Linda R. Davrath

Coherence analysis between respiration and heart rate variability using continuous wavelet transform.

The continuous wavelet transform (CWT) is specifically efficient in the analysis of transient and non-stationary signals. As such, it has become a powerful candidate for time–frequency analysis of cardiovascular variability. CWT has already been established as a valid tool for the analysis of single cardiovascular signals, providing additional insights into the autonomous nervous system (ANS) activity and its control mechanism. Intercorrelation between cardiovascular signals elucidates the function of ANS central control and the peripheral reflex mechanisms. Wavelet transform coherence (WTC) can provide insight into the transient linear order of the regulatory mechanisms, via the computation of time–frequency maps of the time-variant coherence. This paper presents a framework for applying WTC for quantitative analysis of coherence in cardiovascular variability research. Computer simulations were performed to estimate the accuracy of the WTC estimates and a method for determining the coherence threshold for specific frequency band was developed and evaluated. Finally, we demonstrated, in two representative situations, the dynamic behaviour of respiration sinus arrhythmia through the analysis of the WTC between heart rate and respiration signals. This emphasizes that CWT and its application to WTC is a useful tool for dynamic analysis of cardiovascular variability.

Individual time-dependent spectral boundaries for improved accuracy in time-frequency analysis of heart rate variability

Heart rate variability (HRV) is a major noninvasive technique for evaluating the autonomic nervous system (ANS). Use of time-frequency approach to analyze HRV allows investigating the ANS behavior from the power integrals, as a function of time, in both steady-state and non steady-state. Power integrals are examined mainly in the low-frequency and the high-frequency bands. Traditionally, constant boundaries are chosen to determine the frequency bands of interest. However, these ranges are individual, and can be strongly affected by physiologic conditions (body position, breathing frequency). In order to determine the dynamic boundaries of the frequency bands more accurately, especially during autonomic challenges, we developed an algorithm for the detection of individual time-dependent spectral boundaries (ITSB). The ITSB was tested on recordings from a series of standard autonomic maneuvers with rest periods between them, and the response to stand was compared to the known physiological response. A major advantage of the ITSB is the ability to reliably define the mid-frequency range, which provides the potential to investigate the physiologic importance of this range.

High-frequency QRS analysis improves the specificity of exercise ECG testing in women referred for angiography

BACKGROUND Exercise ECG testing in women for the diagnosis of coronary artery disease (CAD) has a higher false-positive rate compared to men. Consequently, women referred for coronary angiography following a positive exercise test often have normal coronary arteries or non-obstructive lesions. Analysis of the high-frequency components of the QRS complexes (HFQRS) has been reported to provide a sensitive means of detecting myocardial ischemia, independent of gender. The aim of the present study was to prospectively test the diagnostic performance of HFQRS and conventional exercise ECG in detecting stress-induced ischemia in women referred for coronary angiography. METHODS The study included 113 female patients (age 64 ± 9 years) referred for non-urgent angiography. Patients performed a symptom-limited treadmill exercise test prior to angiography. High-resolution ECG was acquired during the test and used for both HFQRS and conventional ST-segment analyses. HFQRS diagnosis was determined by computerized analysis, measuring the stress-induced reduction in HFQRS intensity. The diagnostic performance of HFQRS, ST-segment analysis and clinical interpretation of the exercise test were compared, using angiography as a gold standard. RESULTS HFQRS provided sensitivity of 70% and specificity of 80% for detection of angiographically significant coronary obstruction (≥ 70% stenosis in a single vessel or ≥ 50% in the left main artery). HFQRS was more specific than exercise ECG test (80% vs. 55%, P<.005), as well as more accurate (76% vs. 62%, P<.01). The number of ECG leads with ischemic HFQRS response correlated with the severity of CAD. HFQRS was highly specific (93%) in patients who achieved their age-predicted target heart rate, and retained its diagnostic accuracy in subgroups of patients with resting ECG abnormalities or inconclusive exercise ECG. CONCLUSIONS HFQRS analysis, as an adjunct technology to exercise stress testing, may improve the diagnostic value of the ECG, and reduce the number of unnecessary imaging and invasive procedures.

Wavelet transform coherence estimates in cardiovascular analysis: Error analysis and feasibility study

Wavelet transform coherence (WTC) can provide insight into the transient linear order of the regulatory mechanisms, via the computation of time-frequency maps of the time-variant coherence. This paper presents a framework for applying WTC for quantitative analysis of coherence in cardiovascular variability research. Computer simulations were performed to estimate the accuracy of the WTC estimates and a method for determining the coherence threshold for specific frequency band was developed and evaluated. Finally, we demonstrated, in two representative situations, the dynamic behavior of RSA through the analysis of the WTC between HR and respiration signals. This emphasizes that continuous wavelet transform (CWT) and its application to WTC is a useful tool for dynamic analysis of cardiovascular variability.

