Erik Alonso

Universidad del País Vasco / Euskal Herriko Unibertsitatea, Bilbao, Basque Country, Spain

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Publications (19)38.48 Total impact

  • [show abstract] [hide abstract]
    ABSTRACT: To analyze the relationship between the depth of the chest compressions and the fluctuation caused in the thoracic impedance (TI) signal in out-of-hospital cardiac arrest (OHCA). The ultimate goal was to evaluate whether it is possible to identify compressions with inadequate depth using information of the TI waveform. 60 OHCA episodes were extracted, one per patient, containing both compression depth (CD) and TI signals. Every 5s the mean value of the maxima of the CD, Dmax, and three features characterizing the fluctuations caused by the compressions in the TI waveform (peak-to-peak amplitude, area and curve length) were computed. The linear relationship between Dmax and the TI features was tested using Pearson correlation coefficient (r) and univariate linear regression for the whole population, for each patient independently, and for series of compressions provided by a single rescuer. The power of the three TI features to classify each 5s-epoch as shallow/non-shallow was evaluated in terms of area under the curve, sensitivity and specificity. The r was 0.34, 0.36 and 0.37 for peak-to-peak amplitude, area and curve length respectively when the whole population was analyzed. Within patients the median r was 0.40, 0.43 and 0.47 respectively. The analysis of the series of compressions yielded a median r of 0.81 between Dmax and the peak-to-peak amplitude, but it decreased to 0.47 when all the series were considered jointly. The classifier based on the TI features showed 90.0%/37.1% and 86.2%/43.5% sensitivity/specificity values, and an area under the curve of 0.75 and 0.71 for the training and test set respectively. Low linearity between CD and TI was noted in OHCA episodes involving multiple rescuers. Our findings suggest that TI is unreliable as a predictor of Dmax and inaccurate in detecting shallow compressions.
    Resuscitation 01/2014; · 4.10 Impact Factor
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    ABSTRACT: 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 minutes and 67402 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.
    Resuscitation 01/2014; · 4.10 Impact Factor
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    ABSTRACT: Aim: To analyze the feasibility of extracting the circulation component from the thoracic impedance acquired by defibrillation pads. The impedance circulation component (ICC) would permit detection of pulse-generating rhythms (PR) during the analysis intervals of an automated external defibrillator when a non-shockable rhythm with QRS complexes is detected. METHODS: A dataset of 399 segments, 165 associated with PR and 234 with pulseless electrical activity (PEA) rhythms, was extracted from out-of-hospital cardiac arrest episodes by applying a conservative criterion. Records consisted of the electrocardiogram and the thoracic impedance signals free of artifacts due to thoracic compressions and ventilations. The impedance was processed using an adaptive scheme based on a least mean square algorithm to extract the ICC. Waveform features of the ICC signal and its first derivative were used to discriminate PR from PEA rhythms. RESULTS: The segments were split into development (83 PR and 117 PEA rhythms) and testing (82 PR and 117 PEA rhythms) subsets with a mean duration of 10.6s. Three waveform features, peak to peak amplitude, mean power, and mean area were defined for the ICC signal and its first derivative. The discriminative power in terms of area under the curve with the testing dataset was 0.968, 0.971, and 0.969, respectively, when applied to the ICC signal, and 0.974, 0.988 and 0.988, respectively, with its first derivative. CONCLUSION: A reliable method to extract the ICC of the thoracic impedance is feasible. Waveform features of the ICC or its first derivative show a high discriminative power to differentiate PR from PEA rhythms (area under the curve higher than 0.96 for any feature).
    Resuscitation 06/2013; · 4.10 Impact Factor
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    ABSTRACT: OBJECTIVES: Filtering the cardiopulmonary resuscitation (CPR) artifact has been a major approach to minimizing interruptions to CPR for rhythm analysis. However, the effects of these filters on interruptions to CPR have not been evaluated. This study presents the first methodology for directly quantifying the effects of filtering on the uninterrupted CPR time. METHODS: A total of 241 shockable and 634 nonshockable out-of-hospital cardiac arrest records (median duration, 150 seconds) from 248 patients were analyzed. Filtering and rhythm analysis were commenced after 1 minute of CPR, and the end point for CPR was established at the time of the first shock diagnosis. Kaplan-Meier curves were used to compute the probability of interrupting CPR as a function of time. The probabilities of delivering 2 minutes of uninterrupted CPR for the shockable and nonshockable rhythms were compared with the 2-minute cycles of uninterrupted CPR recommended by the guidelines. RESULTS: For the nonshockable rhythms, the probabilities of delivering at least 2 and 3 minutes of uninterrupted CPR were 58% (95% confidence interval, 54%-62%) and 48% (44%-52%), respectively. These are the probabilities of reducing and substantially reducing the frequency of CPR interruptions for rhythm analysis. For the shockable rhythms, the probability of avoiding unnecessary CPR prolongation beyond 2 minutes was 100% (99%-100%). CONCLUSIONS: Filtering reduces the frequency of CPR interruptions for rhythm analysis in less than 60% of nonshockable rhythms. New strategies to increase the probability of prolonging CPR for nonshockable rhythms should be defined and evaluated using the methodology proposed in this study.
