scholarly journals Automated Condition-Based Suppression of the CPR Artifact in ECG Data to Make a Reliable Shock Decision for AEDs during CPR

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8210
Author(s):  
Shirin Hajeb-Mohammadalipour ◽  
Alicia Cascella ◽  
Matt Valentine ◽  
Ki H. Chon
Keyword(s):  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Normal Sinus Rhythm ◽  
Spectral Characteristics ◽  
Stop Band ◽  
Decision Algorithm ◽  
Shockable Rhythm ◽  
Normal Sinus ◽  
Rhythm Analysis ◽  
Ecg Data

Cardiopulmonary resuscitation (CPR) corrupts the morphology of the electrocardiogram (ECG) signal, resulting in an inaccurate automated external defibrillator (AED) rhythm analysis. Consequently, most current AEDs prohibit CPR during the rhythm analysis period, thereby decreasing the survival rate. To overcome this limitation, we designed a condition-based filtering algorithm that consists of three stop-band filters which are turned either ‘on’ or ‘off’ depending on the ECG’s spectral characteristics. Typically, removing the artifact’s higher frequency peaks in addition to the highest frequency peak eliminates most of the ECG’s morphological disturbance on the non-shockable rhythms. However, the shockable rhythms usually have dynamics in the frequency range of (3–6) Hz, which in certain cases coincide with CPR compression’s harmonic frequencies, hence, removing them may lead to destruction of the shockable signal’s dynamics. The proposed algorithm achieves CPR artifact removal without compromising the integrity of the shockable rhythm by considering three different spectral factors. The dataset from the PhysioNet archive was used to develop this condition-based approach. To quantify the performance of the approach on a separate dataset, three performance metrics were computed: the correlation coefficient, signal-to-noise ratio (SNR), and accuracy of Defibtech’s shock decision algorithm. This dataset, containing 14 s ECG segments of different types of rhythms from 458 subjects, belongs to Defibtech commercial AED’s validation set. The CPR artifact data from 52 different resuscitators were added to artifact-free ECG data to create 23,816 CPR-contaminated data segments. From this, 82% of the filtered shockable and 70% of the filtered non-shockable ECG data were highly correlated (>0.7) with the artifact-free ECG; this value was only 13 and 12% for CPR-contaminated shockable and non-shockable, respectively, without our filtering approach. The SNR improvement was 4.5 ± 2.5 dB, averaging over the entire dataset. Defibtech’s rhythm analysis algorithm was applied to the filtered data. We found a sensitivity improvement from 67.7 to 91.3% and 62.7 to 78% for VF and rapid VT, respectively, and specificity improved from 96.2 to 96.5% and 91.5 to 92.7% for normal sinus rhythm (NSR) and other non-shockables, respectively.

Abstract 12792: Enhancing the Accuracy of Cardiac Rhythm Analysis in Automated External Defibrillators During Ongoing Cardiopulmonary Resuscitation by Applying a Deep Encoder-Decoder Filtering Model

Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Shirin Hajeb Mohammadalipour ◽  
Alicia Cascella ◽  
Matt Valentine ◽  
Ki Chon
Keyword(s):  
Cardiopulmonary Resuscitation ◽  
Signal To Noise Ratio ◽  
Normal Sinus Rhythm ◽  
Motion Artifact ◽  
Automated External Defibrillators ◽  
Normal Sinus ◽  
Before And After ◽  
Rhythm Analysis ◽  
Distinct Features ◽  
Ecg Data

Survival from out-of-hospital cardiac arrests depends on an accurate defibrillatory shock decision during cardiopulmonary resuscitation (CPR). Since chest compressions induce severe motion artifact in the electrocardiogram (ECG), current automatic external defibrillators (AEDs) do not perform CPR during the rhythm analysis period. However, performing continuous CPR is vital and dramatically increases the chance of survival. Hence, we demonstrate a novel application of a deep convolutional neural network encoder-decoder (CNNED) method in suppressing CPR artifact in near real-time using only ECG data. The encoder portion of the CNNED uses the frequency and phase contents derived via time-varying spectral analysis to learn distinct features that are representative of both the ECG signal and CPR artifact. The decoder portion takes the results from the encoder and reconstructs what is perceived as the motion artifact removed ECG data. These procedures are done via multitude of training of CNNED using many different arrhythmia contaminated with CPR. In this study, CPR-contaminated ECGs were generated by combining clean ECG with CPR artifacts from 52 different performers. ECG data from CUDB, VFDB, and SDDB datasets which belong to the Physionet’s Physiobank archive were used to create the training set containing 89,984 14-second ECG segments. The performance of the proposed CNNED was evaluated on a separate test set comprising of 23,816 CPR-contaminated 14-second ECG segments from 458 subjects. The results were evaluated by two metrics: signal-to-noise ratio (SNR), and Defibtech’s AED rhythm analysis algorithm. CNNED resulted in the increase of the mean SNR value from -3 dB to 5.63 dB and 6.3 dB for shockable and non-shockable rhythms, respectively. Comparing Defibtech’s AED rhythm classifier before and after applying CNNED on the CPR-contaminated ECG, the specificity improved from 96.57% to 99.31% for normal sinus rhythm, and from 91.5% to 96.57% for other non-shockable rhythms. The sensitivity of shockable detection also increased from 67.68% to 87.76% for ventricular fibrillation, and from 62.71% to 81.27% for ventricular tachycardia. These results indicate continuous and accurate AED rhythm analysis without stoppage of CPR using only ECG data.

