Extracting physiologic and clinical data from defibrillators for research purposes to improve treatment for patients in cardiac arrest

Background

A defibrillator should be connected to all patients receiving cardiopulmonary resuscitation (CPR) to allow early defibrillation. The defibrillator will collect signal data such as the electrocardiogram (ECG), thoracic impedance and end-tidal CO2, which allows for research on how patients demonstrate different responses to CPR. The aim of this review is to give an overview of methodological challenges and opportunities in using defibrillator data for research.

Methods

The successful collection of defibrillator files has several challenges. There is no scientific standard on how to store such data, which have resulted in several proprietary industrial solutions. The data needs to be exported to a software environment where signal filtering and classifications of ECG rhythms can be performed. This may be automated using different algorithms and artificial intelligence (AI). The patient can be classified being in ventricular fibrillation or -tachycardia, asystole, pulseless electrical activity or having obtained return of spontaneous circulation. How this dynamic response is time-dependent and related to covariates can be handled in several ways. These include Aalen's linear model, Weibull regression and joint models.

Conclusions

The vast amount of signal data from defibrillator represents promising opportunities for the use of AI and statistical analysis to assess patient response to CPR. This may provide an epidemiologic basis to improve resuscitation guidelines and give more individualized care. We suggest that an international working party is initiated to facilitate a discussion on how open formats for defibrillator data can be accomplished, that obligates industrial partners to further develop their current technological solutions.

Heart rate and QRS duration as biomarkers predict the immediate outcome from pulseless electrical activity

INTRODUCTION

Pulseless electrical activity (PEA) is commonly observed in in-hospital cardiac arrest (IHCA). Universally available ECG characteristics such as QRS duration (QRSd) and heart rate (HR) may develop differently in patients who obtain ROSC or not. The aim of this study was to assess prospectively how QRSd and HR as biomarkers predict the immediate outcome of patients with PEA.

METHOD

We investigated 327 episodes of IHCA in 298 patients at two US and one Norwegian hospital. We assessed the ECG in 559 segments of PEA nested within episodes, measuring QRSd and HR during pauses of compressions, and noted the clinical state that immediately followed PEA. We investigated the development of HR, QRSd, and transitions to ROSC or no-ROSC (VF/VT, asystole or death) in a joint longitudinal and competing risks statistical model.

RESULTS

Higher HR, and a rising HR, reflect a higher transition intensity ("hazard") to ROSC (p < 0.001), but HR was not associated with the transition intensity to no-ROSC. A lower QRSd and a shrinking QRSd reflect an increased transition intensity to ROSC (p = 0.023) and a reduced transition intensity to no-ROSC (p = 0.002).

CONCLUSION

HR and QRSd convey information of the immediateoutcome during resuscitation from PEA. These universally available and promising biomarkers may guide the emergency team in tailoring individual treatment.

Continuous Assessment of Ventricular Fibrillation Prognostic Status during CPR: Implications for Resuscitation

BACKGROUND

Ventricular fibrillation (VF) waveform measures reflect myocardial physiologic status. Continuous assessment of VF prognosis using such measures could guide resuscitation, but has not been possible due to CPR artifact in the ECG. A recently-validated VF measure (termed VitalityScore), which estimates the probability (0-100%) of return-of-rhythm (ROR) after shock, can assess VF during CPR, suggesting potential for continuous application during resuscitation.

OBJECTIVE

We evaluated VF using VitalityScore to characterize VF prognostic status continuously during resuscitation.

METHODS

We characterized VF using VitalityScore during 60 seconds of CPR and 10 seconds of subsequent pre-shock CPR interruption in patients with out-of-hospital VF arrest. VitalityScore utility was quantified using area under the receiver operating characteristic curve (AUC). VitalityScore trends over time were estimated using mixed-effects models, and associations between trends and ROR were evaluated using logistic models. A sensitivity analysis characterized VF during protracted (100-second) periods of CPR.

RESULTS

We evaluated 724 VF episodes among 434 patients. After an initial decline from 0-8 seconds following VF onset, VitalityScore increased slightly during CPR from 8-60 seconds (slope: 0.18 %/min). During the first 10 seconds of subsequent pre-shock CPR interruption, VitalityScore declined (slope: -14 %/min). VitalityScore predicted ROR throughout CPR with AUCs 0.73-0.75. Individual VitalityScore trends during 8-60 seconds of CPR were marginally associated with subsequent ROR (adjusted odds ratio for interquartile slope change (OR)=1.10, p=0.21), and became significant with protracted (≥100 seconds) CPR duration (OR=1.28, p=0.006).

CONCLUSION

VF prognostic status can be continuously evaluated during resuscitation, a development that could translate to patient-specific resuscitation strategies.

Texture-based probability mapping for automatic scar assessment in late gadolinium-enhanced cardiovascular magnetic resonance images

Purpose

To evaluate a novel texture-based probability mapping (TPM) method for scar size estimation in LGE-CMRI.