    The American journal of emergency medicine 05/2013; · 1.54 Impact Factor
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    ABSTRACT: AIM: To demonstrate the feasibility of doing a reliable rhythm analysis in the chest compression pauses (e.g. pauses for two ventilations) during cardiopulmonary resuscitation (CPR). METHODS: We extracted 110 shockable and 466 nonshockable segments from 235 out-of-hospital cardiac arrest episodes. Pauses in chest compressions were already annotated in the episodes. We classified pauses as ventilation or non-ventilation pause using the transthoracic impedance. A high-temporal resolution shock advice algorithm (SAA) that gives a shock/no-shock decision in 3s was launched once for every pause longer than 3s. The sensitivity and specificity of the SAA for the analyses during the pauses were computed. RESULTS: We identified 4476 pauses, 3263 were ventilation pauses and 2183 had two ventilations. The median of the mean duration per segment of all pauses and of pauses with two ventilations were 6.1s (4.9-7.5s) and 5.1s (4.2-6.4s), respectively. A total of 91.8% of the pauses and 95.3% of the pauses with two ventilations were long enough to launch the SAA. The overall sensitivity and specificity were 95.8% (90% low one-sided CI, 94.3%) and 96.8% (CI, 96.2%), respectively. There were no significant differences between the sensitivities (P=0.84) and the specificities (P=0.18) for the ventilation and the non-ventilation pauses. CONCLUSION: Chest compression pauses are frequent and of sufficient duration to launch a high-temporal resolution SAA. During these pauses rhythm analysis was reliable. Pre-shock pauses could be minimised by analysing the rhythm during ventilation pauses when CPR is delivered at 30:2 compression:ventilation ratio.
    Resuscitation 02/2013; · 4.10 Impact Factor
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    ABSTRACT: Rhythm analysis methods for shock advice during CPR are evaluated in terms of sensitivity and specificity. However, these figures do not convey the real impact that using these methods would have on the delivery of CPR. This study evaluates the impact on CPR delivery of a new rhythm analysis method.
    Computing in Cardiology Conference (CinC), 2013; 01/2013
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    ABSTRACT: The thoracic impedance (TI) signal, available in current automated external defibrillators, has been proposed as an indicator of the compression depth (CD) in animal models of cardiac arrest. This study analysed the linear relationship between the maximum CD and the fluctuation caused in the TI in 19 out-of-hospital cardiac arrest episodes. The mean of the CD maxima, Dmax, and the mean peak-to-peak fluctuation, Zpp, were computed for every 5 s from the CD and TI signals, respectively. Three analyses were performed: distributions of Dmax and Zpp in all episodes, linear relation between Dmax and Zpp (correlation coefficient for every episode, Re, and for the complete dataset, Rc) and time evolution of the correlation coefficient, Ri, for three consecutive intervals along every episode. Median (25th – 75th percentiles) for Zpp were 1.12 (0.78 – 1.48), 1.35 (0.94 – 1.89) and 1.67 (1.09 – 2.33) Ω for Dmax 51 mm respectively. High overlap between the three distributions was observed. Re varied between 0.04-0.83 (median=0.34), and Rc was 0.27. Time evolution of Ri did not show any tendency. Ri varied between 0.06-0.94 (median=0.52). Linearity between Dmax and Zpp showed high variability between episodes in humans. The correlation coefficient for the complete dataset was low.
