A novel wavelet transform based analysis reveals hidden structure in ventricular fibrillation

Quick identification of prostate cancer by wavelet transform-based photoacoustic power spectrum analysis

Pathology is currently the gold standard for grading prostate cancer (PCa). However, pathology takes considerable time to provide a final result and is significantly dependent on subjective judgment. In this study, wavelet transform-based photoacoustic power spectrum analysis (WT-PASA) was used for grading PCa with different Gleason scores (GSs). The tumor region was accurately identified via wavelet transform time-frequency analysis. Then, a linear fitting was conducted on the photoacoustic power spectrum curve of the tumor region to obtain the quantified spectral parameter slope. The results showed that high GSs have small glandular cavity structures and higher heterogeneity, and consequently, the slopes at both 1210 nm and 1310 nm were high (p < 0.01). The classification accuracy of the PA time frequency spectrum (PA-TFS) of tumor region using ResNet-18 was 89% at 1210 nm and 92.7% at 1310 nm. Further, the testing time was less than 7 mins. The results demonstrated that identification of PCa can be rapidly and objectively realized using WT-PASA.

Wavelet transform-based photoacoustic time-frequency spectral analysis for bone assessment

In this study, we investigated the feasibility of using photoacoustic time-frequency spectral analysis (PA-TFSA) for evaluating the bone mineral density (BMD) and bone structure. Simulations and ex vivo experiments on bone samples with different BMDs and mean trabecular thickness (MTT) were conducted. All photoacoustic signals were processed using the wavelet transform-based PA-TFSA. The power-weighted mean frequency (PWMF) was evaluated to obtain the main frequency component at different times. The y-intercept, midband-fit, and slope of the linearly fitted curve of the PWMF over time were also quantified. The results show that the osteoporotic bone samples with lower BMD and thinner MTT have higher frequency components and lower acoustic frequency attenuation over time, thus higher y-intercept, midband-fit, and slope. The midband-fit and slope were found to be sensitive to the BMD; therefore, both parameters could be used to distinguish between osteoporotic and normal bones (p < 0.05).

Detection of ventricular arrhythmia using hybrid time-frequency-based features and deep neural network

Sudden cardiac death (SCD) is a major cause of death among patients with heart diseases. It occurs mainly due to ventricular tachyarrhythmia (VTA) which includes ventricular tachycardia (VT) and ventricular fibrillation (VF) conditions. The main challenging task is to predict the VTA condition at a faster rate and timely application of automatic external defibrillator (AED) for saving lives. In this study, a VF/VT classification scheme has been proposed using a deep neural network (DNN) approach using hybrid time-frequency-based features. Two annotated public domain ECG databases (CUDB and VFDB) were used as training, test, and validation of datasets. The main motivation of this study was to implement a deep learning model for the classification of the VF/VT conditions and compared the results with other standard machine learning algorithms. The signal is decomposed with the wavelet transform, empirical mode decomposition (EMD) and variable mode decomposition (VMD) approaches and twenty-four are extracted to form a hybrid model from a window of length 5 s length. The DNN classifier achieved an accuracy (Acc) of 99.2%, sensitivity (Se) of 98.8%, and specificity (Sp) of 99.3% which is comparatively better than the results of the standard classifier. The proposed algorithm can detect VTA conditions accurately, hence could reduce the rate of misinterpretations by human experts and improves the efficiency of cardiac diagnosis by ECG signal analysis.

A method to predict ventricular fibrillation shock outcome during chest compressions

BACKGROUND

Out-of-hospital ventricular fibrillation (VF) cardiac arrest is a leading cause of death. Quantitative analysis of the VF electrocardiogram (ECG) can predict patient outcomes and could potentially enable a patient-specific, guided approach to resuscitation. However, VF analysis during resuscitation is confounded by cardiopulmonary resuscitation (CPR) artifact in the ECG, challenging continuous application to guide therapy throughout resuscitation. We therefore sought to design a method to predict VF shock outcomes during CPR.

METHODS

Study data included 4577 5-s VF segments collected during and without CPR prior to defibrillation attempts in N = 1151 arrest patients. Using training data (460 patients), an algorithm was designed to predict the VF shock outcomes of defibrillation success (return of organized ventricular rhythm) and functional survival (Cerebral Performance Category 1-2). The algorithm was designed with variable-frequency notch filters to reduce CPR artifact in the ECG based on real-time chest compression rate. Ten ECG features and three dichotomous patient characteristics were developed to predict outcomes. These variables were combined using support vector machines and logistic regression. Algorithm performance was evaluated by area under the receiver operating characteristic curve (AUC) to predict outcomes in validation data (691 patients).

