Neil R. Grubb

CONTINUOUS WAVELET TRANSFORM MODULUS MAXIMA ANALYSIS OF THE ELECTROCARDIOGRAM: BEAT CHARACTERISATION AND BEAT-TO-BEAT MEASUREMENT

The problem of automatic beat recognition in the ECG is tackled using continuous wavelet transform modulus maxima (CWTMM). Features within a variety of ECG signals can be shown to correspond to various morphologies in the CWTMM domain. This domain has an easy interpretation and offers a useful tool for the automatic characterization of the different components observed in the ECG in health and disease. As an application of this enhanced time-frequency analysis technique for ECG signals, an R-wave detector is developed and tested using patient signals recorded in the Coronary Care Unit of the Royal Infirmary of Edinburgh (attaining a sensitivity of 99.53% and a positive predictive value of 99.73%) and with the MIT/BIH database (attaining a sensitivity of 99.70% and a positive predictive value of 99.68%).

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.

Memory impairment in out-of-hospital cardiac arrest survivors is associated with global reduction in brain volume, not focal hippocampal injury.

BACKGROUND AND PURPOSE More than 30% of out-of-hospital cardiac arrest (OHCA) survivors suffer significant memory impairment. The hippocampus may be vulnerable to hypoxic injury during cardiac arrest. The purpose of this study was to determine whether selective hippocampal injury is the substrate for this memory impairment. METHODS Seventeen OHCA survivors and 12 patients with uncomplicated myocardial infarction were studied. OHCA survivors were divided into those with impaired and intact memory. Memory was assessed by use of the Rivermead Behavioural Memory Test and Doors and People Test. MRI was used to determine intracranial, whole-brain, amygdala-hippocampal complex, and temporal lobe volumes. Brain structure was also examined by statistical parametric mapping. RESULTS Left amygdala-hippocampal volume was reduced in memory-impaired OHCA victims compared with control subjects (mean 3. 93 cm(3) and 95% CI 3.50 to 4.36 cm(3) versus mean 4.65 cm(3) and 95% CI 4.37 to 4.93 cm(3); P=0.002). Left temporal lobe and whole-brain volumes were also reduced. There were no differences in amygdala-hippocampal volume indexed against ipsilateral temporal lobe volume. Significant correlations were observed between total brain volume and Rivermead Behavioural Memory Test (r=0.56, P<0.05) and Doors and People Test (r=0.67, P<0.01) scores in OHCA survivors. Both recall and recognition were compromised in memory-impaired subjects. Statistical parametric mapping did not detect focal brain abnormalities in these subjects. Global cerebral atrophy was confirmed by qualitative assessment. CONCLUSIONS Memory impairment in OHCA survivors is associated with global cerebral atrophy, not selective hippocampal damage. Rehabilitation protocols need to account for the global nature of the brain injury.

Prediction of cognitive dysfunction after resuscitation from out-of-hospital cardiac arrest using serum neuron-specific enolase and protein S-100

Background: More than 50% of patients initially resuscitated from out-of-hospital cardiac arrest die in hospital. Objective: To investigate the prognostic value of serum protein S-100 and neuron-specific enolase (NSE) concentrations for predicting (a) memory impairment at discharge; (b) in-hospital death, after resuscitation from out-of-hospital cardiac arrest. Methods: In a prospective study of 143 consecutive survivors of out-of-hospital cardiac arrest, serum samples were obtained within 12, 24–48 and 72–96 hours after the event. S-100 and NSE concentrations were measured. Pre-discharge cognitive assessment of patients (n = 49) was obtained by the Rivermead Behavioural Memory Test (RBMT). The relationship between biochemical brain marker concentrations and RBMT scores, and between marker concentrations and the risk of in-hospital death was examined. Results: A moderate negative relationship was found between S-100 concentration and memory test score, at all time points. The relationship between NSE and memory test scores was weaker. An S-100 concentration >0.29 μg/l at time B predicted moderate to severe memory impairment with absolute specificity (42.8% sensitivity). S-100 remained an independent predictor of memory function after adjustment for clinical variables and cardiac arrest timing indices. NSE and S-100 concentrations were greater in patients who died than in those who survived, at all time points. Both NSE and S-100 remained predictors of in-hospital death after adjustment for clinical variables and cardiac arrest timing indices. The threshold concentrations yielding 100% specificity for in-hospital death were S-100: 1.20 μg/l (sensitivity 44.8%); NSE 71.0 μg/l (sensitivity 14.0%). Conclusions: Estimation of serum S-100 concentration after out-of-hospital cardiac arrest can be used to identify patients at risk of significant cognitive impairment at discharge. Serum S-100 and NSE concentrations measured 24–48 hours after cardiac arrest provide useful additional information.

Resuscitation from out-of-hospital cardiac arrest: implications for cardiac enzyme estimation

