthoracic transfer function from simultaneous measurements of precordial vibrations at the second right intercostal space and intravascular recordings of high frequency pressure fluctuations in the ascending aorta. The transvalvular pressure difference and the square root of the murmur energy ratio correlated well (r = 0.81, SEE = 27 mmHg). In the frequency range from 10-500 Hz the transthoracic transfer function could be modelled by a low-pass filter function with a low frequency attenuation of 36 +/- 7.7 dB (mean +/- SD), a corner frequency of 26 +/- 12 Hz and an attenuation slope of -29 +/- 7.9 dB/decade. Spectral analysis of systolic murmurs might be an attractive non-invasive addition to the array of techniques already in use for assessing the severity of aortic stenosis. It is a simple and cost effective technique, and requires less skill and time for data analysis than conventional methods.

Adult↗
PubMed2002

A comparison between analysis time and inter-analyst reliability using spectral analysis of kinematic data and posture classification.

This study compares the time needed to analyze data and the inter-analyst variability using observational posture classification vs. spectral analysis of upper limb kinematic measurements made using an electrogoniometer for selected industrial jobs. Eight trained analysts studied four jobs using both methods. An incomplete fixed block experimental design was used, whereby each analyst used one method for each job. The four jobs included (1) punch press operation, (2) packaging, (3) parts hanging, and (4) construction vehicle operation. The posture classification analysis method involved visually classifying tipper extremity joint angles into specific zones relative to the range of motion for every one-third second (10 frames) of videotape. Spectral analysis required the analysts to identify cycle break points. The electrogoniometer signals were synchronized with each cycle, and power spectra for each joint were computed. The average difference in RMS joint deviation among analysts was 0.9 (SD = 0.61 degrees) for spectral analysis and 7.1 (SD = 2.53 degrees) for posture classification. The average difference in mean joint angle was 0.8 (SD = 0.59 degrees) for spectral analysis and 11.4 (SD = 1.58 degrees) for posture classification. Repetition frequency differed an average of 0.05 Hz (SD = 0.054 Hz) for spectral analysis and 0.07 Hz (SD = 0.058 Hz) for posture classification. Posture classification took a factor of 6.3 more time than cycle break point assignment for spectral analysis. Even considering the additional time needed for sensor attachment for direct measurement, posture classification took an average factor of 1.29 more time than spectral analysis using electrogoniometer data.

Biomechanical Phenomena↗
PubMed1998

A simplified method for the quantitative analysis of 99Tc(m)-GSA liver scintigraphy using spectral analysis.

The aim of this study was to develop a simplified method for quantitative analysis of liver scintigraphy with 99Tc(m)-diethylenetriamine pentaacetic acid-galactosyl-human serum albumin (GSA) using spectral analysis. Dynamic liver scintigraphy using GSA was performed in three normal volunteers and 19 patients with chronic liver disease. Dynamic data were obtained with a gamma camera for 30 min after the injection of approximately 185 MBq GSA. The rate constant for the liver uptake of GSA from the blood (Ku, min(-1)), total excretion rate (Ke, min(-1)) and non-specific volume of distribution (Vh) were obtained by spectral analysis. Vh was defined as the volume in the liver region of interest (ROI) occupied by GSA which was in equilibrium with that in the blood. It should be noted that Vh had no units, since the counts in both the liver and heart ROIs were normalized by scan length to obtain counts pixel(-1) min(-). For comparison, compartmental analysis was also performed. A receptor index (LHL15) was calculated by dividing the radioactivity of the liver ROI by that of the liver plus heart ROIs 15 min post-injection. The Ku values obtained by spectral analysis (y) agreed well with those obtained by compartmental analysis (x) (y = 0.953x - 0.013, r = 0.992, S.E.E. = 0.016 min(-1)). The Ke and Vh values obtained by spectral analysis (y) correlated significantly with those obtained by compartmental analysis (x) (y = 1.149x - 0.016, r = 0.826, S.E.E. = 0.017 min(-1) for Ke; y = 1.191x + 0.044, r = 0.975, S.E.E. = 0.021 for Vh). The Ku values obtained by spectral analysis decreased as the severity of liver disease progressed, and were non-linearly related to the LHL15 values, suggesting that Ku is more sensitive to liver damage than LHL15, especially in the early stages of liver damage. These results suggest that spectral analysis applied to dynamic liver scintigraphy with GSA provides a simple, non-invasive and useful tool for the quantitative evaluation of liver function.

Hepatitis↗
PubMed1984

Detection of technical error during arterial surgery by pulsed Doppler spectral analysis.