RESULTS

AUC (95% Confidence Interval) for predicting defibrillation success was 0.74 (0.71-0.77) during CPR and 0.77 (0.74-0.79) without CPR. AUC for predicting functional survival was 0.75 (0.72-0.78) during CPR and 0.76 (0.74-0.79) without CPR.

CONCLUSION

A novel algorithm predicted defibrillation success and functional survival during ongoing CPR following VF arrest, providing a potential proof-of-concept towards real-time guidance of resuscitation therapy.

VENTRICULAR TACHYCARDIA AND FIBRILLATION DETECTION USING DWT AND DECISION TREE CLASSIFIER

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the life-threatening ventricular arrhythmias that require treatment in an emergency. Detection of VT and VF at an early stage is crucial for achieving the success of the defibrillation treatment. Hence an automatic system using computer-aided diagnosis tool is helpful in detecting the ventricular arrhythmias in electrocardiogram (ECG) signal. In this paper, a discrete wavelet transform (DWT) was used to denoise and decompose the ECG signals into different consecutive frequency bands to reduce noise. The methodology was tested using ECG data from standard CU ventricular tachyarrhythmia database (CUDB) and MIT-BIH malignant ventricular ectopy database (VFDB) datasets of PhysioNet databases. A set of time-frequency features consists of temporal, spectral, and statistical were extracted and ranked by the correlation attribute evaluation with ranker search method in order to improve the accuracy of detection. The ranked features were classified for VT and VF conditions using support vector machine (SVM) and decision tree (C4.5) classifier. The proposed DWT based features yielded the average sensitivity of 98%, specificity of 99.32%, and accuracy of 99.23% using a decision tree (C4.5) classifier. These results were better than the SVM classifier having an average accuracy of 92.43%. The obtained results prove that using DWT based time-frequency features with decision tree (C4.5) classifier can be one of the best choices for clinicians for precise detection of ventricular arrhythmias.

Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest

BACKGROUND

Quantitative measures of the ventricular fibrillation (VF) ECG waveform can assess myocardial physiology and predict cardiac arrest outcomes, making these measures a candidate to help guide resuscitation. Chest compressions are typically paused for waveform measure calculation because compressions cause ECG artifact. However, such pauses contradict resuscitation guideline recommendations to minimize cardiopulmonary resuscitation interruptions. We evaluated a comprehensive group of VF measures with and without ongoing compressions to determine their performance under both conditions for predicting functionally-intact survival, the study's primary outcome.

METHODS

Five-second VF ECG segments were collected with and without chest compressions before 2755 defibrillation shocks from 1151 out-of-hospital cardiac arrest patients. Twenty-four individual measures and 3 combination measures were implemented. Measures were optimized to predict functionally-intact survival (Cerebral Performance Category score ≤2) using 460 training cases, and their performance evaluated using 691 independent test cases.

RESULTS

Measures predicted functionally-intact survival on test data with an area under the receiver operating characteristic curve ranging from 0.56 to 0.75 (median, 0.73) without chest compressions and from 0.53 to 0.75 (median, 0.69) with compressions ( P<0.001 for difference). Of all measures evaluated, the support vector machine model ranked highest both without chest compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.73-0.78) and with compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.72-0.78; P=0.75 for difference).

CONCLUSIONS

VF waveform measures predict functionally-intact survival when calculated during chest compressions, but prognostic performance is generally reduced compared with compression-free analysis. However, support vector machine models exhibited similar performance with and without compressions while also achieving the highest area under the receiver operating characteristic curve. Such machine learning models may, therefore, offer means to guide resuscitation during uninterrupted cardiopulmonary resuscitation.

Identifying stable phase coupling associated with cerebral autoregulation using the synchrosqueezed cross-wavelet transform and low oscillation morlet wavelets

A novel method of identifying stable phase coupling behavior of two signals within the wavelet transform time-frequency plane is presented. The technique employs the cross-wavelet transform to provide a map of phase coupling followed by synchrosqueezing to collect the stable phase regime information. The resulting synchrosqueezed cross-wavelet transform method (Synchro-CrWT) is illustrated using a synthetic signal and then applied to the analysis of the relationship between biosignals used in the analysis of cerebral autoregulation function.

Advanced life support therapy on out-of-hospital cardiac arrest patients: an engineering perspective

In the USA alone, several hundred thousand people die of sudden cardiac arrests each year. Basic life support, defined as chest compressions and ventilations, and early defibrillation are the only factors proven to increase the survival of patients with out-of-hospital cardiac arrest and are key elements in the chain of survival defined by the American Heart Association. The current cardiopulmonary resuscitation guidelines treat all patients the same but studies show a need for more individualization of treatment. This review focusses on ideas on how to strengthen the weak parts of the chain of survival including the ability to measure the effects of therapy, improve time efficiency and optimize the sequence and quality of the various components of cardiopulmonary resuscitation.

