# Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest

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## Abstract

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## 1. Introduction

## 2. Data Collection

## 3. Proposed DNN Architectures

#### 3.1. First Architecture: Fully Convolutional Neural Network

#### 3.2. Second Architecture: CNN Combined with a Recurrent Layer

#### 3.3. Training Process

#### 3.4. Uncertainty Estimation

## 4. Baseline Approaches

**RF:**Introduced in [56], RF constructs many weak learners, each trained with a certain proportion of the training data, $\phi $. Each subset is generated by resampling with replacement. Each weak learner is a tree, and only $\psi $ features are considered (drawn randomly from an uniform distribution) at each node. The final decision is made by majority voting. We set the number of trees to 300, and optimized the hyper-parameters $\phi $ and $\psi $.**Support vector machine (SVM):**Given a feature vector $\mathit{v}$, the SVM makes the prediction using the following formula [57]:$${y}^{\left(pred\right)}=\mathrm{sign}\left(\right)open="("\; close=")">b+\sum _{i=1}^{{N}_{s}}{w}_{i}K(\mathit{v},{\mathit{v}}_{\mathit{i}})$$$$K(\mathit{v},{\mathit{v}}_{\mathit{i}})=exp(-{\gamma}_{s}\left|\right|\mathit{v}-{\mathit{v}}_{\mathit{i}}{\left|\right|}^{2})$$**Kernel logistic regression (KLR):**This is a version of the well-known logistic regression by applying a kernel-trick [57,58]. The prediction is made using Equation (11), and the kernel of Equation (12). The hyper-parameters to optimize were the regularization-term ${\lambda}_{l}$ and ${\gamma}_{s}$.