Methods

This retrospective proof-of-concept study included chronic myocardial scars from 52 patients. The TPM was compared with three signal intensity-based methods: manual segmentation, full-width-half-maximum (FWHM), and 5-standard deviation (5-SD). TPM is generated using machine learning techniques, expressing the probability of scarring in pixels. The probability is derived by comparing the texture of the 3 × 3 pixel matrix surrounding each pixel with reference dictionaries from patients with established myocardial scars. The Sørensen-Dice coefficient was used to find the optimal TPM range. A non-parametric test was used to test the correlation between infarct size and remodeling parameters. Bland-Altman plots were performed to assess agreement among the methods.

Results

The study included 52 patients (76.9% male; median age 64.5 years (54, 72.5)). A TPM range of 0.328–1.0 was found to be the optimal probability interval to predict scar size compared to manual segmentation, median dice (25th and 75th percentiles)): 0.69(0.42–0.81). There was no significant difference in the scar size between TPM and 5-SD. However, both 5-SD and TPM yielded larger scar sizes compared with FWHM (p < 0.001 and p = 0.002). There were strong correlations between scar size measured by TPM, and left ventricular ejection fraction (LVEF, r = −0.76, p < 0.001), left ventricular end-diastolic volume index (r = 0.73, p < 0.001), and left ventricular end-systolic volume index (r = 0.75, p < 0.001).

Conclusion

The TPM method is comparable with current SI-based methods, both for the scar size assessment and the relationship with left ventricular remodeling when applied on LGE-CMRI.

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Texture based probability mapping can be used to evaluate myocardial scar size.

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The method can assess myocardial fibrosis independent of signal intensity.

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The TPM method shows strong correlations between scar size and left ventricular ejection fraction.

Fetal heart rate development during labour

BACKGROUND

Fresh stillbirths (FSB) and very early neonatal deaths (VEND) are important global challenges with 2.6 million deaths annually. The vast majority of these deaths occur in low- and low-middle income countries. Assessment of the fetal well-being during pregnancy, labour, and birth is normally conducted by monitoring the fetal heart rate (FHR). The heart rate of newborns is reported to increase shortly after birth, but a corresponding trend in how FHR changes just before birth for normal and adverse outcomes has not been studied. In this work, we utilise FHR measurements collected from 3711 labours from a low and low-middle income country to study how the FHR changes towards the end of the labour. The FHR development is also studied in groups defined by the neonatal well-being 24 h after birth.

METHODS

A signal pre-processing method was applied to identify and remove time periods in the FHR signal where the signal is less trustworthy. We suggest an analysis framework to study the FHR development using the median FHR of all measured heart rates within a 10-min window. The FHR trend is found for labours with a normal outcome, neonates still admitted for observation and perinatal mortality, i.e. FSB and VEND. Finally, we study how the spread of the FHR changes over time during labour.

RESULTS

When studying all labours, there is a drop in median FHR from 134 beats per minute (bpm) to 119 bpm the last 150 min before birth. The change in FHR was significant ([Formula: see text]) using Wilcoxon signed-rank test. A drop in median FHR as well as an increased spread in FHR is observed for all defined outcome groups in the same interval.

CONCLUSION

A significant drop in FHR the last 150 min before birth is seen for all neonates with a normal outcome or still admitted to the NCU at 24 h after birth. The observed earlier and larger drop in the perinatal mortality group may indicate that they struggle to endure the physical strain of labour, and that an earlier intervention could potentially save lives. Due to the low amount of data in the perinatal mortality group, a larger dataset is required to validate the drop for this group.

A Multiscale Approach for Whole-Slide Image Segmentation of five Tissue Classes in Urothelial Carcinoma Slides

In pathology labs worldwide, we see an increasing number of tissue samples that need to be assessed without the same increase in the number of pathologists. Computational pathology, where digital scans of histological samples called whole-slide images (WSI) are processed by computational tools, can be of help for the pathologists and is gaining research interests. Most research effort has been given to classify slides as being cancerous or not, localization of cancerous regions, and to the “big-four” in cancer: breast, lung, prostate, and bowel. Urothelial carcinoma, the most common form of bladder cancer, is expensive to follow up due to a high risk of recurrence, and grading systems have a high degree of inter- and intra-observer variability. The tissue samples of urothelial carcinoma contain a mixture of damaged tissue, blood, stroma, muscle, and urothelium, where it is mainly muscle and urothelium that is diagnostically relevant. A coarse segmentation of these tissue types would be useful to i) guide pathologists to the diagnostic relevant areas of the WSI, and ii) use as input in a computer-aided diagnostic (CAD) system. However, little work has been done on segmenting tissue types in WSIs, and on computational pathology for urothelial carcinoma in particular. In this work, we are using convolutional neural networks (CNN) for multiscale tile-wise classification and coarse segmentation, including both context and detail, by using three magnification levels: 25x, 100x, and 400x. 28 models were trained on weakly labeled data from 32 WSIs, where the best model got an F1-score of 96.5% across six classes. The multiscale models were consistently better than the single-scale models, demonstrating the benefit of combining multiple scales. No tissue-class ground-truth for complete WSIs exist, but the best models were used to segment seven unseen WSIs where the results were manually inspected by a pathologist and are considered as very promising.