Electrocardiogram frequency change by extracorporeal blood perfusion in a swine ventricular fibrillation model

Background

Extracorporeal cardiopulmonary resuscitation (ECPR) refers to the application of extracorporeal blood circulation with oxygenation as a resuscitation tool. The objective of this study is to observe the frequency component changes in the electrocardiogram (ECG) by ECPR during prolonged ventricular fibrillation (VF).

Methods

Six swine were prepared as a VF model. Extracorporeal blood circulation with a pulsatile blood pump and oxygenator was set up for the model. ECG signals were measured for 13 min during VF and analyzed using frequency analysis methods. The median frequency (MF), dominant frequency (DF), and amplitude spectrum area (AMSA) were calculated from a spectrogram obtained using short-time Fourier transform (STFT).

Results

MF decreased from 11 Hz at the start to 9 Hz at 2 min after VF and then increased to 11 Hz at 4.5 min after VF. DF started at 7 Hz and increased to 11 Hz within the first min and decreased to 9 Hz at 2 min, then increased to 12 Hz at 4.5 min after VF. Both frequency components decreased gradually from 4.5 min until 10 min after VF. After the oxygenated blood perfusion was initiated, both MF and DF increased remarkably and exceeded 12 and 14 Hz, respectively. Similarly, AMSA decreased gradually for the first 10 min, but increased remarkably and varied beyond 13 mV∙Hz after the oxygenated blood supply started. Remarkable frequency increases in ECG due to the oxygenated blood perfusion during ECPR were observed in the swine VF model.

Conclusions

The ECG frequency analysis during ECPR can give the resuscitation provider important information about the cardiac perfusion status and the appropriateness of the ECPR setup as well as the prediction of defibrillation success.

An ultra low energy biomedical signal processing system operating at near-threshold

This paper presents a voltage-scalable digital signal processing system designed for the use in a wireless sensor node (WSN) for ambulatory monitoring of biomedical signals. To fulfill the requirements of ambulatory monitoring, power consumption, which directly translates to the WSN battery lifetime and size, must be kept as low as possible. The proposed processing platform is an event-driven system with resources to run applications with different degrees of complexity in an energy-aware way. The architecture uses effective system partitioning to enable duty cycling, single instruction multiple data (SIMD) instructions, power gating, voltage scaling, multiple clock domains, multiple voltage domains, and extensive clock gating. It provides an alternative processing platform where the power and performance can be scaled to adapt to the application need. A case study on a continuous wavelet transform (CWT)-based heart-beat detection shows that the platform not only preserves the sensitivity and positive predictivity of the algorithm but also achieves the lowest energy/sample for ElectroCardioGram (ECG) heart-beat detection publicly reported today.

An algorithm used for ventricular fibrillation detection without interrupting chest compression

Ventricular fibrillation (VF) is the primary arrhythmic event in the majority of patients suffering from sudden cardiac arrest. Attention has been focused on this particular rhythm since it is recognized that prompt therapy, especially electrical defibrillation, may lead to a successful outcome. However, current versions of automated external defibrillators (AEDs) mandate repetitive interruptions of chest compression for rhythm analyses since artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) preclude reliable electrocardiographic (ECG) rhythm analysis. Yet, repetitive interruptions in chest compression are detrimental to the success of defibrillation. The capability for rhythm analysis without requiring "hands-off" intervals will allow for more effective resuscitation. In this paper, a novel continuous-wavelet-transformation-based morphology consistency evaluation algorithm was developed for the detection of disorganized VF from organized sinus rhythm (SR) without interrupting the ongoing chest compression. The performance of this method was evaluated on both uncorrupted and corrupted ECG signals recorded from AEDs obtained from out-of-hospital victims of cardiac arrest. A total of 232 patients and 31,092 episodes of either VF or SR were accessed, in which 8195 episodes were corrupted by artifacts produced by chest compressions. We also compared the performance of this method with three other established algorithms, including VF filter, spectrum analysis, and complexity measurement. Even though there was a modest decrease in specificity and accuracy when chest compression artifact was present, the performance of this method was still superior to other reported methods for VF detection during uninterrupted CPR.

Real-time electrogram analysis for monitoring coronary blood flow during human ventricular fibrillation: implications for CPR

BACKGROUND

Effective chest compressions during prolonged ventricular fibrillation (VF) have been shown to increase the chances of successful defibrillation to a rhythm associated with a sustainable cardiac output. There is currently no effective method of recording the degree of antegrade coronary artery flow during chest compression in VF.