Pulsed Doppler spectral analysis of midstream flow was compared with arteriography in 90 patients following carotid endarterectomy (N = 60) or lower-extremity bypass grafting (N = 30) for the detection of unsuspected technical error. Spectral changes in the velocity waveform indicating flow disturbance were identified in the endarterectomy or anastomotic sites of 11 patients (12%). All were associated with an anatomic defect apparent on arteriography. The revision of major defects in six patients (7%) corrected the flow disturbance. The absence of flow disturbance in 79 patients (88%) predicted a technically satisfactory arterial reconstruction. Intraoperative assessment by pulsed Doppler spectral analysis is a noninvasive, rapid, and accurate method for detecting technical errors during arterial surgery. The high sensitivity of this method makes it suitable for use as a screening test, resulting in the selective use of operative arteriography.

Angiography↗
PubMed1988

Quantification of cardiovascular instability in premature infants using spectral analysis of waveforms.

Spectral analysis was applied to blood pressure and cerebral blood flow velocity recordings in premature infants with respiratory distress in order to quantify respiration-induced cardiovascular variability. Aortic blood pressure was transduced via an umbilical arterial catheter and cerebral blood flow velocity measured in the anterior cerebral artery using a 10 MHz continuous wave Doppler velocimeter in 16 infants less than or equal to 32 wk gestational age. Spectral analysis of the resulting waveforms revealed heart rate and respiratory rate components whose relative amplitudes (heart rate/respiratory rate amplitude ratio) represent an index of that component of variability induced by respiratory events. The mean (heart rate/respiratory rate amplitude) ratio was 47.2 in spontaneously breathing infants and rose to 165.9 in infants who were ventilated during muscle paralysis (p = 0.0003). Cerebral blood flow velocity recordings showed R components in only 22 of 38 simultaneous recordings. This method can be used to quantify respiration-induced cardiovascular variability and its response to therapy, and may provide a means of identifying infants at risk from brain injury due to an inability to regulate cerebral blood flow.

Blood Flow Velocity↗
PubMed1977

EEG operant conditioning in a monkey model: II. EEG spectral analysis.

EEG power spectral analysis was studied from 14 (alumina-gel) chronically epileptic, undrugged monkeys during an EEG operant conditioning experiment. The composite profile of the average epileptic monkey shows the majority of power to be below 10 Hz. Because of the large variance in the data, no significant changes in the EEG power spectra could be detected as a function of conditioning. The possible reasons for this large variance are discussed. Hypothesis from previous human "biofeedback" studies would allow the prediction that those frequencies corresponding to the mu and sensory-motor-rhythm should negatively covary with seizure frequency. Data from this study did not support such assertions. The method of using spectral analysis for quantifying changes in the EEG which covary with operant conditioning is evaluated.

Animals↗
PubMed1995

Quantitative assessment of the autonomic nervous system activities during atropine-induced bradycardia by heart rate spectral analysis.

Using power spectral analysis of heart rate fluctuation, autonomic nervous system activities in bradycardia appearing in the initial phase of atropine administration were evaluated quantitatively in 16 healthy females. Atropine sulfate (10 micrograms/kg), diluted in 100 ml of 0.9% NaCl solution, was intravenously infused at a rate of 0.5 micrograms/kg per min. Electrocardiograms were sampled for 4 min for later analysis before and 0, 5, 10, 15 and 20 min after initiation of atropine infusion. Powers of low (LFC, 0.05-0.15 Hz) and high-frequency (HFC, 0.15-0.4 Hz) components in the power spectrum of R-R interval variations, and the LFC/HFC ratio were determined at each sampling point. HFC power at 0-4 min increased from 1.11 +/- 0.18 ms2 (mean) of baseline value to 1.37 +/- 0.19 ms2 (P < 0.05). The next 5-9-min value of 1.48 +/- 0.14 ms2 was the maximum, and the amount of atropine infused by 9 min was 4.5 micrograms/kg. The HFC powers following this point decreased. The 20-24-min value after 10 micrograms/kg atropine decreased to 0.21 +/- 0.03 ms2 (P < 0.01), which was lower than the previous 15-19-min value of 0.36 +/- 0.04 ms2 (P < 0.01). The LFC/HFC ratios showed no significant change for the initial 9 min of the atropine infusion. However, these ratios at 15-19 min and 20-24 min were increased from 0.50 +/- 0.04 (mean) of baseline value to 0.75 +/- 0.09 and 0.81 +/- 0.09, respectively (P < 0.01). A transient vagotonic state after atropine administration, followed by the well-known vagolytic state, was quantitatively detected by non-invasive spectral analysis of heart-rate fluctuation.

Adolescent↗