Automatic identification of stimulation activities during newborn resuscitation using ECG and accelerometer signals

BACKGROUND AND OBJECTIVE

Early neonatal death is a worldwide challenge with 1 million newborn deaths every year. The primary cause of these deaths are complications during labour and birth asphyxia. The majority of these newborns could have been saved with adequate resuscitation at birth. Newborn resuscitation guidelines recommend immediate drying, stimulation, suctioning if indicated, and ventilation of non-breathing newborns. A system that will automatically detect and extract time periods where different resuscitation activities are performed, would be highly beneficial to evaluate what resuscitation activities that are improving the state of the newborn, and if current guidelines are good and if they are followed. The potential effects of especially stimulation are not very well documented as it has been difficult to investigate through observations. In this paper the main objective is to identify stimulation activities, regardless if the state of the newborn is changed or not, and produce timelines of the resuscitation episode with the identified stimulations.

METHODS

Data is collected by utilizing a new heart rate device, NeoBeat, with dry-electrode ECG and accelerometer sensors placed on the abdomen of the newborn. We propose a method, NBstim, based on time domain and frequency domain features from the accelerometer signals and ECG signals from NeoBeat, to detect time periods of stimulation. NBstim use causal features from a gliding window of the signals, thus it can potentially be used in future realtime systems. A high performing feature subset is found using feature selection. System performance is computed using a leave-one-out cross-validation and compared with manual annotations.

RESULTS

The system achieves an overall accuracy of 90.3% when identifying regions with stimulation activities.

CONCLUSION

The performance indicates that the proposed NBstim, used with signals from the NeoBeat can be used to determine when stimulation is performed. The provided activity timelines, in combination with the status of the newborn, for example the heart rate, at different time points, can be studied further to investigate both the time spent and the effect of different newborn resuscitation parameters.

Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks

Chest compressions during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may provoque inaccurate rhythm classification by the algorithm of the defibrillator. The objective of this study was to design an algorithm to produce reliable shock/no-shock decisions during CPR using convolutional neural networks (CNN). A total of 3319 ECG segments of 9 s extracted during chest compressions were used, whereof 586 were shockable and 2733 nonshockable. Chest compression artifacts were removed using a Recursive Least Squares (RLS) filter, and the filtered ECG was fed to a CNN classifier with three convolutional blocks and two fully connected layers for the shock/no-shock classification. A 5-fold cross validation architecture was adopted to train/test the algorithm, and the proccess was repeated 100 times to statistically characterize the performance. The proposed architecture was compared to the most accurate algorithms that include handcrafted ECG features and a random forest classifier (baseline model). The median (90% confidence interval) sensitivity, specificity, accuracy and balanced accuracy of the method were 95.8% (94.6-96.8), 96.1% (95.8-96.5), 96.1% (95.7-96.4) and 96.0% (95.5-96.5), respectively. The proposed algorithm outperformed the baseline model by 0.6-points in accuracy. This new approach shows the potential of deep learning methods to provide reliable diagnosis of the cardiac rhythm without interrupting chest compression therapy.

Factors affecting the course of resuscitation from cardiac arrest with pulseless electrical activity in children and adolescents

BACKGROUND

Although in-hospital pediatric cardiac arrests and cardiopulmonary resuscitation occur >15,000/year in the US, few studies have assessed which factors affect the course of resuscitation in these patients. We investigated transitions from Pulseless Electrical Activity (PEA) to Ventricular Fibrillation/pulseless Ventricular Tachycardia (VF/pVT), Return of Spontaneous Circulation (ROSC) and recurrences from ROSC to PEA in children and adolescents with in-hospital cardiac arrest.

METHODS

Episodes of cardiac arrest at the Children's Hospital of Philadelphia were prospectively registered. Defibrillators that recorded chest compression depth/rate and ventilation rate were applied. CPR variables, patient characteristics and etiology, and dynamic factors (e.g. the proportion of time spent in PEA or ROSC) were entered as time-varying covariates for the transition intensities under study.

RESULTS

In 67 episodes of CPR in 59 patients (median age 15 years) with cardiac arrest, there were 52 transitions from PEA to ROSC, 22 transitions from PEA to VF/pVT, and 23 recurrences of PEA from ROSC. Except for a nearly significant effect of mean compression depth beyond a threshold of 5.7 cm, only dynamic factors that evolved during CPR favored a transition from PEA to ROSC. The latter included a lower proportion of PEA over the last 5 min and a higher proportion of ROSC over the last 5 min. Factors associated with PEA to VF/pVT development were age, weight, the proportion spent in VF/pVT or PEA the last 5 min, and the general transition intensity, while PEA recurrence from ROSC only depended on the general transition intensity.