OBJECTIVE

This study sought to quantify the relationship between the antegrade coronary flow and the characteristics of human VF using near real-time wavelet-based electrocardiographic markers.

METHODS

VF experiments were conducted in 8 isolated human hearts. The Langendorff perfusion enabled different flow rates (perfusion) during VF, which allowed for the simulation of chest compression with different efficacies. After the initiation of VF, the hearts were maintained in ischemia (no flow) for 3 minutes, followed by a 2-minute reperfusion and defibrillation. The experiments were repeated at flows of 0%, 30%, and 100% of baseline perfusion, and volume-conducted surface electrograms were recorded and analyzed using continuous wavelet transform in 5-second frames.

RESULTS

Near real-time wavelet features were derived that demonstrated significant differences in the multicomponent nature of VF signals and predicted perfusion rate characteristics for different flow rates (i.e., 0%, 30%, and 100%; P < .0006). A pattern classifier was trained using the feature values from 5 hearts, and the flow rates for 3 additional hearts were predicted with an accuracy of 90%.

CONCLUSION

VF electrogram characteristics as measured by wavelet analysis relate to antegrade coronary flow rate during VF. These findings suggest that chest compression efficacy of physiological importance could be monitored using near real-time wavelet analysis.

Prompt prediction of successful defibrillation from 1-s ventricular fibrillation waveform in patients with out-of-hospital sudden cardiac arrest

PURPOSE

Ventricular fibrillation (VF) is a common cardiac arrest rhythm that can be terminated by electrical defibrillation. During cardiopulmonary resuscitation, there is a strong need for a prompt and reliable predictor of successful defibrillation because myocardial damage can result from repeated futile defibrillation attempts. Continuous wavelet transform (CWT) provides excellent time and frequency resolution of signals. The purpose of this study was to evaluate whether features based on CWT could predict successful defibrillation.

METHODS

VF electrocardiogram (ECG) waveforms stored in ambulance-located defibrillators were collected. Predefibrillation waveforms were divided into 1.0- or 5.12-s VF waveforms. Indices in frequency domain or nonlinear analysis were calculated on the 5.12-s waveform. Simultaneously, CWT was performed on the 1.0-s waveform, and total low-band (1-3 Hz), mid-band (3-10 Hz), and high-band (10-32 Hz) energy were calculated.

RESULTS

In 152 patients with out-of-hospital cardiac arrest, a total of 233 ECG predefibrillation recordings, consisting of 164 unsuccessful and 69 successful episodes, were analyzed. Indices of frequency domain analysis (peak frequency, centroid frequency, and amplitude spectral area), nonlinear analysis (approximate entropy and Hurst exponent, detrended fluctuation analysis), and CWT analysis (mid-band and high-band energy) were significantly different between unsuccessful and successful episodes (P < 0.01 for all). However, logistic regression analysis showed that centroid frequency and total mid-band energy were effective predictors (P < 0.01 for both).

CONCLUSIONS

Energy spectrum analysis based on CWT as short as a 1.0-s VF ECG waveform enables prompt and reliable prediction of successful defibrillation.

Effect of global ischemia and reperfusion during ventricular fibrillation in myopathic human hearts

The effect of lack of global coronary perfusion on myocardial activation rate, wavebreak, and its temporal progression during human ventricular fibrillation (VF) is not known. We tested the hypothesis that global myocardial ischemia decreases activation rate and spatiotemporal organization during VF in myopathic human hearts, while increasing wavebreak, and that a short duration of reperfusion can restore these spatiotemporal changes to baseline levels. The electrograms were acquired during VF in a human Langendorff model using global mapping consisting of two 112-electrode arrays placed on the epicardium and endocardium simultaneously. We found that global myocardial ischemia results in slowing of the global activation rate (combined endo and epi), from 4.89+/-0.04 Hz. to 3.60+/-0.04 Hz. during the 200 s of global ischemia (no coronary flow) (P<0.01) in eight myopathic hearts. Two minutes of reperfusion contributed to reversal of the slowing with activation rate value increasing close to VF onset (4.72+/-0.04 Hz). In addition, during the period of ischemia, an activation rate gradient between the endocardium (3.76+/-0.06 Hz) and epicardium (3.45+/-0.06 Hz) was observed (P<0.01). There was a concomitant difference in wavebreak index (that provides a normalized parameterization of phase singularities) between the epicardium (11.29+/-2.7) and endocardium (3.25+/-2.7) during the 200 s of ischemia (P=0.02). The activation rate, gradient, and wavebreak changes were reversed by short duration (2 min) of reperfusion. Global myocardial ischemia of 3 min leads to complex spatiotemporal changes during VF in myopathic human hearts; these changes can be reversed by a short duration of reperfusion.

