Wavelet analysis of high-resolution signal-averaged ECGs in postinfarction patients

Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms

The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar. However, by adopting such representations, state-of-the-art approaches usually rely on the magnitude information alone, while the important phase information is often neglected. Motivated by these considerations, the focus of this paper is aimed at investigating the effect of fusing the magnitude and phase of the continuous wavelet transform (CWT), known as the scalogram and phasogram, respectively. Scalograms and phasograms are fused in a simple CNN-based architecture by using several fusion strategies, which fuse the information in the input layer, some intermediate layers, or in the output layer. Numerical results evaluated on the PhysioNet MIT-BIH Arrhythmia database show the effectiveness of the proposed ideas. Although a simple architecture is used, their competitiveness is high compared to other state-of-the-art approaches, by obtaining an overall accuracy of about 98.5% and sensitivity and specificity of 98.5% and 95.6%, respectively.

Association of beat-to-beat P-wave analysis index to the extent of left atrial low-voltage areas in patients with paroxysmal atrial fibrillation

BACKGROUND

Left atrial (LA) fibrosis has been shown to be associated with atrial fibrillation (AF) recurrence. Beat-to-beat (B2B) index is a non-invasive classifier, based on B2B P-wave morphological and wavelet analysis, shown to be associated with AF incidence and recurrence. In this study, we tested the hypothesis that the B2B index is associated with the extent of LA low-voltage areas (LVAs) on electroanatomical mapping.

METHODS

Patients with paroxysmal AF scheduled for pulmonary vein isolation, without evident structural remodeling, were included. Pre-ablation electroanatomical voltage maps were used to calculate the surface of LVAs (<0.5 mV). B2B index was compared between patients with small versus large LVAs.

RESULTS

35 patients were included (87% male, median age 62). The median surface area of LVAs was 7.7 (4.4-15.8) cm2 corresponding to 5.6 (3.3-12.1) % of LA endocardial surface. B2B index was 0.57 (0.52-0.59) in patients with small LVAs (below the median) compared to 0.65 (0.56-0.77) in those with large LVAs (above the median) (p = 0.009). In the receiver operator characteristic curve analysis for predicting large LVAs, the c-statistic was 0.75 (p = 0.006) for B2B index and 0.81 for the multivariable model including B2B index (multivariable p = 0.04) and P-wave duration.

CONCLUSION

In patients with paroxysmal AF without overt atrial myopathy, B2B P-wave analysis appears to be a useful non-invasive correlate of low-voltage areas-and thus fibrosis-in the LA. This finding establishes a pathophysiological basis for B2B index and its potential usefulness in the selection process of patients who are likely to benefit most from further invasive treatment.

P-wave duration as a marker of atrial remodeling in patients referred to ablation for atrial fibrillation: A new stratification tool emerging?

Ablation of atrial fibrillation is one of the most widely applied invasive procedures in cardiovascular medicine, and populations with atrial fibrillation continuously rise. Recurrence rates are, however, consistently high, even in patients without severe comorbidities. Robust stratification algorithms to distinguish patients suitable for ablation are generally lacking. This is a fact caused by the inability to incorporate evidence of atrial remodeling and fibrosis, e.g., atrial remodeling, in the decision pathways. Cardiac magnetic resonance is a powerful tool in identifying fibrosis; however, it is costly and not routinely used. Electrocardiography has been generally underutilized in clinical practice during pre-ablative screening. One of the characteristics of the electrocardiogram that can give us valuable data depicting the existence and the extent of atrial remodeling and fibrosis is the duration of the P-wave. Currently, many studies support the implementation of P-wave duration in the routine practice of patient evaluation as a surrogate marker of existing atrial remodeling, that in turn predicts recurrence after ablation of atrial fibrillation. Further research is guaranteed to establish this electrocardiographic characteristic in our stratification quiver.

P-Wave Beat-to-Beat Analysis to Predict Atrial Fibrillation Recurrence after Catheter Ablation

The identification of patients prone to atrial fibrillation (AF) relapse after catheter ablation is essential for better patient selection and risk stratification. The current prospective cohort study aims to validate a novel P-wave index based on beat-to-beat (B2B) P-wave morphological and wavelet analysis designed to detect patients with low burden AF as a predictor of AF recurrence within a year after successful catheter ablation. From a total of 138 consecutive patients scheduled for AF ablation, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained. Univariate analysis revealed that patients with higher B2B P-wave index had a two-fold risk for AF recurrence (HR: 2.35, 95% CI: 1.24–4.44, p: 0.010), along with prolonged P-wave, interatrial block, early AF recurrence, female gender, heart failure history, previous stroke, and CHA₂DS₂-VASc score. Multivariate analysis of assessable predictors before ablation revealed that B2B P-wave index, along with heart failure history and a history of previous stroke or transient ischemic attack, are independent predicting factors of atrial fibrillation recurrence. Further studies are needed to assess the predictive value of the B2B index with greater accuracy and evaluate a possible relationship with atrial substrate analysis.

Temporally stable frequency mapping using continuous wavelet transform analysis in patients with persistent atrial fibrillation

INTRODUCTION

Dominant frequency (DF) derived from fast Fourier transform (FFT) analysis has failed to guide atrial fibrillation (AF) ablation since it cannot guarantee temporal stability. Continuous wavelet transform (CWT) analysis is another frequency analysis that can show the temporal stability of a frequency.