scholarly journals Spectral characteristics of ventricular response to atrial fibrillation

1997 ◽  
Vol 273 (6) ◽  
pp. H2811-H2816 ◽  
Author(s):  
Junichiro Hayano ◽  
Fumiyasu Yamasaki ◽  
Seiichiro Sakata ◽  
Akiyoshi Okada ◽  
Seiji Mukai ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Spectral Characteristics ◽  
Short Term ◽  
Spectral Exponent ◽  
Linear Pattern ◽  
Regulatory Processes ◽  
Normal Sinus ◽  
Ventricular Response

To investigate the spectral characteristics of the fluctuation in ventricular response during atrial fibrillation (AF), R-R interval time series obtained from ambulatory electrocardiograms were analyzed in 45 patients with chronic AF and in 30 age-matched healthy subjects with normal sinus rhythm (SR). Although the 24-h R-R interval spectrum during SR showed a 1/ f noise-like downsloping linear pattern when plotted as log power against log frequency, the spectrum during AF showed an angular shape with a breakpoint at a frequency of 0.005 ± 0.002 Hz, by which the spectrum was separated into long-term and short-term components with different spectral characteristics. The short-term component showed a white noise-like flat spectrum with a spectral exponent (absolute value of the regression slope) of 0.05 ± 0.08 and an intercept at 10−2 Hz of 4.9 ± 0.3 log(ms2/Hz). The long-term component had a 1/ f noise-like spectrum with a spectral exponent of 1.26 ± 0.40 and an intercept at 10−4 Hz of 7.0 ± 0.3 log(ms2/Hz), which did not differ significantly from those for the spectrum during SR in the same frequency range [spectral exponent, 1.36 ± 0.06; intercept at 10−4 Hz, 7.1 ± 0.3 log(ms2/Hz)]. The R-R intervals during AF may be a sequence of uncorrelated values over the short term (within several minutes). Over the longer term, however, the R-R interval fluctuation shows the long-range negative correlation suggestive of underlying regulatory processes, and spectral characteristics indistinguishable from those for SR suggest that the long-term fluctuations during AF and SR may originate from similar dynamics of the cardiovascular regulatory systems.

scholarly journals Robust Discrimination of Normal Sinus Rhythm and Atrial Fibrillation on ECG Using a Multiscale Frequency Technique

2017 ◽  
Author(s):  
Shivaram Poigai Arunachalam ◽  
Elizabeth M. Annoni ◽  
Suraj Kapa ◽  
Siva K. Mulpuru ◽  
Paul A. Friedman ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Time Series Data ◽  
Normal Sinus Rhythm ◽  
The Body ◽  
Series Data ◽  
Robust Detection ◽  
Normal Sinus ◽  
Surface Ecg ◽  
Ecg Data

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia affecting approximately 3 million Americans, and is a prognostic marker for stroke, heart failure and even death [1]. 12-lead electrocardiogram (ECG) is used to monitor normal sinus rhythm (NSR) and also detect AF. Although the persistent form of AF can be detected relatively easy, detecting paroxysmal AF is often a challenge since requiring continuous monitoring, which becomes expensive and cumbersome to collect lot of ECG data [1]. Several researchers have attempted to develop new methods to discriminate NSR and AF which are based on R-R interval analysis, linear methods, filtering, spectral analysis, statistical approaches such as entropy etc. which faces limitation of successfully detecting AF of all types with high sensitivity and specificity using short time ECG data [1–3]. The major issues with these approaches is that they often distort the ECG by several pre-processing steps with filters, do not provide reliable discrimination using short ECG time series data and many of them lack real-time capability that makes it difficult to trust the data for diagnosis and treatment. Both clinical and scientific communities recognize these difficulties and the necessity to develop novel methods that can enable accurate monitoring and detection of AF [2]. In addition, robust detection and classification algorithms are essential for delivering appropriate therapy for implantable cardioverter defibrillators (ICD) to provide lifesaving timely action. In this work, the authors propose and demonstrate the application of a multiscale frequency (MSF) approach [4] for accurate detection and discrimination between AF and NSR ECG traces taken from publically available Physionet database. The MSF approach takes into account the contribution from various frequencies in ECG and thus yield valuable information regarding the chaotic nature of AF. Therefore, we demonstrate that MSF can capture the complexity of AF which is associated with higher MSF value compared with NSR thus enabling robust discrimination e AF manifests itself with numerous chaotic frequencies within the body surface ECG,. We validate the feasibility of this technique to discriminate NSR from AF.