## 5. Evaluation Setup and Optimization Process

#### 5.1. Evaluation Setup

#### 5.2. Hyper-Parameter Optimization Process

## 6. Results

## 7. Discussion and Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

ADAM | Adaptive moment estimation |

AED | Automated external defibrillator |

AS | Asystole |

AUC | Area under the curve |

BAC | Balanced accuracy |

BER | Balanced error rate |

BO | Bayesian optimization |

BO-GP | Bayesian optimization with Gaussian processes |

BO-TPE | Bayesian optimization with tree-structured parzen estimators |

CNN | Convolutional neural network |

CPR | Cardiopulmonary resuscitation |

DNN | Deep neural network |

ECG | Electrocardiogram |

BGRU | Bidirectional gated recurrent unit |

KLR | Kernel logistic regression |

OHCA | Out-of-hospital cardiac arrest |

PEA | Pulseless electrical activity |

PR | Pulsed rhythm |

RF | Random forest |

RNN | Recurrent neural network |

ROSC | Return of Spontaneous Circulation |

Se | Sensitivity |

Sp | Specificity |

SVM | Support vector machine |

TI | Thoracic impedance |

VF | Ventricular fibrillation |

VT | Ventricular tachycardia |

## References

- Gräsner, J.T.; Bossaert, L. Epidemiology and management of cardiac arrest: What registries are revealing. Best Pract. Res. Clin. Anaesthesiol.
**2013**, 27, 293–306. [Google Scholar] [CrossRef] [PubMed] - Berdowski, J.; Berg, R.A.; Tijssen, J.G.; Koster, R.W. Global incidences of out-of-hospital cardiac arrest and survival rates: Systematic review of 67 prospective studies. Resuscitation
**2010**, 81, 1479–1487. [Google Scholar] [CrossRef] [PubMed] - Deakin, C.D. The chain of survival: Not all links are equal. Resuscitation
**2018**, 126, 80–82. [Google Scholar] [CrossRef] [PubMed] - Perkins, G.D.; Handley, A.J.; Koster, R.W.; Castrén, M.; Smyth, M.A.; Olasveengen, T.; Monsieurs, K.G.; Raffay, V.; Gräsner, J.T.; Wenzel, V.; et al. European Resuscitation Council Guidelines for Resuscitation 2015: Section 2. Adult basic life support and automated external defibrillation. Resuscitation
**2015**, 95, 81–99. [Google Scholar] [CrossRef] [PubMed] - Bahr, J.; Klingler, H.; Panzer, W.; Rode, H.; Kettler, D. Skills of lay people in checking the carotid pulse. Resuscitation
**1997**, 35, 23–26. [Google Scholar] [CrossRef] - Eberle, B.; Dick, W.; Schneider, T.; Wisser, G.; Doetsch, S.; Tzanova, I. Checking the carotid pulse check: Diagnostic accuracy of first responders in patients with and without a pulse. Resuscitation
**1996**, 33, 107–116. [Google Scholar] [CrossRef] - Ochoa, F.J.; Ramalle-Gomara, E.; Carpintero, J.; Garcıa, A.; Saralegui, I. Competence of health professionals to check the carotid pulse. Resuscitation
**1998**, 37, 173–175. [Google Scholar] [CrossRef] - Lapostolle, F.; Le Toumelin, P.; Agostinucci, J.M.; Catineau, J.; Adnet, F. Basic cardiac life support providers checking the carotid pulse: Performance, degree of conviction, and influencing factors. Acad. Emerg. Med.
**2004**, 11, 878–880. [Google Scholar] [CrossRef] - Tibballs, J.; Russell, P. Reliability of pulse palpation by healthcare personnel to diagnose paediatric cardiac arrest. Resuscitation
**2009**, 80, 61–64. [Google Scholar] [CrossRef] [PubMed] - Soar, J.; Nolan, J.; Böttiger, B.; Perkins, G.; Lott, C.; Carli, P.; Pellis, T.; Sandroni, C.; Skrifvars, M.; Smith, G.; et al. Section 3. Adult advanced life support: European Resuscitation Council Guidelines for Resuscitation 2015. Resuscitation
**2015**, 95, 100–147. [Google Scholar] [CrossRef] - Ruppert, M.; Reith, M.W.; Widmann, J.H.; Lackner, C.K.; Kerkmann, R.; Schweiberer, L.; Peter, K. Checking for breathing: Evaluation of the diagnostic capability of emergency medical services personnel, physicians, medical students, and medical laypersons. Ann. Emerg. Med.