METHODS AND RESULTS

Forty-four consecutive patients with persistent AF (PeAF) underwent pulmonary vein (PV) isolation (PVI) as the first-time catheter ablation. The PVs and left atrium were mapped and electrograms (EGMs) were recorded for 30 seconds at each site. Pseudo-frequency (PF) and coefficient of variation (CV) were calculated by CWT analysis. A PF with CV ≤ 10 was defined as a temporally stable PF (sPF). DF was also calculated by traditional FFT analysis from the first 5 seconds of the recorded EGMs. The highest sPF was shown inside the PVs in 20 patients (PV group), and at the non-PV sites in 24 patients (non-PV group). During the follow-up period of 15.3 ± 4.4 months, the ablation success rate in the PV group was significantly higher than that in the non-PV group (90% vs. 62%, P = 0.023). The location of the highest DF did not have a significant effect on ablation success rate between inside the PVs and at the non-PV sites.

CONCLUSION

PVI results for PeAF were significantly worse for patients with highest sPF at the non-PV sites compared to patients with highest sPF sites inside the PVs. CWT analysis during AF could be used to verify whether PVI alone is sufficient for the first-time catheter ablation in patients with PeAF.

QRS analysis using wavelet transformation for the prediction of response to cardiac resynchronization therapy: a prospective pilot study

BACKGROUND

Wider QRS and left bundle branch block morphology are related to response to cardiac resynchronization therapy (CRT). A novel time-frequency analysis of the QRS complex may provide additional information in predicting response to CRT.

METHODS

Signal-averaged electrocardiograms were prospectively recorded, before CRT, in orthogonal leads and QRS decomposition in three frequency bands was performed using the Morlet wavelet transformation.

RESULTS

Thirty eight patients (age 65±10years, 31 males) were studied. CRT responders (n=28) had wider baseline QRS compared to non-responders and lower QRS energies in all frequency bands. The combination of QRS duration and mean energy in the high frequency band had the best predicting ability (AUC 0.833, 95%CI 0.705-0.962, p=0.002) followed by the maximum energy in the high frequency band (AUC 0.811, 95%CI 0.663-0.960, p=0.004).

CONCLUSIONS

Wavelet transformation of the QRS complex is useful in predicting response to CRT.

Time frequency power profile of QRS complex obtained with wavelet transform in spontaneously hypertensive rats

We evaluated whether frequency analysis could detect the development of interstitial fibrosis in rats. SHR/Izm and age-matched WKY/Izm were used. Limb lead II electrocardiograms were recorded. Continuous wavelet transform (CWT) was applied for the time-frequency analysis. The integrated time-frequency power (ITFP) between QRS complexes was measured and compared between groups. The ITFP at low-frequency bands (≤125Hz) was significantly higher in SHR/Izm. The percent change of ITFP showed the different patterns between groups. Prominent interstitial fibrosis with an increase in TIMP-1 mRNA expression was also observed in SHR/Izm. These results were partly reproduced in a computer simulation.

A new approach for the comparison of conduction abnormality between arrhythmogenic right ventricular cardiomyopathy/dysplasia and Brugada syndrome

BACKGROUND

Right ventricular outflow tract ventricular tachycardia (RVOT-VT), arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/ARVD), and Brugada syndrome (BrS) were characterized by arrhythmias originating in the right ventricle, and the pathophysiologic mechanism underlying these arrhythmias has not been fully understood.

METHODS

This study consisted of 40 subjects, including 20 patients with RVOT-VT, 10 patients with BrS, and 10 ARVD patients. The parameters on the signal-averaged electrocardiography (ECG) and the frequency components recorded from the wavelet-transformed ECG were compared between the three groups. Late potentials were positive in none of the patients with RVOT-VT, seven of the patients with BrS, and all of ARVD patients.

RESULTS

In Brugada and ARVD patients, the power of high-frequency components (80-150 Hz) was developed to a greater extent than in RVOT-VT patients. In the power analysis of the high-frequency components between BrS and ARVD, the frequency showing the greatest power was significantly higher in ARVD patients than that in BrS patients (145.4 ± 27.9 Hz vs 81.7 ± 19.9 Hz, P < 0.01).

CONCLUSIONS

High-frequency components were developed in ARVD and BrS, but not in RVOT-VT. The frequency levels showing high power by wavelet analysis obviously differ between ARVD and BrS. Wavelet analysis may provide new insight into unsolved mechanisms in arrhythmogenic right heart disease.

High-frequency powers hidden within QRS complex as an additional predictor of lethal ventricular arrhythmias to ventricular late potential in post-myocardial infarction patients

BACKGROUND

Ventricular late potentials (VLPs) have been known to be a predictor of lethal ventricular arrhythmias (L-VAs); however, detection of other arrhythmogenic signals within the QRS complex remains obscure.

OBJECTIVE

The aim of this study was to evaluate whether abnormal intra-QRS high-frequency powers (IQHFP) within the QRS complex become a new predictor of L-VAs in addition to VLPs.

METHODS

Both 12-lead electrocardiograms (ECG) and VLPs were recorded from 142 subjects, including 37 patients without heart diseases, 97 patients post-myocardial infarction (MI), and 45 post-MI patients with L-VAs. Time-frequency analysis of ECG (leads V(1) or II) using wavelet transform with the Morlet function was performed. After the time-frequency powers were calculated, the ratios of the peak of signal power during the QRS complex in high-frequency bands against the peak power at 80 Hz (b/a ratio; P100, P150, P200, P250, or P300Hz/P80Hz) were measured. Abnormal IQHFP was defined when the b/a ratio exceeded the optimal cut-off values estimated by receiver-operator characteristic curves.