T-wave alternans found in preventricular tachyarrhythmias in CCU patients using a wavelet transform-based methodology

Ventricular tachyarrhythmias are potentially lethal cardiac pathologies and the commonest cause of sudden cardiac death. Efforts to predict the onset of such events are based on feature extraction from the surface ECG. T-wave alternans (TWAs) are considered a marker of abnormal ventricular function that may be associated with ventricular tachycardia (VT) and ventricular fibrillation. A novel TWA detection algorithm utilizing the continuous wavelet transform is described in this paper. Simulated ECGs containing artificial TWA were used to test the algorithm that achieved a sensitivity of 91.40% and a specificity of 94.00%. The algorithm was subsequently used to analyze the ECGs of eight patients prior to the onset of VT. Of these, the algorithm indicated that five patients exhibited TWA prior to the onset of the tachyarrhythmic events, while the remaining three patients did not exhibit identifiable TWA. Healthy individuals were also studied in which one short TWA episode was detected by the algorithm. However, closer visual inspection of the data revealed this to be a likely false positive result.

Logarithm of the absolute correlations of the ECG waveform estimates duration of ventricular fibrillation and predicts successful defibrillation

BACKGROUND

Measures of the ventricular fibrillation (VF) waveform may enable better allocation of cardiac arrest treatment by discriminating which patients should receive immediate defibrillation versus alternate therapies such as CPR. We derive a new measure based on the 'roughness' of the VF waveform, the Logarithm of the Absolute Correlations (LAC), and assess and contrast how well the LAC and the previously published scaling exponent (ScE) predict the duration of VF and the likelihood of return of spontaneous circulation (ROSC) under both optimal experimental and commercial-defibrillator sampling conditions.

METHODS AND RESULTS

We derived the LAC and ScE from two different populations--an animal study of 44 swine and a retrospective human sample of 158 out-of-hospital VF arrests treated with a commercial defibrillator. In the animal study, the LAC and ScE were calculated on 5s epochs of VF recorded at 1000 samples/s and then down sampled to 125 samples/s. In the human study, the LAC and ScE were calculated using 6s epochs recorded at 200 samples/s that occurred immediately prior to the initial shock. We compared the LAC and ScE measures using the Spearman correlation coefficients (CC) and areas under the receiver operating characteristic curve (AUC).

RESULTS

In the animal study, the LAC and ScE were highly correlated at 1000 sample/s (CC=0.93) but not at 125 samples/s (CC=-0.06). These correlations were reflected in how well the measures discriminated VF of < or =5 versus >5 min: AUC at 1000 samples/s was similar for LAC compared to ScE (0.71 versus 0.76). However AUC at 125 samples was greater for LAC compared to ScE (0.75 versus 0.62). In the human study, the LAC measure was a better predictor of ROSC following initial defibrillation as reflected by an AUC of 0.77 for LAC compared to 0.57 for ScE.

CONCLUSIONS

The LAC is an improvement over the ScE because the LAC retains its prognostic characteristics at lower ECG sampling rates typical of current clinical defibrillators. Hence, the LAC may have a role in better allocating treatment in resuscitation of VF cardiac arrest.

Identifying potentially shockable rhythms without interrupting cardiopulmonary resuscitation

OBJECTIVE

Current versions of automated external defibrillators (AEDs) mandate interruptions of chest compression for rhythm analyses because of artifacts produced by chest compressions. Interruption of chest compressions reduces likelihood of successful resuscitation by as much as 50%. We sought a method to identify a shockable rhythm without interrupting chest compressions during cardiopulmonary resuscitation (CPR).

DESIGN

Experimental study.

SETTING

Weil Institute of Critical Care Medicine, Rancho Mirage, CA.

SUBJECTS

None.

INTERVENTIONS

Electrocardiographs (ECGs) were recorded in conjunction with AEDs during CPR in human victims. A shockable rhythm was defined as disorganized rhythm with an amplitude > 0.1 mV or, if organized, at a rate of > or = 180 beats/min. Wavelet-based transformation and shape-based morphology detection were used for rhythm classification. Morphologic consistencies of waveform representing QRS components were analyzed to differentiate between disorganized and organized rhythms. For disorganized rhythms, the amplitude spectrum area was computed in the frequency domain to distinguish between shockable ventricular fibrillation and nonshockable asystole. For organized rhythms, in victims in whom the absence of a heartbeat was independently confirmed, the heart rate was estimated for further classification.