CONCLUSION

The clinical course during pediatric cardiac arrest was mainly influenced by dynamic factors associated with time in PEA and ROSC. Transitions from PEA to ROSC seemed to be favored by deeper compressions.

Duration of Elevated Heart Rate Is an Important Predictor of Exercise-Induced Troponin Elevation

Background The precise mechanisms causing cardiac troponin (cTn) increase after exercise remain to be determined. The aim of this study was to investigate the impact of heart rate (HR) on exercise-induced cTn increase by using sports watch data from a large bicycle competition. Methods and Results Participants were recruited from NEEDED (North Sea Race Endurance Exercise Study). All completed a 91-km recreational mountain bike race (North Sea Race). Clinical status, ECG, blood pressure, and blood samples were obtained 24 hours before and 3 and 24 hours after the race. Participants (n=177) were, on average, 44 years old; 31 (18%) were women. Both cTnI and cTnT increased in all individuals, reaching the highest level (of the 3 time points assessed) at 3 hours after the race (P<0.001). In multiple regression models, the duration of exercise with an HR >150 beats per minute was a significant predictor of both cTnI and cTnT, at both 3 and 24 hours after exercise. Neither mean HR nor mean HR in percentage of maximum HR was a significant predictor of the cTn response at 3 and 24 hours after exercise. Conclusions The duration of elevated HR is an important predictor of physiological exercise-induced cTn elevation. Clinical Trial Registration URL: https://www.clinicaltrials.gov/. Unique identifier: NCT02166216.

State transition modeling of complex monitored health data

This article considers the analysis of complex monitored health data, where often one or several signals are reflecting the current health status that can be represented by a finite number of states, in addition to a set of covariates. In particular, we consider a novel application of a non-parametric state intensity regression method in order to study time-dependent effects of covariates on the state transition intensities. The method can handle baseline, time varying as well as dynamic covariates. Because of the non-parametric nature, the method can handle different data types and challenges under minimal assumptions. If the signal that is reflecting the current health status is of continuous nature, we propose the application of a weighted median and a hysteresis filter as data pre-processing steps in order to facilitate robust analysis. In intensity regression, covariates can be aggregated by a suitable functional form over a time history window. We propose to study the estimated cumulative regression parameters for different choices of the time history window in order to investigate short- and long-term effects of the given covariates. The proposed framework is discussed and applied to resuscitation data of newborns collected in Tanzania.

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.

Automatic Estimation of Coronary Blood Flow Velocity Step 1 for Developing a Tool to Diagnose Patients With Micro-Vascular Angina Pectoris

Aim: Our aim was to automatically estimate the blood velocity in coronary arteries using cine X-ray angiographic sequence. Estimating the coronary blood velocity is a key approach in investigating patients with angina pectoris and no significant coronary artery disease. Blood velocity estimation is central in assessing coronary flow reserve. Methods and Results: A multi-step automatic method for blood flow velocity estimation based on the information extracted solely from the cine X-ray coronary angiography sequence obtained by invasive selective coronary catheterization was developed. The method includes (1) an iterative process of segmenting coronary arteries modeling and removing the heart motion using a non-rigid registration, (2) measuring the area of the segmented arteries in each frame, (3) fitting the measured sequence of areas with a 7° polynomial to find start and stop time of dye propagation, and (4) estimating the blood flow velocity based on the time of the dye propagation and the length of the artery-tree. To evaluate the method, coronary angiography recordings from 21 patients with no obstructive coronary artery disease were used. In addition, coronary flow velocity was measured in the same patients using a modified transthoracic Doppler assessment of the left anterior descending artery. We found a moderate but statistically significant correlation between flow velocity assessed by trans thoracic Doppler and the proposed method applying both Spearman and Pearson tests. Conclusion: Measures of coronary flow velocity using a novel fully automatic method that utilizes the information from the X-ray coronary angiographic sequence were statistically significantly correlated to measurements obtained with transthoracic Doppler recordings.

Rhythm characteristics and patterns of change during cardiopulmonary resuscitation for in-hospital paediatric cardiac arrest

During paediatric cardiopulmonary resuscitation (CPR), patients may transition between pulseless electrical activity (PEA), asystole, ventricular fibrillation/tachycardia (VF/VT), and return of spontaneous circulation (ROSC). The aim of this study was to quantify the dynamic characteristics of this process.

METHODS

ECG recordings were collected in patients who received CPR at the Children's Hospital of Philadelphia (CHOP) between 2006 and 2013. Transitions between PEA (including bradycardia with poor perfusion), VF/VT, asystole, and ROSC were quantified by applying a multi-state statistical model with competing risks, and by smoothing the Nelson-Aalen estimator of cumulative hazard.