RESULTS

The combination of abnormal IQHFP appearing at 200, 250, and 300 Hz with positive VLPs increased the sensitivity for prediction of L-VAs from 53.3% by VLPs to 89.5%, and the negative predictive value from 74.7% by VLPs to 87.7%.

CONCLUSION

The combined use of VLPs and IQHFP hidden within the QRS complex improved the prediction of L-VAs in post-MI patients.

Levels of complexity in scale-invariant neural signals

Many physical and physiological signals exhibit complex scale-invariant features characterized by 1/f scaling and long-range power-law correlations, indicating a possibly common control mechanism. Specifically, it has been suggested that dynamical processes, influenced by inputs and feedback on multiple time scales, may be sufficient to give rise to 1/f scaling and scale invariance. Two examples of physiologic signals that are the output of hierarchical multiscale physiologic systems under neural control are the human heartbeat and human gait. Here we show that while both cardiac interbeat interval and gait interstride interval time series under healthy conditions have comparable 1/f scaling, they still may belong to different complexity classes. Our analysis of the multifractal scaling exponents of the fluctuations in these two signals demonstrates that in contrast to the multifractal behavior found in healthy heartbeat dynamics, gait time series exhibit less complex, close to monofractal behavior. Further, we find strong anticorrelations in the sign and close to random behavior for the magnitude of gait fluctuations at short and intermediate time scales, in contrast to weak anticorrelations in the sign and strong positive correlation for the magnitude of heartbeat interval fluctuations-suggesting that the neural mechanisms of cardiac and gait control exhibit different linear and nonlinear features. These findings are of interest because they underscore the limitations of traditional two-point correlation methods in fully characterizing physiological and physical dynamics. In addition, these results suggest that different mechanisms of control may be responsible for varying levels of complexity observed in physiological systems under neural regulation and in physical systems that possess similar 1/f scaling.

Fatigue-induced changes in muscle fiber action potentials estimated by wavelet analysis

We aimed to investigate fatigue-induced changes in the spectral parameters of slow (SMF) and fast fatigable muscle fiber (FMF) action potentials using discrete wavelet (DWT) and fast Fourier (FFT) transforms. Intracellular potentials were recorded during repetitive stimulation of isolated muscle fibers immersed in Ca(2+)-enriched medium, while extracellular potentials were obtained from muscle fibers pre-exposed to electromagnetic microwaves (MMW, 2.45 GHz, 20 mW/cm(2)). The changes in the frequency distribution of the action potentials during the period of uninterrupted fiber activity were used as criteria for fatigue assessment. The wavelet coefficients' changes in the calculated frequency scales demonstrated a contribution of the increased [Ca(2+)](0) to an earlier compression of the frequency spectrum towards lower ranges. Root mean square (RMS) analysis of the wavelet coefficients calculated from SMF potentials showed a reduction of the higher frequencies (scale 1) by 90% in elevated [Ca(2+)](0) vs. 55% in controls and an increase of low frequencies (scale 5) by 323% vs. 187%, respectively. For FMF potentials a decrease of 71% vs. 59% for high frequencies (scale 1, elevated [Ca(2+)](0) vs. control) and an increase of 386% vs. 295% in scale 5, respectively, were observed. MMW pre-exposure resulted in increased muscle fiber resistance to fatigue. The fatigue-induced decrease of potential high frequencies (SMF: 59% vs. 96%, MMW vs. control; FMF: 30% vs. 92%, respectively), and the increase of low frequencies (SMF: 200% vs. 207%, MMW vs. control; FMF: 93% vs. 314%, respectively) were significantly smaller and delayed in exposed muscle fibers. Data from RMS analysis indicate that DWT provides a reliable method for estimation of muscle fatigue onset and progression.

Estimation of microinhomogeneity of conduction impairment by wavelet analysis during early phase of myocardial ischemia in pigs

Ventricular fibrillation (VF) is most frequent in the very early phase in acute coronary occlusion, and is triggered by the re-entrant mechanism in this phase. An inhomogeneous conduction in the ischemic myocardium would be substrates for re-entry. The aim of this study was to examine the relationship between the severity of irregularities of the QRS complex and VF. Eleven pigs were analyzed, and the heart was fixed in the pericardial cradle. Ag-AgCl bipolar electrodes were fixed on the epicardium in ischemic and non-ischemic regions. The proximal portion of the left anterior descending coronary artery was occluded for one hour. Electrocardiograms (ECGs) were continuously recorded on a magnetic tape, and wavelet analysis was performed on signal-averaged ECG (25 beats) every 60 sec after the experiment. The number of local maxima (N) and the duration between the first and the last local maximum (D) were automatically measured. N and D significantly increased in the ischemic area, but not in the non-ischemic area. N and D increased approximately twofold just before the occurrence of VF in 8 fibrillated pigs (p<0.01, each). There were significant positive linear relationships between the rate of increase in N and D to VF and basal heart rate before coronary occlusion (r=0.90, p <0.01 in N, r=0.84, p <0.01 in D at 160 Hz). These results suggest that there would be a threshold inhomogeneous conduction for the occurrence of VF and an increase in heart rate would accelerate the inhomogeneous conduction in acute myocardial ischemia.

High-frequency potentials developed in wavelet-transformed electrocardiogram as a novel indicator for detecting Brugada syndrome

BACKGROUND

A reliable alternative method for detecting Brugada syndrome is desirable because the diagnosis of Brugada syndrome using 12-lead ECG is not optimal.