    Computing in Cardiology Conference (CinC), 2013; 01/2013
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    ABSTRACT: During cardiopulmonary resuscitation, excessive ventilation rates decrease cardiac output, thus reducing the chance of survival. We have developed a simple method to automatically detect ventilations based on the analysis of the thoracic impedance signal recorded through defibrillation pads. We used 18 out-of hospital cardiac arrest episodes that contained both ventilations provided during chest compressions (CCs) and during pauses in CCs. The detection algorithm first identified fluctuations on the preprocessed impedance signal. Then, it characterized the fluctuations by features for amplitude, duration and slope. Finally, a decision system based on static and dynamic thresholds was applied in order to determine whether each fluctuation corresponded to a ventilation. Sensitivity (Se) and positive predictive value (PPV) for the test set (2831 ventilations) were 97% and 94%, respectively. Before intubation (343 ventilations), Se and PPV were 92% and 79%, and 97% and 97% after intubation. The performance was very similar for intervals with and without CCs. The proposed method could be implemented in automatic external defibrillators for ventilation rate monitoring.
    Computing in Cardiology Conference (CinC), 2013; 01/2013
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    ABSTRACT: To design the core algorithm of a high-temporal resolution rhythm analysis algorithm for automated external defibrillators (AEDs) valid for adults and children. Records from adult and paediatric patients were used all together to optimize and test the performance of the algorithm. A total of 574 shockable and 1126 nonshockable records from 1379 adult patients, and 57 shockable and 503 nonshockable records from 377 children aged between 1 and 8 years were used. The records were split into two groups for development and testing. The core algorithm analyses ECG segments of 3.2s duration and classifies the segments as nonshockable or likely shockable combining a time, slope and frequency domain analysis to detect normally conducted QRS complexes. The algorithm correctly identified 98% of nonshockable segments, 97.5% in adults and 98.4% in children, and identified 99.5% of shockable segments as likely shockable, 100% in adults and 96% in children. When likely shockable segments were further analysed in terms of regularity, spectral content and heart rate to form a complete rhythm analysis algorithm the overall specificity increased to 99.6% and the sensitivity was 99.1%. Paediatric and adult rhythms can be accurately diagnosed using 3.2s ECG segments. A single algorithm safe for children and adults can simplify AED use, and its high temporal resolution shortens pre-shock pauses which may contribute to improve resuscitation outcome.
    Resuscitation 02/2012; 83(9):1090-7. · 4.10 Impact Factor
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    ABSTRACT: A shock advice algorithm (SAA) that reliably diagnoses the rhythm during cardiopulmonary resuscitation (CPR) would avoid unnecessary CPR interruptions and increase the probability of a successful resuscitation. Current approaches based on filtering the CPR artifact from the ECG or analyzing the corrupted ECG do not meet the American Heart Association's (AHA) requirements for SAA. This study presents a preliminary design of a SAA to classify the rhythm during CPR. It is based on the analysis of five non-overlapping 3 s segments of the corrupt and the filtered ECG. A total of 290 non-shockable and 89 shockable records were analyzed and a specificity above the 95% AHA goal was obtained for a 89.5% sensitivity, only half a point below the AHA goal.
    Computing in Cardiology (CinC), 2012; 01/2012
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    ABSTRACT: Circulation detection by checking the carotid pulse in cardiac arrest patients during cardiopulmonary resuscitation (CPR) has been reported inaccurate. Thoracic impedance (TI) measured via the defibrillator pads has been recently proposed for the assessment of circulation during CPR. However, ventilation artefacts severely corrupt and spectrally overlap the cardiac component of the TI. This study proposes an adaptive scheme to suppress respiration artefacts and extract the circulation component from the TI recorded through the defibrillation pads. The database consisted of 12 records from hemodynamically stable volunteers including the TI and the ECG. Each record contained intervals without respiration and intervals with 5 respiration rates. R-R intervals detected in the ECG were used to estimate the instantaneous frequency of the cardiac component of the TI. An adaptive scheme was applied to estimate the amplitude and phase of the three-harmonic model of the cardiac component. The method was evaluated in terms of signal-to-noise ratio (SNR) improvement, and in terms of the normalized cross correlation coefficient of the circulation components obtained during respiration and in artefact-free intervals.
    Computing in Cardiology (CinC), 2012; 01/2012
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    ABSTRACT: The recommended treatment for out-of-hospital cardiac arrest (OHCA) is immediate cardiopulmonary resuscitation (CPR) and early electrical defibrillation. During CPR, chest compressions and ventilations should be provided with a compression-ventilation ratio of 30:2. Chest compressions and ventilations induce fast and slow fluctuations, respectively, on the transthoracic impedance (TTI). In this work we present a method for the automatic detection of pauses in chest compression using the TTI. The fuctuations induced by chest compressions were first isolated and emphasized and then, using an adaptive threshold, the intervals without chest compressions were identified. The method was adjusted and evaluated using a dataset of 3596 pauses corresponding to OHCA episodes. The mean duration of the pauses was 7.0 ± 6.2 s. For the test set, the sensitivity and the positive predictive value were 93.9% and 96.2%, respectively. This method could be used for both online and offine CPR quality evaluation or to detect artifact free ECG intervals in which a rhythm assesment could be launched.
