ECG-based Random Forest Classifier for Cardiac Arrest Rhythms

Manibardo, Eric L.; Irusta, Unai; Ser, Javier Del; Aramendi, Elisabete; Isasi, Iraia; Olabarria, Mikel; Corcuera, Carlos; Veintemillas, Jose; Larrea, Andima

doi:10.1109/embc.2019.8857893

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…Besides, the hands-off pauses in chest compressions required for artifact-free ECG analysis in AEDs should be shortened, considering that 5 s to 10 s delay of the shock after stopping chest compressions reduces the probability of the defibrillation success and survival [5,6]. Therefore, effective strategies for early shock decision have been reported within the AED setting, such as early starting of the ECG analysis at the end of chest compressions [7] or during ventilation pauses [8]; as well as short ECG analysis durations, varying across studies from 2 s to 10 s [3,7,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…During the last decades, Sh/NSh rhythm detection strategies employ comprehensive measurements of the ECG waveform morphology and heart rhythm periodicity in the time-domain [7,9,11,14,16,[18][19][20][21][22][23][24][25]28,29], specific frequency bands via band-pass filtering for QRS or VF enhancement [11,[13][14][15][21][22][23]30], Fourier transform [11,14,[22][23][24]26,31,32] or time-frequency ECG transformations [10,24,27,33], as well as nonlinear ECG measures [11,12,14,17,[22][23][24]34,35]. Although sets of those classical features measured with computer-based programs have been shown to present good discrimination between Sh/NSh rhythms with state-of-the-art machine learning classifiers (discriminant analysis, logistic regression, bagging and random forests, support vector machines, genetic algorithms) [15,<...>…”

Section: Introductionmentioning

confidence: 99%

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Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Krasteva

Ménétré

Didon

et al. 2020

Sensors

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Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocardiogram (ECG) and can generally provide high-output diagnostic accuracy if subjected to robust training and optimization on large datasets at high computational cost. So far, limited research and optimization of DNNs in shock advisory systems is found on large ECG arrhythmia databases from out-of-hospital cardiac arrests (OHCA). The objective of this study is to optimize the hyperparameters (HPs) of deep convolutional neural networks (CNN) for detection of shockable (Sh) and nonshockable (NSh) rhythms, and to validate the best HP settings for short and long analysis durations (2–10 s). Large numbers of (Sh + NSh) ECG samples were used for training (720 + 3170) and validation (739 + 5921) from Holters and defibrillators in OHCA. An end-to-end deep CNN architecture was implemented with one-lead raw ECG input layer (5 s, 125 Hz, 2.5 uV/LSB), configurable number of 5 to 23 hidden layers and output layer with diagnostic probability p ∈ [0: Sh,1: NSh]. The hidden layers contain N convolutional blocks × 3 layers (Conv1D (filters = Fi, kernel size = Ki), max-pooling (pool size = 2), dropout (rate = 0.3)), one global max-pooling and one dense layer. Random search optimization of HPs = {N, Fi, Ki}, i = 1, … N in a large grid of N = [1, 2, … 7], Fi = [5;50], Ki = [5;100] was performed. During training, the model with maximal balanced accuracy BAC = (Sensitivity + Specificity)/2 over 400 epochs was stored. The optimization principle is based on finding the common HPs space of a few top-ranked models and prediction of a robust HP setting by their median value. The optimal models for 1–7 CNN layers were trained with different learning rates LR = [10−5; 10−2] and the best model was finally validated on 2–10 s analysis durations. A number of 4216 random search models were trained. The optimal models with more than three convolutional layers did not exhibit substantial differences in performance BAC = (99.31–99.5%). Among them, the best model was found with {N = 5, Fi = {20, 15, 15, 10, 5}, Ki = {10, 10, 10, 10, 10}, 7521 trainable parameters} with maximal validation performance for 5-s analysis (BAC = 99.5%, Se = 99.6%, Sp = 99.4%) and tolerable drop in performance (<2% points) for very short 2-s analysis (BAC = 98.2%, Se = 97.6%, Sp = 98.7%). DNN application in future-generation shock advisory systems can improve the detection performance of Sh and NSh rhythms and can considerably shorten the analysis duration complying with resuscitation guidelines for minimal hands-off pauses.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Krasteva

Ménétré

Didon

et al. 2020

Sensors

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“…Random Forest is a probabilistic approach for growing an ensemble of random trees for classification [25]. Because the trees vote for the most popular class, classification accuracy is high.…”

Section: Random Forestmentioning

confidence: 99%

Machine Learning-Based ECG Signal Classification for Enhanced Early Detection of Doxorubicin-Induced Cardiotoxicity in Rats

Mohammed,

H.Hanafy,

abdo

et al. 2024

Preprint

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Cardiotoxicity, which leads to irreversible myocardial damage, is a major adverse effect associated with chemotherapy. Electrocardiogram (ECG) is an inexpensive, rapid, and simple tool that may provide valuable diagnostic information pertinent to cardiotoxicity. An automatic interpretation and classification of the ECG signals by machine learning algorithms is considered superior to human interpretation of the ECG which may not be able to early detect subtle alterations in the ECG and vary according to the experience of the specialist. The present work aimed at using different machine learning algorithms to classify ECG signals recorded from doxorubicin-injected rats. Rats were divided into four groups and each group was intraperitoneally injected with different cumulative doses of doxorubicin (0, 6, 12, and 18 mg/kg). ECG signal classification depended on multiple features that were extracted from the recorded signals under different conditions. K nearest neighbors’ algorithm achieved higher classification accuracy (99.83 %) than random forest (99.56 %), decision tree (99.54 %), artificial neural network (99.50 %), and support vector machine (99.38 %). Furthermore, the dose-dependent cardiotoxicity was validated via a histopathological examination of the left ventricle that indicated significant pathological alterations in the cardiac tissue. The present findings emphasized the potential of the machine learning-based enhanced detection of cardiotoxicity and validated the dose-dependent toxicity of doxorubicin in the cardiac left ventricle. This approach might be applicable clinically to avoid cardiotoxicity in chemotherapy-treated patients.

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“…While there are some studies that demonstrate models which can predict ECG rhythms in OHCA, 8 , 9 there are limited studies investigating prediction of refractory VF/VT specifically. One such study developed a random forest algorithm using ECG data with considerable performance.…”

Section: Introductionmentioning

confidence: 99%

Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS

Rahadian,

Okada,

Shahidah

et al. 2024

Resuscitation Plus

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ECG-based Random Forest Classifier for Cardiac Arrest Rhythms

Cited by 9 publications

References 28 publications

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms

Machine Learning-Based ECG Signal Classification for Enhanced Early Detection of Doxorubicin-Induced Cardiotoxicity in Rats

Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS

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