BACKGROUND diagnosis of precipitating myocardial infarction is essential for management of victims of out-of-hospital cardiac arrest, since investigations and treatment are determined by the underlying cause. Skeletal muscle and myocardial damage from external cardiac massage and defibrillation may complicate biochemical diagnosis of myocardial infarction. OBJECTIVES (a) to examine the relationship between cumulative defibrillation energy and serum levels of cardiac troponin T and MB creatine kinase (MB-CK) mass in out-of-hospital cardiac arrest survivors without electrocardiographic evidence of myocardial infarction; (b) to reassess diagnostic thresholds for myocardial infarction using MB-CK mass and troponin T in this setting. METHODS 77 victims of out-of-hospital cardiac arrest were studied. Serum was obtained for MB-CK mass, CK and troponin T estimation on the first 4 days of admission. Patients were divided into three groups using electrocardiographic criteria: group 1, myocardial infarction; group 2, no evidence of infarction; and group 3, equivocal electrocardiograms. Correlation coefficients were calculated for highest recorded levels of the biochemical markers versus defibrillation energy. Receiver-operating characteristic plots were used to determine optimum biochemical diagnostic thresholds for subjects in groups 1 and 2. RESULTS using predefined criteria, 27 patients had myocardial infarction, 34 did not have myocardial infarction and 16 had equivocal electrocardiograms. Significant correlations were found for defibrillation energy versus log troponin T (r = 0.42, P < 0.05), log MB-CK mass (r = 0.51, P < 0.01) and total CK (r = 0.68, P < 0.001) in group 2. Within groups 1 and 2, MB-CK mass and troponin T provided additional diagnostic value over MB-CK fraction (P < 0.001). Diagnostic accuracy was not improved by adjusting for shock energy. The optimum threshold value was 4 ng/ml for troponin T (sensitivity 88%, specificity 95%), 60 ng/ml for MB-CK mass (sensitivity 88%, specificity 88%) and 8% of total CK for MB-CK fraction (sensitivity 74%, specificity 82%). These values should be interpreted with caution, since this study is limited by the exclusion of patients with uncertain electrocardiographic diagnoses into group 3. CONCLUSIONS skeletal muscle and myocardial damage occurs in survivors of out-of-hospital cardiac arrest and is related to the duration of resuscitation. This complicates biochemical diagnosis of underlying myocardial infarction. Specific high diagnostic threshold values for MB-CK and troponin T are needed to optimise diagnostic accuracy. The use of MB-CK fraction leads to greater diagnostic error because of the variability of muscle CK release after resuscitation.

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.

Type D Personality Predicts Poor Medication Adherence in Myocardial Infarction Patients

Type D personality, the combination of negative affectivity (NA) and social inhibition (SI), is an emerging risk factor in cardiovascular disease. This study aimed to examine one possible behavioural mechanism to explain the link between Type D and ill-health. It was hypothesised that Type D personality would predict medication adherence in myocardial infarction (MI) patients. In a prospective study, 192 MI patients (54 females and 138 males) completed measures of Type D personality and provided demographic and medical information 1 week post-MI, and then 131 patients went on to complete a self-report measure of medication adherence 3 months post-MI. It was found that Type D personality predicts adherence to medication, after controlling for demographic and clinical risk factors. Critically, the constituent components of Type D, NA and SI, interact to predict medication adherence, after controlling for the effects of each component separately. Poor adherence to medication may represent one mechanism to explain why Type D cardiac patients experience poor clinical outcome, in comparison to non-Type D patients. Interventions, which target the self-management of medication, may be useful in these high-risk patients.

R-wave detection using continuous wavelet modulus maxima

Modulus maxima derived from the continuous wavelet transform offers an enhanced time-frequency analysis technique for ECG signal analysis. Features within the ECG can be shown to correspond to various morphologies in the continuous modulus maxima domain. This domain has an easy interpretation and offers a good tool for the automatic characterization of the different components observed in the ECG in health and disease. As an application of these properties we have developed an R-wave detector and tested it using patient signals recorded in the Coronary Care Unit of the Royal Infirmary of Edinburgh (attaining a sensitivity of 99.53% and a positive predictive value of 99.73%) and with the MIT/BIH database (attaining a sensitivity of 99.7% and a positive predictive value of 99.68%).

Uncovering non-linear structure in human ECG recordings

We employ surrogate data techniques and a new correlation dimension estimation algorithm, the Gaussian kernel algorithm, to uncover non-linearity in human electrocardiogram recordings during normal (sinus) rhythm, ventricular tachycardia (VT) and ventricular fibrillation (VF). We conclude that all three rhythms are not linear (i.e. distinct from a monotonic non-linear transformation of linearly filtered noise) and have significant correlations over a period greater than the inter-beat interval. Furthermore, we observe that sinus rhythm and VT exhibit a correlation dimension of approximately 2.3 and 2.4, respectively. The correlation dimension of VF exceeds 3.2. The entropy of sinus rhythm, VT and VF is approximately 0.69, 0.55, and 0.67 nats/s, respectively. These results indicate that techniques from non-linear dynamical systems theory should help us understand the mechanism underlying ventricular arrhythmia, and that these rhythms are likely to be a combination of low dimensional chaos and noise. 2002 Elsevier Science Ltd. All rights reserved.

In-hospital cardiac arrest leads to chronic memory impairment

OBJECTIVE To assess the prevalence and severity of memory deficits in a group of patients who survived an in-hospital cardiac arrest (IHCA) in comparison with patients resuscitated after cardiac arrest outside hospital (OHCA) and patients with acute myocardial infarction (MI). SUBJECTS Thirty-five IHCA survivors, 35 OHCA survivors, and 35 patients who had suffered MI uncomplicated by cardiac arrest. PROCEDURE Participants were assessed 8.2 (4.5) months after the event for current affective state (Hospital anxiety and depression scale (HADS)), pre-morbid intelligence (National adult reading test (NART)), short-term memory (digit span test) and long-term episodic memory (Rivermead behavioural memory test-RBMT). RESULTS IHCA patients scored lower on the RBMT than MI controls but did not score significantly differently OHCA patients. Moderate or severe memory impairment was found in 26% of the IHCA group and 38% of the OHCA group. None of the MI group was found to have this degree of impairment. This difference in prevalence of memory impairment between the two cardiac arrest groups was not statistically significant. However, both arrest groups had significantly greater memory impairment than the MI control group. CONCLUSIONS Clinically important memory impairment was found in one in four patients surviving IHCA. The shorter arrest durations that are thought to be associated with IHCA may not be sufficient to protect patients from memory impairment associated with cerebral hypoxia.