**1999**, 34, 720–729. [Google Scholar] [CrossRef] - Perkins, G.D.; Stephenson, B.; Hulme, J.; Monsieurs, K.G. Birmingham assessment of breathing study (BABS). Resuscitation
**2005**, 64, 109–113. [Google Scholar] [CrossRef] - Zengin, S.; Gümüşboğa, H.; Sabak, M.; Eren, Ş.H.; Altunbas, G.; Al, B. Comparison of manual pulse palpation, cardiac ultrasonography and Doppler ultrasonography to check the pulse in cardiopulmonary arrest patients. Resuscitation
**2018**, 133, 59–64. [Google Scholar] [CrossRef] [PubMed] - Clattenburg, E.J.; Wroe, P.; Brown, S.; Gardner, K.; Losonczy, L.; Singh, A.; Nagdev, A. Point-of-care ultrasound use in patients with cardiac arrest is associated prolonged cardiopulmonary resuscitation pauses: A prospective cohort study. Resuscitation
**2018**, 122, 65–68. [Google Scholar] [CrossRef] - in’t Veld, M.A.H.; Allison, M.G.; Bostick, D.S.; Fisher, K.R.; Goloubeva, O.G.; Witting, M.D.; Winters, M.E. Ultrasound use during cardiopulmonary resuscitation is associated with delays in chest compressions. Resuscitation
**2017**, 119, 95–98. [Google Scholar] [CrossRef] [PubMed] - Babbs, C.F. We still need a real-time hemodynamic monitor for CPR. Resuscitation
**2013**, 84, 1297–1298. [Google Scholar] [CrossRef] - Irusta, U.; Ruiz, J.; Aramendi, E.; de Gauna, S.R.; Ayala, U.; Alonso, E. A high-temporal resolution algorithm to discriminate shockable from nonshockable rhythms in adults and children. Resuscitation
**2012**, 83, 1090–1097. [Google Scholar] [CrossRef] [PubMed] - Figuera, C.; Irusta, U.; Morgado, E.; Aramendi, E.; Ayala, U.; Wik, L.; Kramer-Johansen, J.; Eftestøl, T.; Alonso-Atienza, F. Machine learning techniques for the detection of shockable rhythms in automated external defibrillators. PLoS ONE
**2016**, 11, e0159654. [Google Scholar] [CrossRef] - Li, Q.; Rajagopalan, C.; Clifford, G.D. Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans. Biomed. Eng.
**2014**, 61, 1607–1613. [Google Scholar] [PubMed] - Jekova, I.; Krasteva, V. Real time detection of ventricular fibrillation and tachycardia. Physiol. Meas.
**2004**, 25, 1167. [Google Scholar] [CrossRef] [PubMed] - Myerburg, R.J.; Halperin, H.; Egan, D.A.; Boineau, R.; Chugh, S.S.; Gillis, A.M.; Goldhaber, J.I.; Lathrop, D.A.; Liu, P.; Niemann, J.T.; et al. Pulseless electric activity: Definition, causes, mechanisms, management, and research priorities for the next decade: Report from a National Heart, Lung, and Blood Institute workshop. Circulation
**2013**, 128, 2532–2541. [Google Scholar] [CrossRef] [PubMed] - Ayala, U.; Irusta, U.; Ruiz, J.; Eftestøl, T.; Kramer-Johansen, J.; Alonso-Atienza, F.; Alonso, E.; González-Otero, D. A reliable method for rhythm analysis during cardiopulmonary resuscitation. BioMed Res. Int.
**2014**, 2014, 872470. [Google Scholar] [CrossRef] [PubMed] - Johnston, P.; Imam, Z.; Dempsey, G.; Anderson, J.; Adgey, A. The transthoracic impedance cardiogram is a potential haemodynamic sensor for an automated external defibrillator. Eur. Heart J.
**1998**, 19, 1879–1888. [Google Scholar] [CrossRef] [Green Version] - Pellis, T.; Bisera, J.; Tang, W.; Weil, M.H. Expanding automatic external defibrillators to include automated detection of cardiac, respiratory, and cardiorespiratory arrest. Crit. Care Med.
**2002**, 30, S176–S178. [Google Scholar] [CrossRef] [PubMed] - Losert, H.; Risdal, M.; Sterz, F.; Nysæther, J.; Köhler, K.; Eftestøl, T.; Wandaller, C.; Myklebust, H.; Uray, T.; Aase, S.O.; et al. Thoracic-impedance changes measured via defibrillator pads can monitor signs of circulation. Resuscitation
**2007**, 73, 221–228. [Google Scholar] [CrossRef] - Cromie, N.A.; Allen, J.D.; Turner, C.; Anderson, J.M.; Adgey, A.A.J. The impedance cardiogram recorded through two electrocardiogram/defibrillator pads as a determinant of cardiac arrest during experimental studies. Crit. Care Med.
**2008**, 36, 1578–1584. [Google Scholar] [CrossRef] - Cromie, N.A.; Allen, J.D.; Navarro, C.; Turner, C.; Anderson, J.M.; Adgey, A.A.J. Assessment of the impedance cardiogram recorded by an automated external defibrillator during clinical cardiac arrest. Crit. Care Med.
**2010**, 38, 510–517. [Google Scholar] [CrossRef] - Risdal, M.; Aase, S.O.; Kramer-Johansen, J.; Eftesol, T. Automatic identification of return of spontaneous circulation during cardiopulmonary resuscitation. IEEE Trans. Biomed. Eng.
**2008**, 55, 60–68. [Google Scholar] [CrossRef] - Alonso, E.; Aramendi, E.; Daya, M.; Irusta, U.; Chicote, B.; Russell, J.K.; Tereshchenko, L.G. Circulation detection using the electrocardiogram and the thoracic impedance acquired by defibrillation pads. Resuscitation
**2016**, 99, 56–62. [Google Scholar] [CrossRef] - Lee, Y.; Shin, H.; Choi, H.J.; Kim, C. Can pulse check by the photoplethysmography sensor on a smart watch replace carotid artery palpation during cardiopulmonary resuscitation in cardiac arrest patients? a prospective observational diagnostic accuracy study. BMJ Open
**2019**, 9. [Google Scholar] [CrossRef] - Wijshoff, R.W.; van Asten, A.M.; Peeters, W.H.; Bezemer, R.; Noordergraaf, G.J.; Mischi, M.; Aarts, R.M. Photoplethysmography-based algorithm for detection of cardiogenic output during cardiopulmonary resuscitation. IEEE Trans. Biomed. Eng.