Abstract 254: High Shock Efficacy and Algorithm Performance for a 150J Biphasic Waveform AED Used by Lay and BLS Responders During Out-of-hospital Cardiac Arrest

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_4) ◽  
Author(s):  
Stacy Gehman ◽  
Edward Kompare ◽  
Barbara Fink ◽  
Tim Johnson ◽  
Walter Hufford ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Normal Sinus Rhythm ◽  
Convenience Sample ◽  
Shock Delivery ◽  
Shockable Rhythm ◽  
Normal Sinus ◽  
High Shock ◽  
Biphasic Waveform ◽  
Post Shock ◽  
Hospital Cardiac Arrest

Introduction: Effective AED defibrillation of out of hospital cardiac arrest (OHCA) depends on the safe and effective identification of shockable rhythms, and on delivery of effective defibrillation energy. This report summarizes rhythm detection performance and shock efficacy during OHCA uses of Philips HeartStart Home and OnSite AEDs using non-escalating 150 J therapy. Methods: A convenience sample of 185 OHCA AED patient uses were reviewed by clinical experts. All analysis periods that resulted in AED rhythm advisories (Shock Advised or No Shock Advised) were annotated. Shockable rhythm categories include VF and polymorphic VT/flutter. Non-Shockable rhythm categories include normal sinus rhythm, other rhythms (e.g., atrial fibrillation/flutter, bradycardia, SVT, idioventricular, bundle branch block), and asystole. Intermediate rhythms (benefits of defibrillation are limited or uncertain) were not included. Post-shock rhythm was categorized as shockable, non-shockable, or undeterminable (rhythms corrupted by CPR artifact or pads removal within 5-s of shock delivery). Shock success was defined as conversion to a non-shockable rhythm within 5-s post-shock. Results: A total of 487 analysis periods resulted in AED rhythm advisories, with 175 annotated as Shockable and 312 Non-shockable. Sensitivity and specificity (n/N, Exact 95% CI) were 97.7% (171/175, 94.3%, 99.4%) and 100% (312/312, 98.8%, 100.0%) respectively. A total of 165 shocks were delivered to 100 patients with 5 undeterminable post-shock rhythms. The remaining 160 shocks were delivered to 156 Shockable rhythm episodes. All shock efficacy was 96.9% (155/160, 92.9%, 99.0%): 150 episodes converted to non-shockable rhythms after one shock (96.2% (150/156, 91.8%, 98.6%)); 154 after two shocks (98.7% (154/156, 95.4%, 99.8%)); and 155 after three shocks, the first two of which were undeterminable (99.4% (155/156, 96.5%, 100.0%)). The remaining episode had a failed first shock, followed by an undeterminable second shock, which was the last shock of the use. Conclusion: For these 150J fixed-energy AEDs, OHCA defibrillation is safe (100% specificity), and effective (97.7% sensitivity; 96.2% single shock effectiveness; 98.7% after two shocks; 99.4% after three shocks).

scholarly journals TASK-1 current is inhibited by phosphorylation during human and canine chronic atrial fibrillation

2015 ◽  
Vol 308 (2) ◽  
pp. H126-H134 ◽  
Author(s):  
Erin Harleton ◽  
Alessandra Besana ◽  
Parag Chandra ◽  
Peter Danilo ◽  
Tove S. Rosen ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Human Subjects ◽  
Phosphorylation Site ◽  
Chronic Atrial Fibrillation ◽  
Resting Membrane Potential ◽  
Atrial Remodeling ◽  
Protein Levels ◽  
Atrial Tissue ◽  
Normal Sinus

Atrial fibrillation (AF) is a common arrhythmia with significant morbidities and only partially adequate therapeutic options. AF is associated with atrial remodeling processes, including changes in the expression and function of ion channels and signaling pathways. TWIK protein-related acid-sensitive K+ channel (TASK)-1, a two-pore domain K+ channel, has been shown to contribute to action potential repolarization as well as to the maintenance of resting membrane potential in isolated myocytes, and TASK-1 inhibition has been associated with the induction of perioperative AF. However, the role of TASK-1 in chronic AF is unknown. The present study investigated the function, expression, and phosphorylation of TASK-1 in chronic AF in atrial tissue from chronically paced canines and in human subjects. TASK-1 current was present in atrial myocytes isolated from human and canine hearts in normal sinus rhythm but was absent in myocytes from humans with AF and in canines after the induction of AF by chronic tachypacing. The addition of phosphatase to the patch pipette rescued TASK-1 current from myocytes isolated from AF hearts, indicating that the change in current is phosphorylation dependent. Western blot analysis showed that total TASK-1 protein levels either did not change or increased slightly in AF, despite the absence of current. In studies of perioperative AF, we have shown that phosphorylation of TASK-1 at Thr383 inhibits the channel. However, phosphorylation at this site was unchanged in atrial tissue from humans with AF or in canines with chronic pacing-induced AF. We conclude that phosphorylation-dependent inhibition of TASK-1 is associated with AF, but the phosphorylation site responsible for this inhibition remains to be identified.

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