MEASUREMENTS AND MAIN RESULTS

To derive the algorithm, we used 29 recordings on 29 patients from the Creighton University ventricular tachyarrhythmia database. For validation, the algorithm was tested on an independent population of 229 victims, including recordings of both ECG and depth of chest compressions obtained during suspected out-of-hospital sudden death. The recordings included 111 instances in which the ECG was corrupted during chest compressions. A shockable rhythm was identified with a sensitivity of 93% and a specificity of 89%, yielding a positive predictive value of 91%. A nonshockable rhythm was identified with a sensitivity of 89%, a specificity of 93%, and a positive predictive value of 91% during uninterrupted chest compression.

CONCLUSIONS

The algorithm fulfilled the potential lifesaving advantages of allowing for uninterrupted chest compression, avoiding pauses for automated rhythm analyses before prompting delivery of an electrical shock.

The frequency ratio: An improved method to estimate ventricular fibrillation duration based on Fourier analysis of the waveform

BACKGROUND

CPR prior to defibrillation improves survival from prolonged ventricular fibrillation (VF) by over 10%, based on recent studies. Waveform analysis could identify those patients with VF of over 5 to 7-min duration who should receive CPR first. A method based on the changes in the Fourier-derived frequency spectrum over time could improve the ability to identify prolonged VF.

METHODS AND RESULTS

The Fourier frequency spectra were calculated on 5-s epochs of VF from 45 swine for 12.5 min. The average power at each frequency shows a marked loss of frequencies above 8 Hz occurring at 5 min accompanied by an increase in the power in frequency spectra from 3 to 5 Hz. A measure termed the frequency ratio is defined as the ratio of the power in the high frequency band from 8 to 24 Hz compared to the power in the low frequency band from 3 to 5 Hz. The frequency ratio is shown to detect 90% of epochs with VF less than 5 min while allowing selection of 74% of those epochs over 5 min. When the frequency ratio is set to detect 90% of episodes of VF under 7 min, it is able to select 88% of those traces with VF over 7 min. The receiver operating curve (ROC) for the frequency ratio has an area under the curve of 0.91 at 5 min and 0.95 at 7 min of VF duration.

CONCLUSIONS

The frequency ratio is an improved frequency-based measure of VF duration. Waveform analysis techniques could determine which patients should receive shock first and which should receive a period of CPR prior to shock, thereby increasing probability of survival.

A fully automated algorithm for the determination of respiratory rate from the photoplethysmogram

OBJECTIVE

To determine if an automatic algorithm using wavelet analysis techniques can be used to reliably determine respiratory rate from the photoplethysmogram (PPG).

METHODS

Photoplethysmograms were obtained from 12 spontaneously breathing healthy adult volunteers. Three related wavelet transforms were automatically polled to obtain a measure of respiratory rate. This was compared with a secondary timing signal obtained by asking the volunteers to actuate a small push button switch, held in their right hand, in synchronisation with their respiration. In addition, individual breaths were resolved using the wavelet-method to identify the source of any discrepancies.

RESULTS

Volunteer respiratory rates varied from 6.56 to 18.89 breaths per minute. Through training of the algorithm it was possible to determine a respiratory rate for all 12 traces acquired during the study. The maximum error between the PPG derived rates and the manually determined rate was found to be 7.9%.

CONCLUSION

Our technique allows the accurate measurement of respiratory rate from the photoplethysmogram, and leads the way for developing a simple non-invasive combined respiration and saturation monitor.

Practical issues in the evaluation of methods for the prediction of shock outcome success in out-of-hospital cardiac arrest patients

There is a need for robust, effective predictors of the outcome from shock for out-of-hospital cardiac arrest patients. Such technology would enable the emergency responder to provide a therapy tailored to the patient's needs. Here we report our most recent findings while dwelling intentionally on the rationale behind the decisions taken during system development. Specifically, we illustrate the need for sensible data selection, fully cross-validated results and the care necessary when evaluating system performance. We analyze 878 pre-shock ECG traces, all of at least 10 s duration from 110 patients with cardiac arrest of cardiac aetiology. The continuous wavelet transform was applied to preshock segments of ECG trace. Time-frequency markers are extracted from the transform and a linear threshold derived from a training set to provide high sensitivity prediction of successful defibrillation. These systems are then evaluated on a withheld test set. All experiments are cross-validated. When compared to popular Fourier-based techniques our wavelet transform method, COP (Cardioversion Outcome Predictor), provides a 10-20% improvement in performance with values of 66 +/- 4 specificity at 95 +/- 4 sensitivity, 61 +/- 4 specificity at 97 +/- 2 sensitivity and 56 +/- 1 specificity at 98 +/- 2 sensitivity achieved for datasets limited to 3, 6, and 9 shocks per patient, respectively. Thus, the assessment of the wavelet marker was associated with a high specificity value at or above 95% sensitivity in comparison to previously reported methods. Therefore, COP could provide an optimal index for the identification of patients for whom shocking would be futile, and for whom an alternative therapy could be considered.