RESULTS

Seventy-four episodes of cardiac arrest were included. Median age of patients was 15 years [IQR 11-17], 50% were female and 62% had a respiratory aetiology of arrest. Presenting cardiac arrest rhythms were PEA (60%), VF/VT (24%) and asystole (16%). A temporary surge of PEA was observed between 10 and 15 min due to a doubling of the transition rate from ROSC to PEA (i.e. 're-arrests'). The prevalence of sustained ROSC reached an asymptotic value of 30% at 20 min. Simulation suggests that doubling the transition rate from PEA to ROSC and halving the relapse rate might increase the prevalence of sustained ROSC to 50%.

CONCLUSION

Children and adolescents who received CPR were prone to re-arrest between 10 and 15 min after start of CPR efforts. If the rate of PEA to ROSC transition could be increased and the rate of re-arrests reduced, the overall survival rate may improve.

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for VF-detection are customarily assessed using Holter recordings from public electrocardiogram (ECG) databases, which may be different from the ECG seen during OHCA events. This study evaluates VF-detection using data from both OHCA patients and public Holter recordings. ECG-segments of 4-s and 8-s duration were analyzed. For each segment 30 features were computed and fed to state of the art machine learning (ML) algorithms. ML-algorithms with built-in feature selection capabilities were used to determine the optimal feature subsets for both databases. Patient-wise bootstrap techniques were used to evaluate algorithm performance in terms of sensitivity (Se), specificity (Sp) and balanced error rate (BER). Performance was significantly better for public data with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of 94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times more features than the data from public databases for an accurate detection (6 vs 3). No significant differences in performance were found for different segment lengths, the BER differences were below 0.5-points in all cases. Our results show that VF-detection is more challenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s.

The relationship between transmurality of ischemic scars and the heart rate of ventricular tachycardia

AIMS

The relationship between the heart rate of ventricular tachycardia (VT) and the transmurality of ischemic scars was assessed by a new semiautomatic coordinate-based analysis of late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) images.

METHODS AND RESULTS

Twenty patients assessed by LGE-CMR before implantation of implantable cardioverter defibrillator (ICD) with verified VT during the first year following ICD implantation were included. Scar was defined by pixels with a signal intensity ≥ 50% of maximum signal intensity. All pixels were assigned a coordinate position between endo- and epicardium (λ) and the angle of the heart axis (φ). Based upon the λ and φ values, multiple scar features were computed for all scarred areas. These features were correlated to VT heart rate across the complete range of transmurality. The strongest correlation with univariate regression was found between VT heart rate and the sum of transmurality when the maximum transmurality of these features was ≥ 90% (R-square = 0.47). In multiple regressions analysis, the strongest relationship with VT heart rate was found with a maximum transmurality ≥ 90% and by a combination of scar size, transmurality, and endocardial extent of infarction (R-square = 0.64).

CONCLUSION

Transmurality is the strongest predictor of VT heart rate both in univariate and multivariate models. The strongest relationships were found at a transmurality level > 90%.

Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients

INTRODUCTION

Patients surviving myocardial infarction (MI) can be divided into high and low arrhythmic risk groups. Distinguishing between these two groups is of crucial importance since the high-risk group has been shown to benefit from implantable cardioverter defibrillator insertion; a costly surgical procedure with potential complications and no proven advantages for the low-risk group. Currently, markers such as left ventricular ejection fraction and myocardial scar size are used to evaluate arrhythmic risk.

METHODS

In this paper, we propose quantitative discriminative features extracted from late gadolinium enhanced cardiac magnetic resonance images of post-MI patients, to distinguish between 20 high-risk and 34 low-risk patients. These features include size, location, and textural information concerning the scarred myocardium. To evaluate the discriminative power of the proposed features, we used several built-in classification schemes from matrix laboratory (MATLAB) and Waikato environment for knowledge analysis (WEKA) software, including k-nearest neighbor (k-NN), support vector machine (SVM), decision tree, and random forest.

RESULTS

In Experiment 1, the leave-one-out cross-validation scheme is implemented in MATLAB to classify high- and low-risk groups with a classification accuracy of 94.44%, and an AUC of 0.965 for a feature combination that captures size, location and heterogeneity of the scar. In Experiment 2 with the help of WEKA, nested cross-validation is performed with k-NN, SVM, adjusting decision tree and random forest classifiers to differentiate high-risk and low-risk patients. SVM classifier provided average accuracy of 92.6%, and AUC of 0.921 for a feature combination capturing location and heterogeneity of the scar. Experiment 1 and Experiment 2 show that textural features from the scar are important for classification and that localization features provide an additional benefit.

CONCLUSION

These promising results suggest that the discriminative features introduced in this paper can be used by medical professionals, or in automatic decision support systems, along with the recognized risk markers, to improve arrhythmic risk stratification in post-MI patients.