OBJECTIVES

The purpose of this study was to assess the usefulness of the wavelet-transformed ECG in detecting Brugada syndrome.

METHODS

The study consisted of 15 patients with Brugada syndrome and 15 healthy subjects (control group). The parameters on the signal-averaged ECG and the frequency components recorded from the wavelet-transformed ECG were compared between the two groups. Measurements were repeated after pilsicainide infusion in the two groups of patients, after an isoproterenol infusion following pilsicainide injection, and after administration of cilostazol in the group of patients with Brugada syndrome.

RESULTS

The positive rate of late potentials was 80% in the Brugada syndrome group and 0% in the control group (P <.01). The high-frequency components (80-150 Hz) were developed in the Brugada syndrome group to a greater extent than in the control group, but the low-frequency components (10-50 Hz) did not differ (mean peak power at 80 Hz; 713 +/- 36 vs 488 +/- 60, P <.001). After pilsicainide injection, high-frequency components significantly increased in both groups. However, after isoproterenol and cilostazol administration, high-frequency components significantly decreased but remained higher than in the control group (80 Hz; 655 +/- 40 vs 488 +/- 60, P <.001). The sensitivity of the development of high-frequency components in detecting Brugada syndrome was higher than that of signal-averaged ECG (100% vs 80%), but specificity remained high and similar (100% for both methods).

CONCLUSION

Abnormally high-frequency components recorded from the wavelet-transformed ECG might be a novel factor in detecting Brugada syndrome.

Evaluation of Arrhythmogenic Substrate in Patients With Hypertrophic Cardiomyopathy Using Wavelet Transform Analysis

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is thought to have a microvolt-level electrical disarrangement in the myocardium that leads to ventricular tachyarrhythmias and sudden cardiac death. Although signal-averaged electrocardiography (ECG) has been used to detect late potential as a parameter of electrical instability, its predictability is not high. The focus of the present study was the ability of high-resolution wavelet transform from beat-to-beat analysis to detect arrhythmogenic substrates and to evaluate its relationship to the severity of ventricular tachycardia.

METHODS AND RESULTS

The study group comprised 50 healthy subjects and 50 patients with HCM. The filtered-QRS duration from the signal-averaged ECG, the high-power duration (HPD) and number of disarrangement points (NDP) from the wavelet-transform ECG were measured. When HPD was defined >114 ms and/or NDP >9 points as abnormal, the sensitivity and specificity for ventricular tachycardia was 93.8% and 79.4%, respectively. When a mean +/- standard deviation of the HPD in normal subjects was defined as normal, 93.8% of patients with a positive late potential were out of the normal range.

CONCLUSIONS

The newly developed color-display 3-dimensional wavelet transform system showed good time-frequency resolution in analyzing every single beat without signal-averaging. The analysis could be used to detect arrhythmogenic substrates in patients with HCM.

Wavelet transforms and the ECG: a review

The wavelet transform has emerged over recent years as a powerful time-frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.

Cross-correlation time-frequency analysis for multiple EMG signals in Parkinson's disease: a wavelet approach

Using a wavelet analysis approach, it is possible to investigate better the transient and intermittent behavior of multiple electromyographic (EMG) signals during ballistic movements in Parkinsonian patients. In particular, a wavelet cross-correlation analysis on surface signals of two different shoulder muscles allows us to evidence the related unsteady and synchronization characteristics. With a suitable global parameter extracted from local wavelet power spectra, it is possible to accurately classify the subjects in terms of a reliable statistic and to study the temporal evolution of the Parkinson's disease level. Moreover, a local intermittency measure appears as a new promising index to distinguish the low-frequency behavior from normal subjects to Parkinsonian patients.

Can P wave wavelet analysis predict atrial fibrillation after coronary artery bypass grafting?

The purpose of this study was the evaluation of Morlet wavelet analysis of the P wave as a means of predicting the development of atrial fibrillation (AF) in patients who undergo coronary artery bypass grafting (CABG). The P wave was analyzed using the Morlet wavelet in 50 patients who underwent successful CABG. Group A consisted of 17 patients, 12 men and 5 women, of mean age 66.9 +/- 5.9 years, who developed AF postoperatively. Group B consisted of 33 patients, 29 men and 4 women, mean age 62.4 +/- 7.8 years, who remained arrhythmid-free. Using custom-designed software, P wave duration and wavelet parameters expressing the mean and maximum energy of the P wave were calculated from 3-channel digital recordings derived from orthogonal ECG leads (X, Y, and Z), and the vector magnitude (VM) was determined in each of 3 frequency bands (200-160 Hz, 150-100 Hz and 90-50 Hz). Univariate logistic-regression analysis identified a history of hypertension, the mean and maximum energies in all frequency bands along the Z axis, the mean and maximum energies (expressed by the VM) in the 200-160 Hz frequency band, and the mean energy in the 150-100 Hz frequency band along the Y axis as predictors for post-CABG AF. Multivariate analysis identified hypertension, ejection fraction, and the maximum energies in the 90-50 Hz frequency band along the Z and composite-vector axes as independent predictors. This multivariate model had a sensitivity of 91% and a specificity of 65%. We conclude that the Morlet wavelet analysis of the P wave is a very sensitive method of identifying patients who are likely to develop AF after CABG. The occurrence of post-CABG AF can be explained by a different activation pattern along the Z axis.