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.

Wavelet-based analysis of heart-rate-dependent ECG features

BACKGROUND

Wavelet-based methods of analyzing ECG signals have been used to identify specific features in cardiac arrhythmias. Since some of these features are rate dependent, it is a requirement that they are examined across a range of physiological heart rates. The wavelet transform is a signal analysis tool that can elucidate spectral and temporal information simultaneously from complex signals, including the ECG. The aim of this study was to identify the local frequency characteristics of the ECG using a real-time wavelet scalogram and to study the rate dependence of these features.

METHODS

We examined the spectral temporal behavior of the local characteristics of the electrocardiogram (ECG) of 10 patients, in whom precise control of heart rate was achieved using right atrial pacing. Temporary reprogramming was used to adjust the paced atrial rate to predetermined values so that a rate-controlled rhythm was produced that closely resembled sinus rhythm.

RESULTS

Rate-dependent features are seen on time-frequency scalograms. As the rate increases, the temporal spacing of features decrease and the frequency bands shift upward on the plot. Two patients with abnormal atrioventricular conduction demonstrate features of Wenckebach conduction and fusion.

CONCLUSIONS

Characterization of the rate-dependent features of the ECG in a paced atrial rhythm by wavelet transform techniques has revealed some additional information not readily seen on single lead ECG analysis. This model provides a surrogate for changes that might be expected during rate changes in physiological sinus rhythm. It is envisaged that this method will offer advantages in detecting features of clinical significance that may not be readily seen by existing methods.

An Algorithm for the Detection of Individual Breaths from the Pulse Oximeter Waveform

OBJECTIVES

To determine if wavelet analysis techniques can be used to reliably identify individual breaths from the photoplethysmogram (PPG).

METHODS

Photoplethysmograms were obtained from 22 healthy adult volunteers timing their respiration rate in synchronisation with a metronome. A secondary timing signal was obtained by asking the volunteers to actuate a small push button switch, held in their right hand, in synchronisation with their respiration. Each PPG was analyzed using primary wavelet decomposition and two new, related, secondary decompositions to determine the accuracy of individual breath detection.

RESULTS

The optimal breath capture was obtained by manually polling the three techniques, allowing detection of 466 out of the 472 breaths studied; a detection rate of 98.7% with no false positive breaths detected.

CONCLUSION

Our technique allows the accurate capture of individual breaths from the photoplethysmogram, and leads the way for developing a simple non-invasive combined respiration and saturation monitor.

Improved prediction of defibrillation success for out-of-hospital VF cardiac arrest using wavelet transform methods

We report an improved method for the estimation of shock outcome prediction based on novel wavelet transform-based time-frequency methods. Wavelet-based peak frequency, energy, mean frequency, spectral flatness and a new entropy measure were studied to predict shock outcome. Of these, the entropy measure provided optimal results with 60 +/- 6% specificity at 91 +/- 2% sensitivity achieved for the prediction of return of spontaneous circulation (ROSC). These results represent a major improvement in shock prediction in human ventricular fibrillation.

Automated external defibrillation versus manual defibrillation for prolonged ventricular fibrillation

STUDY OBJECTIVE

We sought to determine whether the delays in chest compressions and defibrillation associated with an automated external defibrillator would adversely affect outcome compared with manual defibrillation in a swine model of out-of-hospital prolonged ventricular fibrillation.

METHODS

After 8 minutes of untreated ventricular fibrillation, 16 swine (33+/-4 kg) were randomly assigned to automated external defibrillator defibrillation or manual defibrillation with the same biphasic truncated exponential waveform 150-J shock through the same type of pads. Defibrillation with the automated external defibrillator was performed as recommended by the manufacturer, and manual defibrillation was provided per American Heart Association Guidelines. The primary outcome measure was 24-hour survival with good neurologic outcome. Data are described as means+/-SD.

RESULTS

None of 8 animals in the automated external defibrillator group survived for 24 hours, whereas 5 of 8 animals in the manual defibrillation group survived 24 hours, all with good neurologic outcome (P=.027). The time interval from simulated defibrillator arrival to first compressions was 98+/-18 seconds in the automated external defibrillator group versus 68+/-15 seconds in the manual defibrillation group. In particular, the interval from first shock to first chest compressions was 46+/-18 seconds versus 22+/-16 seconds, respectively. The mean percentage of time that chest compressions were performed in the first minute after the first countershock was 15%+/-13% versus 40%+/-15%, respectively. As a result, return of spontaneous circulation within 5 minutes of simulated defibrillator arrival occurred in only 1 of 8 animals in the automated external defibrillator group versus 6 of 8 animals in the manual defibrillation group.