Beyond ventricular fibrillation analysis: comprehensive waveform analysis for all cardiac rhythms occurring during resuscitation

AIM

To propose a method which analyses the electrocardiogram (ECG) waveform of any cardiac rhythm occurring during resuscitation and computes the probability of that rhythm converting into another with better prognosis (Pdes).

METHODS

Rhythm transitions occurring spontaneously or due to defibrillation were analyzed. For each possible rhythm, ventricular fibrillation/ventricular tachycardia (VF/VT), pulseless electrical activity (PEA), pulse-generating rhythm (PR) and asystole (AS), the desired and undesired transitions were defined. ECG segments corresponding to the last 3s of rhythms prior to transition were used to extract waveform features. For each rhythm type, waveform features were combined into a logistic regression model to develop a rhythm specific classifier of desired transitions. This model was the monitoring function for the Pdes. The capacity of each rhythm specific classifier to discriminate between desired and undesired transitions was evaluated in terms of area under the curve (AUC). Pdes was integrated into a state sequence representation, which structures the information of cardiac arrest episodes, to analyze the effect of therapy on patient. As a case study, the effect of optimal/suboptimal cardiopulmonary resuscitation (CPR) on Pdes was analyzed. The mean Pdes was computed for the pre- and post-CPR intervals which presented the same underlying rhythm. The relationship between the optimal/suboptimal CPR and increase/decrease of Pdes was analyzed.

RESULTS

The AUC was 0.80, 0.79, 0.73 and 0.61 for VF/VT, PEA, PR and AS respectively. The Pdes quantified the probability of every rhythm of the episode developing to a better state, and the evolution of Pdes was coherent with the provided therapy. The case study indicated, for most rhythms, that positive trends in the dynamic behaviour could be associated with optimal CPR, whereas the opposite seemed true for negative trends.

CONCLUSION

A method for continuous ECG waveform analysis covering all cardiac rhythms during resuscitation has been proposed. This methodology can be further developed to be used in retrospective studies of CPR techniques, and, in the future, for potentially monitoring in real time the probability of survival of patients being resuscitated.

A reliable method for rhythm analysis during cardiopulmonary resuscitation

Interruptions in cardiopulmonary resuscitation (CPR) compromise defibrillation success. However, CPR must be interrupted to analyze the rhythm because although current methods for rhythm analysis during CPR have high sensitivity for shockable rhythms, the specificity for nonshockable rhythms is still too low. This paper introduces a new approach to rhythm analysis during CPR that combines two strategies: a state-of-the-art CPR artifact suppression filter and a shock advice algorithm (SAA) designed to optimally classify the filtered signal. Emphasis is on designing an algorithm with high specificity. The SAA includes a detector for low electrical activity rhythms to increase the specificity, and a shock/no-shock decision algorithm based on a support vector machine classifier using slope and frequency features. For this study, 1185 shockable and 6482 nonshockable 9-s segments corrupted by CPR artifacts were obtained from 247 patients suffering out-of-hospital cardiac arrest. The segments were split into a training and a test set. For the test set, the sensitivity and specificity for rhythm analysis during CPR were 91.0% and 96.6%, respectively. This new approach shows an important increase in specificity without compromising the sensitivity when compared to previous studies.

Automatic detection of chest compressions for the assessment of CPR-quality parameters

AIM

Accurate chest compression detection is key to evaluate cardiopulmonary resuscitation (CPR) quality. Two automatic compression detectors were developed, for the compression depth (CD), and for the thoracic impedance (TI). The objective was to evaluate their accuracy for compression detection and for CPR quality assessment.

METHODS

Compressions were manually annotated using the force and ECG in 38 out-of-hospital resuscitation episodes, comprising 869 min and 67,402 compressions. Compressions were detected using a negative peak detector for the CD. For the TI, an adaptive peak detector based on the amplitude and duration of TI fluctuations was used. Chest compression rate (CC-rate) and chest compression fraction (CCF) were calculated for the episodes and for every minute within each episode. CC-rate for rescuer feedback was calculated every 8 consecutive compressions.

RESULTS

The sensitivity and positive predictive value were 98.4% and 99.8% using CD, and 94.2% and 97.4% using TI. The mean CCF and CC-rate obtained from both detectors showed no significant differences with those obtained from the annotations (P>0.6). The Bland-Altman analysis showed acceptable 95% limits of agreement between the annotations and the detectors for the per-minute CCF, per-minute CC-rate, and CC-rate for feedback. For the detector based on TI, only 3.7% of CC-rate feedbacks had an error larger than 5%.

CONCLUSION

Automatic compression detectors based on the CD and TI signals are very accurate. In most cases, episode review could safely rely on these detectors without resorting to manual review. Automatic feedback on rate can be accurately done using the impedance channel.

Towards the automated analysis and database development of defibrillator data from cardiac arrest

BACKGROUND

During resuscitation of cardiac arrest victims a variety of information in electronic format is recorded as part of the documentation of the patient care contact and in order to be provided for case review for quality improvement. Such review requires considerable effort and resources. There is also the problem of interobserver effects.