    Computing in Cardiology (CinC), 2012; 01/2012
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    ABSTRACT: To demonstrate that the instantaneous chest compression rate can be accurately estimated from the transthoracic impedance (TTI), and that this estimated rate can be used in a method to suppress cardiopulmonary resuscitation (CPR) artefacts. A database of 372 records, 87 shockable and 285 non-shockable, from out-of-hospital cardiac arrest episodes, corrupted by CPR artefacts, was analysed. Each record contained the ECG and TTI obtained from the defibrillation pads and the compression depth (CD) obtained from a sternal CPR pad. The chest compression rates estimated using TTI and CD were compared. The CPR artefacts were then filtered using the instantaneous chest compression rates estimated from the TTI or CD signals. The filtering results were assessed in terms of the sensitivity and specificity of the shock advice algorithm of a commercial automated external defibrillator. The correlation between the mean chest compression rates estimated using TTI or CD was r=0.98 (95% confidence interval, 0.97-0.98). The sensitivity and specificity after filtering using CD were 95.4% (88.4-98.6%) and 87.0% (82.6-90.5%), respectively. The sensitivity and specificity after filtering using TTI were 95.4% (88.4-98.6%) and 86.3% (81.8-89.9%), respectively. The instantaneous chest compression rate can be accurately estimated from TTI. The sensitivity and specificity after filtering are similar to those obtained using the CD signal. Our CPR suppression method based exclusively on signals acquired through the defibrillation pads is as accurate as methods based on signals obtained from CPR feedback devices.
    Resuscitation 12/2011; 83(6):692-8. · 4.10 Impact Factor
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    ABSTRACT: A reliable diagnosis by automated external defibrillators (AED) during cardiopulmonary resuscitation (CPR) would reduce hands-off time, thus increasing the resuscitation success. Several filters based on one (dual-channel) or multiple (multi-channel) reference signals have been proposed to remove the artifact induced on the ECG by chest compressions. However these filters were optimized and their performance evaluated using different ECG data and AED algorithms. In this study, we have re-optimized and evaluated the performance of two dual-channel filters using the same ECG data and AED algorithm used to develop and test a well known multi-channel filter. The accuracy of the tested multi-channel and dual-channel filters was similar. Dual-channel filters need fewer reference channels and a lower computational burden and can be more easily incorporated to current AED.
    01/2011;
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    ABSTRACT: Diagnosis during cardiopulmonary resuscitation (CPR) is highly desirable because it has been reported to be determinant for a successful outcome from sudden cardiac arrest. This study evaluates the accuracy and the time-effect of applying CPR artefact suppression prior to rhythm classification with long out-of-hospital cardiac arrest episodes. A total of 191 episodes were considered corresponding to intervals between two consecutive defibrillation attempts. 127 maintained ventricular fibrillation as the underlying rhythm and 64 began in asystole but converted to a final ventricular fibrillation rhythm. The records comprised subintervals with and without chest compressions. A dual-channel adaptive filter is used to suppress the CPR artefact prior to applying the shock advice algorithm. The accuracy of the diagnosis during CPR is evaluated and the reduction in time-to-defibrillation and no-flow-time were computed.
    01/2011;
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    ABSTRACT: Detection of Ventricular fibrillation (VF) in automated external defibrillators (AED) is tested following the recommendations of the American Heart Association (AHA). However, nonshockable out-of-hospital cardiac arrest (OHCA) rhythms may be very different from those covered in the AHA recommendations. In this study we compare the performance of four VF detection parameters for a testing database of rhythms covered by the AHA recommendations and a database of OHCA rhythms. Spectral parameters performed better than parameters related to the heart rate or the complexity for the testing database but worse for the OHCA database. The performance of the parameters was very different in an algorithm design scenario compliant with the AHA statement and in a real resuscitation scenario.
    01/2011;
  • Resuscitation 01/2010; 81(2). · 4.10 Impact Factor
  • Resuscitation 01/2010; 81(2). · 4.10 Impact Factor
  • Resuscitation 83:e8. · 4.10 Impact Factor