Monitoring a skipped heartbeat: a real-time premature ventricular contraction (PVC) monitor

González, Laura López; Walker, Katherine; Challa, S. Sastry; Bent, Brinnae

doi:10.1109/vcacs.2016.7888785

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…The denoised ECG,

, and the detail coefficients

were used to obtain twenty five ECG features, selected using recursive feature elimination from a set of over 200 features (consult [ 25 ] for the details). The most relevant features were classical VF detection features like VFleak or

[ 22 , 45 ] computed from

, and a rich set of features obtained from the detail coefficients

, such as: sample entropy (

), the mean and standard deviation of the absolute value of the signal (

) and its slope (

), and the Hjorth mobility (

) and complexity (

) indices [ 46 ]. A detailed description of the algorithm is found in [ 25 ], with a detailed bibliography for the computation of the features.…”

Section: Methodsmentioning

confidence: 99%

Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks

Isasi

Irusta

Aramendi

et al. 2020

Entropy

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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.

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“…The denoised ECG,

, and the detail coefficients

[ 22 , 45 ] computed from

, and a rich set of features obtained from the detail coefficients

, such as: sample entropy (

), the mean and standard deviation of the absolute value of the signal (

) and its slope (

), and the Hjorth mobility (

) and complexity (

) indices [ 46 ]. A detailed description of the algorithm is found in [ 25 ], with a detailed bibliography for the computation of the features.…”

Section: Methodsmentioning

confidence: 99%

Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks

Isasi

Irusta

Aramendi

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…Features from 10 to 15 in the left column and from 10 to 12 in the right column were introduced by Rad et al [8]. Fuzzy Entropy (FuzzEn), the Signal Integral parameter (SignInt), the Peak Power Frequency (PPF), the Smoothed Nonlinear Energy Operator (SNEO) and the Hjorth Mobility parameter are described in [9,12], [13], [14], [15] and [16], respectively. The remaining features were designed for this work: the number of QRS-like peaks (Npeak) and the Euclidean distance between the Hjorth Mobility and the Hjorth Mobility of the second degree (Mx2).…”

Section: Resultsmentioning

confidence: 99%

ECG Rhythm Analysis During Manual Chest Compressions Using an Artefact Removal Filter and Random Forest Classifiers

Isasi¹,

Rad²,

Irusta³

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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Interruptions in cardiopulmonary resuscitation (CPR) decrease the chances of survival. However, CPR must be interrupted for a reliable rhythm analysis because chest compressions (CCs) induce artifacts in the ECG. This paper introduces a double-stage shock advice algorithm (SAA) for a reliable rhythm analysis during manual CCs. The method used two configurations of the recursive least-squares (RLS) filter to remove CC artifacts from the ECG. For each filtered ECG segment over 200 shock/no-shock decision features were computed and fed into a random forest (RF) classifier to select the most discriminative 25 features. The proposed SAA is an ensemble of two RF classifiers which were trained using the 25 features derived from different filter configurations. Then, the average value of class posterior probabilities was used to make a final shock/no-shock decision. The dataset was comprised of 506 shockable and 1697 non-shockable rhythms which were labelled by expert rhythm resuscitation reviewers in artifact-free intervals. Shock/no-shock diagnoses obtained through the proposed double-stage SAA were compared with the rhythm annotations to obtain the Sensitivity (Se), Specificity (Sp) and balanced accuracy (BAC) of the method. The results were 93.5%, 96.5% and 95.0%, respectively.

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“…• Statistical analysis (54 features). Nine features were calculated to characterize the statistical distribution of the signal amplitude: interquartile ranges (IQR) [15], mean and standard deviation of the absolute value of the amplitudes (MeanAbs and StdAbs) and slopes (MeanAbs1 and StdAbs1), Skewness (Skew), Kurtosis (Kurt) [11], and the Hjorth mobility and complexity (Hmb and Hcmp) [44]. All the features were computed forŝ den and d 3 − d 7 .…”

Section: B Stationary Wavelet Transformmentioning

confidence: 99%

Automatic Cardiac Rhythm Classification With Concurrent Manual Chest Compressions

et al. 2019

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Electrocardiogram (EKG) based classification of out-of-hospital cardiac arrest (OHCA) rhythms is important to guide treatment and to retrospectively elucidate the effects of therapy on patient response. OHCA rhythms are grouped into five categories: ventricular fibrillation (VF) and tachycardia (VT), asystole (AS), pulseless electrical activity (PEA), and pulse-generating rhythms (PR). Clinically these rhythms are grouped into broader categories like shockable (VF/VT), non-shockable (AS/PEA/PR), or organized (ORG, PEA/PR). OHCA rhythm classification is further complicated because EKGs are corrupted by cardiopulmonary resuscitation (CPR) artifacts. The objective of this study was to demonstrate a framework for automatic multiclass OHCA rhythm classification in the presence of CPR artifacts. In total, 2133 EKG segments from 272 OHCA patients were used: 580 AS, 94 PR, 953 PEA, 479 VF, and 27 VT. CPR artifacts were adaptively filtered, 93 features were computed from the stationary wavelet transform analysis, and random forests were used for classification. A repeated stratified nested cross-validation procedure was used for feature selection, parameter tuning, and model assessment. Data were partitioned patient-wise. The classifiers were evaluated using per class sensitivity, and the unweighted mean of sensitivities (UMS) as a global performance metric. Four levels of clinical detail were studied: shock/no-shock, shock/AS/ORG, VF/VT/AS/ORG, and VF/VT/AS/PEA/PR. The median UMS (interdecile range) for the 2, 3, 4, and 5-class classifiers were: 95.4% (95.1-95.6), 87.6% (87.3-88.1), 80.6% (79.3-81.8), and 71.9% (69.5-74.6), respectively. For shock/no-shock decisions sensitivities were 93.5% (93.0-93.9) and 97.2% (97.0-97.4), meeting clinical standards for artifact-free EKG. The UMS for five classes with CPR artifacts was 5.8-points below that of the best algorithms without CPR artifacts, but improved the UMS of latter by over 19-points for EKG with CPR artifacts. A robust and accurate approach for multiclass OHCA rhythm classification during CPR has been demonstrated, improving the accuracy of the current state-of-the-art methods.INDEX TERMS Out-of-hospital cardiac arrest (OHCA), electrocardiogram (EKG), cardiopulmonary resuscitation (CPR), adaptive filter, stationary wavelet transform (SWT), random forest (RF) classifier. I. INTRODUCTIONOut-of-hospital cardiac arrest (OHCA) is a leading cause of death in the industrialized world. In Europe the estimated annual average incidence of ambulance treated casesThe associate editor coordinating the review of this manuscript and approving it for publication was Nuno Garcia. is 41 (range 19-104) per 100 000 persons [1]. Patients in cardiac arrest lose their cardiac and respiratory function, and die within minutes if not treated. Treatment consists of highly time-sensitive interventions such as: recognition, call for help, cardiopulmonary resuscitation (CPR), defibrillation, and post-resuscitation care. Bystanders and lay rescuers can provide CPR to maintain an artificial perfus...

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Monitoring a skipped heartbeat: a real-time premature ventricular contraction (PVC) monitor

Cited by 4 publications

References 8 publications

Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks

Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks

ECG Rhythm Analysis During Manual Chest Compressions Using an Artefact Removal Filter and Random Forest Classifiers

Automatic Cardiac Rhythm Classification With Concurrent Manual Chest Compressions

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