Non-invasive Wedensky modulation within the QRS complex

To investigate non-invasively induced Wedensky modulation, 2ms pulses of 5, 20 and 40mA were delivered between precordial and subscapular patches synchronously with the ORS complex. Wavelet vector magnitude was obtained for averaged modulated and non-modulated complexes. The surface area of a 3D-envelope of their difference (WSR) was compared in 59 patients with an uncomplicated follow-up after myocardial infarction (MI) (42 men, 64.3+/-9.1 years), in 30 patients with ischaemic heart disease and a history of ventricular tachycardia/fibrillation (VT/VF) (29 men, 63.1+/-9.8 years), and in 53 healthy subjects (control) (22 men, 56.6+/-10.1 years). Reproducibility of the assessment was tested by computing relative errors in a sub-population of 30 VT/VF patients and 47 controls. Wedensky modulation parameters differed significantly between control, MI and VT/VF subjects. In 10 ms post-modulation windows, the following WSR values were obtained: controls: 1184+/-496 (5mA), 1553+/-838 (20 mA) and 2092+/-1488 (40 mA); VT/VF: 861+/-412 (5mA), 1134+/-636 (20 mA) and 1320+/-1036 (40 mA); MI: 1305+/-885 (5mA) and 1779+/-1169 (20 mA). With all modulating energies used, the VT/VF patients differed significantly from both the controls and MI patients; control patients against VT/VF patients: p<0.004 (5 mA), p<0.01 (20 mA) and p<0.001 (40 mA); VT/VF patients against MI patients: p<0.02 (5mA), p<0.01 (20 mA); control patients against MI patients: all p=NS. The reproducibility assessment showed an acceptable stability of Wedensky modulation parameters. This study demonstrated that wavelet decomposition detects non-invasive Wedensky modulation within the QRS complex, and VT/VF patients are less sensitive to Wedensky modulation than control and MI patients.

Wavelet and short-time Fourier transform analysis of electromyography for detection of back muscle fatigue

Measurement of the time-varying characteristics of the frequency content of trunk muscle electromyography is a method to quantify the amount of fatigue endured by workers during industrial tasks, as well as a tool that may guide the training and rehabilitation of healthy and injured workers. Quantification of the change of signal power within specific frequency ranges may shed greater insight into the fatigue process. Sixteen healthy male subjects performed isometric trunk extension at 70% of their maximum voluntary contraction. Surface electromyography from medial and lateral erector spinae, and latissimus dorsi locations were processed using the short-time Fourier transform (STFT) and wavelet transform. Linear regression quantified the time rate of change of median frequency as well as frequency specific STFT filter and wavelet scale measures. The median frequency from the short-time Fourier transform declined by 22 Hz/min from an initial value of 77 Hz on average. The wavelet and STFT filter measures demonstrated this decline to be caused by a reduction in 209-349 Hz signal power in addition to an increase in 7-88 Hz signal power. A significant reduction in median frequency and significant elevation in 13-22 Hz wavelet signal component was detected in about 90% of the cases, indicating their use for detecting and quantifying fatigue.

Wavelet analysis of electromyography for back muscle fatigue detection during isokinetic constant-torque exertions

STUDY DESIGN

An investigation of the effects of human trunk extensor muscle fatigue on the temporal change in frequency content of the electromyogram as quantified using the Fourier and wavelet transforms during the performance of repetitive dynamic trunk extension.

OBJECTIVE

To evaluate whether alterations in the Fourier and wavelet transform measures were consistent with a shift of the signal power to lower frequencies, and to determine which measures were more highly correlated with the decline in maximal trunk extension torque.

SUMMARY OF BACKGROUND DATA

Objective assessment of trunk muscle fatigue is likely to play a more important role in the rehabilitation and prevention of low back injuries, given the association between lack of trunk muscle endurance and acquisition of low back pain. Validation of new methods designed to quantify the level of fatigue using the surface electromyogram is necessary before these techniques can be used in industrial rehabilitation settings. The wavelet transform is a recent development in the signal processing of electromyograms that shows promise as a method for assessment of fatigue.

METHODS

Trunk muscle electromyograms obtained from study participants performing repetitive isokinetic trunk extension endurance tests were analyzed using the wavelet and the traditional Fourier methods. Trunk extension torque was controlled at 35% and 70% of the participants' maximal voluntary contraction while they exerted at 5 and 10 repetitions per minute. The decline in maximal trunk extension torque was measured once per minute. Linear regression quantified the rate of change in Fourier and wavelet measures caused by fatigue, whereas Pearson's correlation coefficient determined their association with the decline in maximum torque.

RESULTS

Changes in the characteristics of the electromyogram were consistent with a shift to lower frequencies: The signal power at higher frequencies was reduced, whereas the power at lower frequencies was elevated. The amount of change was dependent on the task conditions (exertion level and repetition rate). The wavelet-based measures demonstrated as strong an association with the decline in maximal torque output as the Fourier-based measures.

CONCLUSIONS

This study demonstrates that assessment of trunk muscle fatigue during isokinetic movementis possible using both Fourier and wavelet measurements. However, the methods were not as likely to change significantly during lower rates of exertion. These methods, when implemented in a controlled setting, may be used to document the rehabilitation process and guide preventive exercise training.

Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods

In this paper, we introduce wavelet packets as an alternative method for spectral analysis of surface myoelectric (ME) signals. Both computer synthesized and real ME signals are used to investigate the performance. Our simulation results show that wavelet packet estimate has slightly less mean square error (MSE) than Fourier method, and both methods perform similarly on the real data. Moreover, wavelet packets give us some advantages over the traditional methods such as multiresolution of frequency, as well as its potential use for effecting time-frequency decomposition of the nonstationary signals such as the ME signals during dynamic contractions. We also introduce wavelet shrinkage method for improving spectral estimates by significantly reducing the MSE's for both Fourier and wavelet packet methods.

Automatic and efficient R wave discrimination in the right atrium using a two-state hidden Markov model

INTRODUCTION

Discrimination of far-field R waves from atrial events in atrial electrograms (EGMs) is problematic in present implantable pacing systems. Adjustments of atrial refractory periods and sensitivity settings are the only options, and they will not provide optimal performance in many patients. The reliable detection or rejection of R waves in atrial EGMs would avoid problems of atrial undersensing or oversensing, thus benefiting DDD patients by providing more reliable and specific atrial arrhythmia detection. In addition, detection of far-field R waves could allow a measurement of AV conduction time in AAI and aid in discrimination of supraventricular tachyarrhythmia from ventricular tachyarrhythmia.

METHODS AND RESULTS

Both atrial and ventricular unipolar EGMs were collected from 25 patients undergoing pacemaker implant or replacement. An average of 141 seconds of intrinsic or VVI paced EGMs was recorded and post analyzed. A new two-state hidden Markov model (HMM) was developed specifically for far-field R wave and P wave discrimination in the atrium. The recorded patients' EGMs were analyzed using this model, and the sensitivity and positive predictivity of far-field R wave detection were evaluated. The collected atrial EGMs were visually examined and marked as the control for verification of the detection analysis. Far-field R wave detection using this model had an overall sensitivity of 94% +/- 9.4% and a positive predictivity of 98.3% +/- 4.4%; and the far-field R wave rejection using the same model had a sensitivity and a positive predictivity of 98.8% +/- 3.8% and 99.1% +/- 1.7%, respectively.

CONCLUSION

Far-field R wave detection in the right atrium by the two-state HMM is reliable and accurate, and can significantly improve atrial arrhythmia management for patients.

Detection of abnormal high-frequency components in the QRS complex by the wavelet transform in patients with idiopathic dilated cardiomyopathy

In order to investigate whether increased fine, fractionated signals within the QRS complex can detect arrhythmogenic substrates and how these fine signals link with ventricular mechanical dysfunction, wavelet analysis was performed on averaged QRS complexes obtained from the left precordial lead in 26 patients with idiopatic dilated cardiomyopathy (IDCM) and in 12 normal subjects. The number of local maxima and the duration of the wavelet transform were significantly greater in patients with IDCM than in normal subjects; the number at 100 Hz was 8.8+/-3.1 vs 6.0+/-1.1 (p<0.01), and the duration at 100Hz was 93+/-15 vs 75+/-7ms (p<0.01). Both of these indices were greater in the patients with than in those without late potentials, repetitive ventricular premature beats or cardiac death. In addition, significant inverse curvilinear relationships were observed between the left ventricular ejection fraction and both the number of local maxima and the duration of the wavelet transform. In conclusion, fine fragmented signals in the QRS complex detected by wavelet analysis would be an important marker for potentially arrhythmogenic substrates and seemed to progress in parallel with left ventricular mechanical dysfunction in IDCM.

Fragmentation of bandpass-filtered QRS-complex of patients prone to malignant arrhythmia

The structure of high-frequency components of electric and magnetic signals from the heart during the depolarisation phase is investigated. After averaging and broadband filtering with a binomial bandpass filter (37 Hz-90 Hz), the fragmentation of the QRS-complex is quantified. The number of extrema M and a new score value S are calculated from the signals of three electrical leads and one magnetic lead of 23 healthy subjects, 23 patients with coronary heart disease (CHD) without reported event of ventricular tachycardia or fibrillation at the time of measurement, and eight patients with CHD who have suffered from malignant tachycardia. For the parameter M, the sensitivity and specificity for healthy subjects against patients with CHD and ventricular tachycardia for the magnetic lead (the best electric lead) are 100% (75%) and 100% (100%). For the magnetic lead (best electric lead) and parameter S, the sensitivity and specificity are 100% (75%) and 95.6% (100%).

Assessment of intersignal variability for discrimination of atrial fibrillation from atrial flutter

The analysis of endocardial signals obtained from an electrode located in the right atrium enabled by new dual chamber implantable cardioverter defibrillators may be helpful to provide additional therapies such as overdrive pacing or low energy atrial cardioversion for the treatment of concomitant atrial flutter (AFL) or atrial fibrillation (AF). Algorithms for discrimination of atrial tachyarrhythmias based on rate counting are of limited efficacy. The aim of this study was to assess the intersignal variability by using fast discrete wavelet transforms (FDWT) as a new method of discrimination of AF from AFL. Patients with spontaneous episodes of AF/AFL or patients who developed AF/AFL during an electrophysiological study were studied. The endocardial signals were recorded from the high right atrium using a transvenous 5 Fr bipolar electrode catheter (interelectrode spacing: 1 cm). The signals were digitized (2 kHz, 12-bit resolution) after amplification and filtering (40-500 Hz). Within data segments of 10-second duration, 25 consecutive signals were selected and normalized and FDWT was applied. Standard deviations of the wavelet coefficients (SD) from coarse scales (scale 4-8) were calculated. A total of 94 data segments (AF: 52, AFL: 42) from 28 patients were analyzed. SD at each considered scale was higher for AF than for AFL (P < 0.001). SD at scale 8 discriminated between AF from AFL with 100% sensitivity and specificity. We conclude that assessment of intersignal variability of bipolar endocardial recordings using FDWT is an effective method for the discrimination of AF from AFL. The implementation of this tool in a discrimination algorithm of an implantable device may help provide the appropriate differential therapy for atrial tachyarrhythmias.