OBJECTIVE

We show that it is possible to efficiently analyze resuscitation episodes automatically using a minimal set of the available information.

METHODS AND RESULTS

A minimal set of variables is defined which describe therapeutic events (compression sequences and defibrillations) and corresponding patient response events (annotated rhythm transitions). From this a state sequence representation of the resuscitation episode is constructed and an algorithm is developed for reasoning with this representation and extract review variables automatically. As a case study, the method is applied to the data abstraction process used in the King County EMS. The automatically generated variables are compared to the original ones with accuracies ≥ 90% for 18 variables and ≥ 85% for the remaining four variables.

CONCLUSIONS

It is possible to use the information present in the CPR process data recorded by the AED along with rhythm and chest compression annotations to automate the episode review.

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.

Probability mapping of scarred myocardium using texture and intensity features in CMR images

Background

The myocardium exhibits heterogeneous nature due to scarring after Myocardial Infarction (MI). In Cardiac Magnetic Resonance (CMR) imaging, Late Gadolinium (LG) contrast agent enhances the intensity of scarred area in the myocardium.

Methods

In this paper, we propose a probability mapping technique using Texture and Intensity features to describe heterogeneous nature of the scarred myocardium in Cardiac Magnetic Resonance (CMR) images after Myocardial Infarction (MI). Scarred tissue and non-scarred tissue are represented with high and low probabilities, respectively. Intermediate values possibly indicate areas where the scarred and healthy tissues are interwoven. The probability map of scarred myocardium is calculated by using a probability function based on Bayes rule. Any set of features can be used in the probability function.

Results

In the present study, we demonstrate the use of two different types of features. One is based on the mean intensity of pixel and the other on underlying texture information of the scarred and non-scarred myocardium. Examples of probability maps computed using the mean intensity of pixel and the underlying texture information are presented. We hypothesize that the probability mapping of myocardium offers alternate visualization, possibly showing the details with physiological significance difficult to detect visually in the original CMR image.

Conclusion

The probability mapping obtained from the two features provides a way to define different cardiac segments which offer a way to identify areas in the myocardium of diagnostic importance (like core and border areas in scarred myocardium).

Clinical state transitions during advanced life support (ALS) in in-hospital cardiac arrest

BACKGROUND

When providing advanced life support (ALS) in cardiac arrest, the patient may alternate between four clinical states: ventricular fibrillation/tachycardia (VF/VT), pulseless electrical activity (PEA), asystole, and return of spontaneous circulation (ROSC). At the end of the resuscitation efforts, either death has been declared or sustained ROSC has been obtained. The aim of this study was to describe and analyze the clinical state transitions during ALS among patients experiencing in-hospital cardiac arrest.

METHODS AND RESULTS

The defibrillator files from 311 in-hospital cardiac arrests at the University of Chicago Hospital (IL, USA) and St. Olav University Hospital (Trondheim, Norway) were analyzed (clinicaltrials.gov: NCT00920244). The transitions between clinical states were annotated along the time axis and visualized as plots of the state prevalence according to time. The cumulative intensity of the state transitions was estimated by the Nelson-Aalen estimator for each type of state transition, and for the intensities of overall state transitions. Between 70% and 90% of patients who eventually obtained sustained ROSC had progressed to ROSC by approximately 15-20 min of ALS, depending on the initial rhythm. Patients behaving unstably after this time period, i.e., alternating between ROSC, VF/VT and PEA, had a high risk of ultimately being declared dead.

CONCLUSIONS

We provide an overall picture of the intensities and patterns of clinical state transitions during in-hospital ALS. The majority of patients who obtained sustained ROSC obtained this state and stabilized within the first 15-20 min of ALS. Those who continued to behave unstably after this time point had a high risk of ultimately being declared dead.

Segmentation of scarred and non-scarred myocardium in LG enhanced CMR images using intensity-based textural analysis

The Late Gadolinium (LG) enhancement in Cardiac Magnetic Resonance (CMR) imaging is used to increase the intensity of scarred area in myocardium for thorough examination. Automatic segmentation of scar is important because scar size is largely responsible in changing the size, shape and functioning of left ventricle and it is a preliminary step required in exploring the information present in scar. We have proposed a new technique to segment scar (infarct region) from non-scarred myocardium using intensity-based texture analysis. Our new technique uses dictionary-based texture features and dc-values to segment scarred and non-scarred myocardium using Maximum Likelihood Estimator (MLE) based Bayes classification. Texture analysis aided with intensity values gives better segmentation of scar from myocardium with high sensitivity and specificity values in comparison to manual segmentation by expert cardiologists.

Cardiopulmonary resuscitation artefact suppression using a Kalman filter and the frequency of chest compressions as the reference signal

AIM

To develop a new method to suppress the artefact generated by chest compressions during cardiopulmonary resuscitation (CPR) using only the frequency of the compressions as additional information.