Ischemia monitoring by analysis of depolarization changes

Myocardial ischemia causes changes in the depolarization phase of the cardiac cycle, which can be quantified by analysis of high-frequency QRS components (HFQRS). We introduce a novel HFQRS analysis technology and evaluate its performance in monitoring transient ischemic episodes in patients hospitalized due to chest pain. Continuous monitoring by high-resolution 12-lead ECG was performed in 43 patients admitted to a chest pain unit, followed by cardiac imaging. Indices of HFQRS based on ischemia-specific morphological changes and conventional ST segment levels were extracted from signal-averaged ECG. HFQRS indices were positive for 5 of 10 patients that were diagnosed with coronary artery disease, while ST analysis was negative for all patients. The severity of the HFQRS indices was directly related to the likelihood of ischemic events. HFQRS is a promising technology for monitoring and early detection of acute coronary syndrome.

Cluster headache patients have normal circadian and sleep time autonomic nervous system function

Cluster headache (CH) is a rare form of primary headache of neurovascular origin causing severe pain attacks associated with autonomic changes. Attacks are more likely to occur during sleep. Time-frequency decomposition (TFD) of instantaneous heart rate variability (HRV) is widely accepted as a non-invasive tool of investigation of autonomic nervous function and was applied in the present study. Our goal was to estimate the autonomic features of CH patients and their connection to sleep. The study included 20 subjects belonging to 3 groups: (a) CH (active headache attacks, N=7); (b) Normal control (C, N=6); (c) patients with CH during a quiet period (QP, N=7). The study revealed similar circadian behaviour of all HRV variables and of the HR in all groups indicating normal changes in central autonomic function between daytime and sleep in CH. Increased overall VLF power in CH compared to normal subjects suggests increased vasomotor activity during active headache periods only

The cost-effectiveness of stress testing using high-frequency QRS analysis

Exercise ECG testing (EET) has limited accuracy in diagnosing coronary heart disease (CHD). High-frequency QRS (HFQRS) analysis is a new technology that improves the diagnostic performance of EET. We analysed the potential economic and healthcare benefits of HFQRS technology. The changes in utilisation of cardiac imaging tests and expenditures on medical treatment were studied using probabilistic models. A decision tree model was used to assess the expected costs of CHD workup and a prognostic Markov model was used to estimate long-term consequences. The models indicated that compared with EET, HFQRS-based workup results in a reduction in superfluous imaging tests. Analysis of long-term changes indicated a reduction in adverse events among CHD patients, with a decrease in overall medical costs and an increase in quality-adjusted life years. HFQRS technology is a promising tool for diagnosing CHD that may reduce medical costs while providing favourable prognostic outcomes.

Heart rate recovery after exercise: a study by wavelet analysis

The reduction in heart rate (HR) during the first min of recovery immediately after a maximal exercise stress test (GXT) has recently been found to be a powerful and independent predictor of mortality. A modified continuous wavelet transform (CWT) designed in our lab, is used to perform time-dependent spectral analysis during the non steady-state conditions created by GXT. This method provides dynamic measures of the low frequency (LF) and high frequency (HF) peaks, associated with autonomic activity. A group of 18 patients underwent GXT using the Bruce Protocol. Five of 18 patients demonstrated pathologic delta HR (/spl les/ 18 bpm), while 13 patients displayed normal delta HR (/spl Gt/ 18 bpm). Patients with pathologic delta HR displayed significantly (p < 0.05) lower HF fluctuations at one-min post exercise than did the controls. Attenuated delta HR upon recovery from GXT is indeed associated with abnormal vagal function, as assessed by CWT.

High-frequency QRS analysis in the evaluation of chest pain in the emergency department

OBJECTIVES High frequency QRS (HFQRS) analysis has been shown to be an accurate marker for myocardial ischemia. Our objective was to test the use of HFQRS in diagnosing ACS in the emergency department. METHODS 324 patients presenting to the ED with chest pain were enrolled. Resting ECG was recorded and later analyzed by an HFQRS algorithm. Results were compared to the conventional ECG diagnosis by 3 independent interpretations: treating physician, expert cardiologist and an automated computer program. RESULTS The HFQRS analysis demonstrated improved sensitivity (67.5%) for the NSTE-ACS group compared to the human interpreters (59.7% and 53.2% for the treating physician and cardiologist respectively) with similar specificity. The automatic program had significantly lower sensitivity (31%) with a higher specificity (77%). CONCLUSIONS HFQRS which has shown great promise in diagnosing stable CAD may also be helpful in the ED for diagnosing ACS.

Early detection of essential hypertension by time-frequency analysis

Hypertension affects approximately 25% of adults in industrialized countries and contributes significantly to morbidity and mortality from cardiovascular diseases. Young adult, normotensive offspring of one hypertensive parent (KHT, n = 12) and normotensive offspring of two normotensive parents (YN, n = 14) participated ECG, continuous blood pressure, and respiration were recorded Time-frequency decomposition of these signals was performed by a Continuous Wavelet Transform. During change in posture (CP), KHT demonstrated a significantly greater increase in the low frequency fluctuations in heart rate (HR) than YN, indicating enhanced sympathetic involvement in the HR response to CP. Upon recovery from Handgrip, vagal reactivation was more sluggish in KHT These results indicate possible malfunctions in both branches of autonomic control in individuals at increased risk of hypertension.