Wavelet transform system makes one-beat analysis possible in late potential evaluation

High-frequency components of the QRS complex, including late potentials, can be analyzed by signal averaging (SA). However, this method may fail to detect transient changes as a result of cancellation. The wavelet transform, which has a superior time-frequency resolution, was used to analyze beat-to-beat changes of the QRS components in 50 normal subjects and 50 patients who showed positive late potentials. The transformed data, displayed in three dimensions and in color, were highly reproducible in each patient. Measurement of high-power duration at a frequency of 50 Hz (WD50) showed a significant correlation between WD50 and filtered QRS duration in both groups. When the mean +/- SD of WD50 in normal subjects was defined as normal, 96% of patients with late potentials were out of the normal range. The wavelet signals in patients with late potentials were more inhomogeneous than those of normal subjects. It is concluded that this newly developed color display, three-dimensional wavelet transform system showed extremely good time-frequency resolution in analyzing every beat without signal averaging.

Time-frequency plane Wiener filtering of the high-resolution ECG: background and time-frequency representations

This paper introduces the concept of a posteriori Wiener filtering (APWF), performed in the time-frequency plane. The objective is to improve the signal-to-noise ratio (SNR) of the ensemble-averaged high-resolution electrocardiogram (HRECG). APWF was developed to address the problem of a limited ensemble size for estimating ensemble-averaged evoked potentials. For the HRECG, we identify the major challenge as adapting the time-frequency structure of the filter to that of low-level cardiac signals. Technical limitations and the characteristics of HRECG signals make time-frequency analysis of the ensemble average problematic. Normal and abnormal signal components are difficult to distinguish due to low time-frequency energy concentration and limited spectrotemporal resolution. However, considering the entire ensemble of repetitive ECG records, signal and noise components are separable in the time-frequency plane. This forms the basis of the new time-frequency plane Wiener (TFPW) filter, applicable to any ensemble averaging problem involving repetitive deterministic signals mixed with uncorrelated noise.

Real-time spike detection in EEG signals using the wavelet transform and a dedicated digital signal processor card

This paper describes a complete real-time system for EEG signal analysis. Specific software and hardware have been designed to provide biologists with an efficient tool, which allows a complete study of the different states of vigilance as well as the paroxysmal activities. The analysis method which is based on the wavelet transform is first presented and compared to the standard spectral approach. The dedicated digital signal processor card, based on the Motorola 96002 processor chip, that has been designed to support real-time acquisition and real-time processing of EEG signals is then presented. We finally illustrate the proposed method by processing real EEG signals of rats, and show that it opens up new prospects in the domain of EEG-based diagnosis. We propose a new representation, called globalization, that provides a global view and better detection of paroxysmal activities.

Multiresolution decomposition of the signal-averaged ECG using the mallat approach for prediction of arrhythmic events after myocardial infarction

The aim of this study was to analyze the ability of the multiresolution decomposition of the signal-averaged electrocardiogram (ECG) to discriminate between patients who develop life-threatening ventricular arrhythmias after myocardial infarction and those who do not and to compare the predictive values of this approach with those obtained from the analysis of ventricular late potentials in the time domain. Signal-averaged ECGs of 769 prospectively included patients were analyzed. A total of 42 arrhythmic events occurred during the follow-up period. For numerical calculations of wavelet analysis, the total and relative energies of the QRS complex were obtained in seven frequency bands. The combination of the relative energy in the frequency bands 7.8-15.6 Hz and 62.5-125 Hz enhanced statistical performance as compared with the time-domain parameters (positive predictive accuracy, 11.3 vs 8.2%). Combining wavelet transform and time-domain parameters enhanced the predictive values even more (positive predictive accuracy, 14.3%) compared with applying each method alone.

Predictive value of wavelet correlation functions of signal-averaged electrocardiogram in patients after anterior versus inferior myocardial infarction

OBJECTIVES

This study sought to evaluate the prognostic value of wavelet correlation functions of the signal-averaged electrocardiogram (ECG) for arrhythmic events in patients after myocardial infarction.

BACKGROUND

Wavelet transform of the signal-averaged ECG has been shown to be a nonstationary analysis technique describing the time evolution of frequency spectra throughout the QRS complex. To quantify the wavelet transform, we introduced the new concept of the wavelet correlation function.

METHODS

The relation among wavelet correlation functions, ventricular late potentials and the site of infarction was investigated in 769 men < 66 years old who survived the acute phase of myocardial infarction (351 [46%] anterior, 418 [54%] inferior infarctions). Signal-averaged ECG recordings were obtained 2 to 3 weeks after infarction. During 6 months of follow-up, 33 patients (4.3%) experienced a malignant arrhythmic event. Wavelet correlation functions of the signal-averaged ECG were evaluated in a time-frequency plane ranging from 25 ms before QRS onset to 25 ms after QRS offset in the frequency range between 40 and 100 Hz.

RESULTS

Patients with an anterior infarction had lower mean wavelet correlation coefficients (p < 0.001) and a lower incidence of ventricular late potentials than patients with an inferior infarction (32.3% vs. 42.7%, p = 0.003). The combination of wavelet correlation functions and late potentials increased the total predictive accuracy from 52% to 72% for inferior and from 64% to 76% for anterior infarctions.