MATERIALS AND METHODS

The CPR artefact suppression method was developed and tested using a database of 381 ECG records (89 shockable and 292 non-shockable) from 299 patients. All records were extracted from real out-of-hospital cardiac arrest episodes. The suppression method consists of a Kalman filter that uses the frequency of the measured compressions to estimate the artefact and to remove it from the ECG. The performance of the filter was evaluated by comparing the sensitivity and specificity of an automated external defibrillator before and after the artefact suppression.

RESULTS

For the test database, the sensitivity improved from 57.8% (95% confidence interval, 43.3-71.0%) to 93.3% (81.5-98.4%) and the specificity decreased from 92.5% (87.0-95.9%) to 89.1% (83.0-93.3%).

CONCLUSION

For a similar sensitivity, we obtained better specificity than that reported for other methods, although still short of the values recommended by the American Heart Association. The results suggest that the CPR artefact can be accurately modelled using only the frequency of the compressions. This information could be easily acquired through the defibrillator's CPR help pads, with minimal hardware modifications.

Using ontologies to integrate and share resuscitation data from diverse medical devices

OBJECTIVE

To propose a method for standardised data representation and demonstrate a technology that makes it possible to translate data from device dependent formats to this standard representation format.

METHODS AND RESULTS

Outcome statistics vary between emergency medical systems organising resuscitation services. Such differences indicate a potential for improvement by identifying factors affecting outcome, but data subject to analysis have to be comparable. Modern technology for communicating information makes it possible to structure, store and transfer data flexibly. Ontologies describe entities in the world and how they relate. Letting different computer systems refer to the same ontology results in a common understanding on data content. Information on therapy such as shock delivery, chest compressions and ventilation should be defined and described in a standardised ontology to enable comparison and combining data from diverse sources. By adding rules and logic data can be merged and combined in new ways to produce new information. An example ontology is designed to demonstrate the feasibility and value of such a standardised structure.

CONCLUSIONS

The proposed technology makes possible capturing and storing of data from different devices in a structured and standardised format. Data can easily be transformed to this standardised format, compared and combined independent of the original structure.

Development of the probability of return of spontaneous circulation in intervals without chest compressions during out-of-hospital cardiac arrest: an observational study

BACKGROUND

One of the factors that limits survival from out-of-hospital cardiac arrest is the interruption of chest compressions. During ventricular fibrillation and tachycardia the electrocardiogram reflects the probability of return of spontaneous circulation associated with defibrillation. We have used this in the current study to quantify in detail the effects of interrupting chest compressions.

METHODS

From an electrocardiogram database we identified all intervals without chest compressions that followed an interval with compressions, and where the patients had ventricular fibrillation or tachycardia. By calculating the mean-slope (a predictor of the return of spontaneous circulation) of the electrocardiogram for each 2-second window, and using a linear mixed-effects statistical model, we quantified the decline of mean-slope with time. Further, a mapping from mean-slope to probability of return of spontaneous circulation was obtained from a second dataset and using this we were able to estimate the expected development of the probability of return of spontaneous circulation for cases at different levels.

RESULTS

From 911 intervals without chest compressions, 5138 analysis windows were identified. The results show that cases with the probability of return of spontaneous circulation values 0.35, 0.1 and 0.05, 3 seconds into an interval in the mean will have probability of return of spontaneous circulation values 0.26 (0.24-0.29), 0.077 (0.070-0.085) and 0.040(0.036-0.045), respectively, 27 seconds into the interval (95% confidence intervals in parenthesis).

CONCLUSION

During pre-shock pauses in chest compressions mean probability of return of spontaneous circulation decreases in a steady manner for cases at all initial levels. Regardless of initial level there is a relative decrease in the probability of return of spontaneous circulation of about 23% from 3 to 27 seconds into such a pause.

A least mean-square filter for the estimation of the cardiopulmonary resuscitation artifact based on the frequency of the compressions

Cardiopulmonary resuscitation (CPR) artifacts caused by chest compressions and ventilations interfere with the rhythm diagnosis of automated external defibrillators (AED). CPR must be interrupted for a reliable diagnosis. However, pauses in chest compressions compromise the defibrillation success rate and reduce perfusion of vital organs. The removal of the CPR artifacts would enable compressions to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. We have estimated the CPR artifact using only the frequency of the compressions as additional information to model it. Our model of the artifact is adaptively estimated using a least mean-square (LMS) filter. It was tested on 89 shockable and 292 nonshockable ECG samples from real out-of-hospital sudden cardiac arrest episodes. We evaluated the results using the shock advice algorithm of a commercial AED. The sensitivity and specificity were above 95% and 85%, respectively, for a wide range of working conditions of the LMS filter. Our results show that the CPR artifact can be accurately modeled using only the frequency of the compressions. These can be easily registered after small changes in the hardware of the CPR compression pads.