CONCLUSION

The longer delays in chest compressions with automated external defibrillator defibrillation versus manual defibrillation can worsen the outcome from prolonged ventricular fibrillation.

Dynamic nature of electrocardiographic waveform predicts rescue shock outcome in porcine ventricular fibrillation

STUDY OBJECTIVE

Survival decreases with duration of ventricular fibrillation, and it is possible that failed rescue shocks increase myocardial damage. Structure in the ECG signal during ventricular fibrillation can be quantified by using the scaling exponent, a dimensionless measure that correlates with ventricular fibrillation duration. This study examined whether the scaling exponent could predict rescue shock success and whether unsuccessful rescue shocks altered the structure of the ventricular fibrillation waveform and the responsiveness to subsequent rescue shocks.

METHODS

Ventricular fibrillation was electrically induced in 44 anesthetized swine, which were randomly assigned to receive 70-J biphasic rescue shocks at 2, 4, 6, 8, or 10 minutes. If rescue shocks failed, up to 2 subsequent rescue shocks were performed at 2-minute intervals. The scaling exponent was calculated at 1-second intervals from ECG to quantify the organization of the ventricular fibrillation waveform.

RESULTS

A total of 92 rescue shocks were delivered, of which 23 successfully converted ventricular fibrillation to an organized rhythm (immediate success). After these 23 rescue shocks, 14 swine sustained organized rhythms for more than 30 seconds (sustained success). Lower scaling exponent values were associated with increased probability of successful rescue shocks. Receiver operating characteristic curves had an area under the curve of 0.86 for immediate rescue shock success and 0.93 for sustained rescue shock success. Failed rescue shocks increased the rate of scaling exponent increase over time but did not appear to affect subsequent rescue shock success when the scaling exponent was taken into account.

CONCLUSION

Highly deterministic ventricular fibrillation, reflected by a low scaling exponent, predicted rescue shock success regardless of antecedent failed rescue shocks. In addition, unsuccessful rescue shocks might decrease post-rescue shock ventricular fibrillation waveform organization.

Analysing the ventricular fibrillation waveform

The surface electrocardiogram associated with ventricular fibrillation has been of interest to researchers for some time. Over the last few decades, techniques have been developed to analyse this signal in an attempt to obtain more information about the state of the myocardium and the chances of successful defibrillation. This review looks at the implications of analysing the VF waveform and discusses the various techniques that have been used, including fast Fourier transform analysis, wavelet transform analysis and mathematical techniques such as chaos theory.

Bench to bedside: resuscitation from prolonged ventricular fibrillation

Ventricular fibrillation (VF) remains the most common cardiac arrest heart rhythm. Defibrillation is the primary treatment and is very effective if delivered early within a few minutes of onset of VF. However, successful treatment of VF becomes increasingly more difficult when the duration of VF exceeds 4 minutes. Classically, successful cardiac arrest resuscitation has been thought of as simply achieving restoration of spontaneous circulation (ROSC). However, this traditional approach fails to consider the high early post-cardiac arrest mortality and morbidity and ignores the reperfusion injuries, which are manifest in the heart and brain. More recently, resuscitation from cardiac arrest has been divided into two phases; phase I, achieving ROSC, and phase II, treatment of reperfusion injury. The focus in both phases of resuscitation remains the heart and brain, as prolonged VF remains primarily a two-organ disease. These two organs are most sensitive to oxygen and substrate deprivation and account for the vast majority of early post-resuscitation mortality and morbidity. This review focuses first on the initial resuscitation (achieving ROSC) and then on the reperfusion issues affecting the heart and brain.

Algorithms to analyze ventricular fibrillation signals

Prediction of the success of defibrillation to avoid myocardial injury and performance feedback during CPR requires algorithms to analyze ventricular fibrillation signals. This report reviews investigations on different parameters of ventricular fibrillation electrocardiographic signals, including amplitude, frequency, bispectral analysis, amplitude spectrum area, wavelets, nonlinear dynamics, N(alpha) histograms, and combinations of several of these parameters. To date, no satisfactory methods have been found that cope with CPR artifacts and show adequate predictive power of successful defibrillation. The usual limitations of the studies are the small number of subjects, which precludes separation into training and test data. Because many investigations are animal studies of untreated short ventricular fibrillation, the results may be different for prolonged ventricular fibrillation in humans. The universality of threshold values has to be examined, and promising new parameters have to be monitored over longer time periods and analyzed for the effects of chest compressions, ventilation, and concomitant vasopressor therapy.