CONCLUSIONS

Spectral changes in the signal-averaged QRS complex are more prominent in anterior than inferior infarctions. Combination of late potential analysis and wavelet correlation functions increases the prognostic value for serious arrhythmic events after myocardial infarction.

Stratification of time-frequency abnormalities in the signal-averaged high-resolution ECG in postinfarction patients with and without ventricular tachycardia and congenital long QT syndrome

Having developed sound mathematical techniques that allow precise mapping of cardiac signals in the time-frequency (TF) and time-scale planes, the next important issue is to extract from these representations information that best reflects the electrophysiologic and anatomic derangement unique to patients at risk of arrhythmias and other cardiac diseases. In this study, the authors present a new method that stratifies the magnitude of the TF transforms of abnormal cardiac signals into distinguishing features by comparing the means of the coefficients of the TF transforms of any study population to the corresponding means of a control population using a standard ANOVA technique. This results in a three-dimensional mapping of the high-resolution ECG into time, frequency, and P value components. Significant energy increases are given positive P values and depressed energies are given negative P values: these are ranked according to a color scale. The method was tested on two study populations: postmyocardial infarction patients with documented ventricular tachycardia (MI+VT, n = 23) and without (MI-VT, n = 40) and patients with congenital long QT syndrome (LQTS, n = 19). Two groups of healthy control subjects (n = 31 and n = 40) were used as a reference group matched for sex. The study results were based on the Morlet analyzing wavelets, with frequencies ranging from 40 to 250 Hz in 10 logarithmically progressing scales, and computed millisecond per millisecond over a 350-ms analyzing time window, starting from 100 ms before the onset of the QRS. The patients with MI+VT displayed significantly increased high-frequency components in the 40-250-Hz frequency range, corresponding to prolonged QRS duration and late potentials in the area from 80 to 150 ms after QRS onset. Significantly depressed energy (P < 10(-4)) was also observed for the 40-106-Hz frequency range in the first 50 ms of the QRS complex, mainly in lead Y and in the magnitude vector. In patients with LQTS, significant modifications (P < 10(-2)) were observed in the first half of the QRS and in the ST-segment, in all leads, revealing anomalies in the genesis of the ventricular depolarization and repolarization processes. In conclusion, the authors propose a new method for the stratification of abnormal TF components occurring in the signal-averaged high-resolution electrocardiogram of patients at risk of VT and fibrillation under different pathologic conditions.

Wavelets in biomedical engineering

Wavelets analysis methods have been widely used in the signal processing of biomedical signals. These methods represent the temporal characteristics of a signal by its spectral components in the frequency domain. In this way, important features of the signal can be extracted in order to understand or model the physiological system. This paper reviews the widely used orthogonal wavelet transform method in the biomedical applications.

Wavelet analysis of high-resolution ECGs in post-infarction patients: role of the basic wavelet and of the analyzed lead

Wavelet analysis provides a fruitful alternative to standard techniques for the detection of fractionated potentials in signal averaged high-resolution (SA-HR) ECGs. In this study, an attempt is made to optimize the discrimination of post infarction patients prone to ventricular tachycardia (VT), using wavelet analysis. Optimization is based on the choice of the ECG leads or lead combinations to be analyzed, and on the analyzing wavelet to be computed. A set of 40 post-infarction patients (20 patients with VT and 20 patients without any arrhythmia) is analyzed. Individual leads and lead combinations of the SA-HR ECGs are processed using a multiparametric algorithm, based on coherent detection of aligned local maxima of the wavelet transform. Seven basic wavelets are tested: the Morlet's wavelet, and the six first derivatives of a Gaussian function. The first derivative of a Gaussian function provides poor results, and is discarded. All other wavelets prove to perform equivalent classification. A vector magnitude computed from the wavelet transforms of the three SA-HR ECGs achieves better results than individual leads. An optimized risk stratification algorithm leads to 90% sensitivity and 100% specificity in the 40 patients learning set.

Investigating the effects of vasodilator drugs on the turbulent sound caused by femoral artery stenosis using short-term Fourier and wavelet transform methods

In this study, the effects of vasodilator drugs on the turbulent sound generation mechanisms during femoral artery stenoses were investigated using the wavelet analysis of the turbulent sounds to characterize these sounds before and after the injection of vasodilator drugs. Results showed that the injection of drugs drastically improved the diagnostic performance of the turbulent sounds in detecting stenoses by increasing the signal-to-noise ratio of the sounds. Results also suggested that the sound above 250 Hz was drastically increased in response to the injection of the vasodilator drug for the partially occluded cases. The turbulence sounds caused by partially occluded femoral arteries are directly related to the slope of baseline of blood flow and to the velocity of the flow. For the 0% occlusion case, initially, sounds were produced with the injection of drugs. However, the sounds totally disappeared when the slope of average blood flow was zero. These results show that the diagnostic performance of diastolic heart sounds associated with occluded arteries can be improved by using vasodilator drugs, which increase the acoustic energy in the first and second wavelet bandwidths due to the turbulence. The short-term Fourier transform (STFT) method was also applied to the same data base. Results using the STFT showed somewhat similar power distributions in that the acoustical power above 250 Hz was increased after the injection of drugs for the occluded cases. However, the WT method provided better time-frequency resolution than the STFT method, showing details of the change in the frequency characteristics with respect to time after the injection of drug.