**2015**, 62, 909–921. [Google Scholar] [CrossRef] [PubMed] - Brinkrolf, P.; Borowski, M.; Metelmann, C.; Lukas, R.P.; Pidde-Küllenberg, L.; Bohn, A. Predicting ROSC in out-of-hospital cardiac arrest using expiratory carbon dioxide concentration: Is trend-detection instead of absolute threshold values the key? Resuscitation
**2018**, 122, 19–24. [Google Scholar] [CrossRef] - Wei, L.; Chen, G.; Yang, Z.; Yu, T.; Quan, W.; Li, Y. Detection of spontaneous pulse using the acceleration signals acquired from CPR feedback sensor in a porcine model of cardiac arrest. PLoS ONE
**2017**, 12, e0189217. [Google Scholar] [CrossRef] [PubMed] - Elola, A.; Aramendi, E.; Irusta, U.; Del Ser, J.; Alonso, E.; Daya, M. ECG-based pulse detection during cardiac arrest using random forest classifier. Med. Biol. Eng. Comput.
**2019**, 57, 453–462. [Google Scholar] [CrossRef] [PubMed] - Faust, O.; Hagiwara, Y.; Hong, T.J.; Lih, O.S.; Acharya, U.R. Deep learning for healthcare applications based on physiological signals: A review. Comput. Methods Programs Biomed.
**2018**, 161, 1–13. [Google Scholar] [CrossRef] - Shen, S.; Yang, H.; Li, J.; Xu, G.; Sheng, M. Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data. Entropy
**2018**, 20, 990. [Google Scholar] [CrossRef] - Almgren, K.; Krishna, M.; Aljanobi, F.; Lee, J. AD or Non-AD: A Deep Learning Approach to Detect Advertisements from Magazines. Entropy
**2018**, 20, 982. [Google Scholar] [CrossRef] - Cohen, I.; David, E.O.; Netanyahu, N.S. Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images. Entropy
**2019**, 21, 221. [Google Scholar] [CrossRef] - Al Rahhal, M.M.; Bazi, Y.; Al Zuair, M.; Othman, E.; BenJdira, B. Convolutional neural networks for electrocardiogram classification. J. Med. Biol. Eng.
**2018**, 38, 1014–1025. [Google Scholar] [CrossRef] - Kiranyaz, S.; Ince, T.; Gabbouj, M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans. Biomed. Eng.
**2016**, 63, 664–675. [Google Scholar] [CrossRef] - Acharya, U.R.; Fujita, H.; Lih, O.S.; Hagiwara, Y.; Tan, J.H.; Adam, M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf. Sci.
**2017**, 405, 81–90. [Google Scholar] [CrossRef] - Xia, Y.; Wulan, N.; Wang, K.; Zhang, H. Detecting atrial fibrillation by deep convolutional neural networks. Comput. Biol. Med.
**2018**, 93, 84–92. [Google Scholar] [CrossRef] - Acharya, U.R.; Fujita, H.; Oh, S.L.; Hagiwara, Y.; Tan, J.H.; Adam, M. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci.
**2017**, 415, 190–198. [Google Scholar] [CrossRef] - Lipton, Z.C.; Kale, D.C.; Elkan, C.; Wetzel, R. Learning to diagnose with LSTM recurrent neural networks. arXiv, 2015; arXiv:1511.03677. [Google Scholar]
- Chauhan, S.; Vig, L. Anomaly detection in ECG time signals via deep long short-term memory networks. In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Paris, France, 19–21 October 2015; pp. 1–7. [Google Scholar]
- Alonso, E.; Ruiz, J.; Aramendi, E.; González-Otero, D.; de Gauna, S.R.; Ayala, U.; Russell, J.K.; Daya, M. Reliability and accuracy of the thoracic impedance signal for measuring cardiopulmonary resuscitation quality metrics. Resuscitation
**2015**, 88, 28–34. [Google Scholar] [CrossRef] - Ayala, U.; Eftestøl, T.; Alonso, E.; Irusta, U.; Aramendi, E.; Wali, S.; Kramer-Johansen, J. Automatic detection of chest compressions for the assessment of CPR-quality parameters. Resuscitation
**2014**, 85, 957–963. [Google Scholar] [CrossRef] - Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res.
**2014**, 15, 1929–1958. [Google Scholar] - Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv, 2014; arXiv:1412.3555. [Google Scholar]
- Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput.
**1997**, 9, 1735–1780. [Google Scholar] [CrossRef] - Gal, Y.; Ghahramani, Z. A theoretically grounded application of dropout in recurrent neural networks. Adv. Neural Inf. Process. Syst.
**2016**, 1019–1027. [Google Scholar] - Kingma, D.P.; Ba, J. Adam: A method for stochastic optimization. arXiv, 2014; arXiv:1412.6980. [Google Scholar]
- Chollet, F. Keras-Team/keras. Available online: https://github.com/fchollet/keras (accessed on 20 March 2019).
- Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G.S.; Davis, A.; Dean, J.; Devin, M.; et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. 2015. Available online: tensorflow.org (accessed on 20 March 2019).
- Gal, Y.; Ghahramani, Z. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In Proceedings of the International Conference on Machine Learning, New York, NY, USA, 19–24 June 2016; pp. 1050–1059. [Google Scholar]
- Breiman, L. Random forests. Mach. Learn.
**2001**, 45, 5–32. [Google Scholar] [CrossRef] - Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning; Springer Series in Statistics; Springer: New York, NY, USA, 2001. [Google Scholar]
- Zhu, J.; Hastie, T. Kernel logistic regression and the import vector machine. In Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada, 3–8 December 2001; pp. 1081–1088. [Google Scholar]
- Snoek, J.; Larochelle, H.; Adams, R.P. Practical bayesian optimization of machine learning algorithms. In Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, 3–6 December 2012; pp. 2951–2959. [Google Scholar]
- Bergstra, J.; Yamins, D.; Cox, D.D. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. In Proceedings of the 30th International Conference on International Conference on Machine Learning—Volume 28, Atlanta, GA, USA, 16–21 June 2013; pp. I-115–I-123. [Google Scholar]
- Bergstra, J.S.; Bardenet, R.; Bengio, Y.; Kégl, B. Algorithms for hyper-parameter optimization. In Proceedings of the Advances in Neural Information Processing Systems, Granada, Spain, 12–15 December 2011; pp. 2546–2554. [Google Scholar]
- Snyder, D.; Morgan, C. Wide variation in cardiopulmonary resuscitation interruption intervals among commercially available automated external defibrillators may affect survival despite high defibrillation efficacy. Crit. Care Med.
**2004**, 32, S421–S424. [Google Scholar] [CrossRef] - Kern, K.B.; Hilwig, R.W.; Berg, R.A.; Sanders, A.B.; Ewy, G.A. Importance of continuous chest compressions during cardiopulmonary resuscitation: Improved outcome during a simulated single lay-rescuer scenario. Circulation
**2002**, 105, 645–649. [Google Scholar] [CrossRef] - Vaillancourt, C.; Everson-Stewart, S.; Christenson, J.; Andrusiek, D.; Powell, J.; Nichol, G.; Cheskes, S.; Aufderheide, T.P.; Berg, R.; Stiell, I.G.; et al. The impact of increased chest compression fraction on return of spontaneous circulation for out-of-hospital cardiac arrest patients not in ventricular fibrillation. Resuscitation
**2011**, 82, 1501–1507. [Google Scholar] [CrossRef] - Elola, A.; Aramendi, E.; Irusta, U.; Picón, A.; Alonso, E.; Owens, P.; Idris, A. Deep Learning for Pulse Detection in Out-of-Hospital Cardiac Arrest Using the ECG. In Proceedings of the 2018 Computing in Cardiology Conference (CinC), Maastricht, The Netherlands, 23–26 September 2018. [Google Scholar]
- Elola Artano, A.; Aramendi Ecenarro, E.; Irusta Zarandona, U.; Picón Ruiz, A.; Alonso González, E. Arquitecturas de aprendizaje profundo para la detección de pulso en la parada cardiaca extrahospitalaria utilizando el ECG. In Proceedings of the Libro de Actas del XXXVI Congreso Anual de la Sociedad Española de Ingeniería Biomédica, Ciudad Real, Spain, 21–23 November 2018; pp. 375–378. [Google Scholar]
- Zhang, C.; Bengio, S.; Hardt, M.; Recht, B.; Vinyals, O. Understanding deep learning requires rethinking generalization. arXiv, 2016; arXiv:1611.03530. [Google Scholar]
- Arpit, D.; Jastrzębski, S.; Ballas, N.; Krueger, D.; Bengio, E.; Kanwal, M.S.; Maharaj, T.; Fischer, A.; Courville, A.; Bengio, Y.; et al. A closer look at memorization in deep networks. In Proceedings of the 34th International Conference on Machine Learning—Volume 70, Sydney, Australia, 6–11 August 2017; pp. 233–242. [Google Scholar]
- Hafner, D.; Tran, D.; Irpan, A.; Lillicrap, T.; Davidson, J. Reliable uncertainty estimates in deep neural networks using noise contrastive priors. arXiv, 2018; arXiv:1807.09289. [Google Scholar]
- Harang, R.; Rudd, E.M. Principled Uncertainty Estimation for Deep Neural Networks. arXiv, 2018; arXiv:1810.12278. [Google Scholar]
- Lakshminarayanan, B.; Pritzel, A.; Blundell, C. Simple and scalable predictive uncertainty estimation using deep ensembles. In Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, 4–9 December 2017; pp. 6402–6413. [Google Scholar]
- McDermott, P.L.; Wikle, C.K. Bayesian recurrent neural network models for forecasting and quantifying uncertainty in spatial-temporal data. Entropy
**2019**, 21, 184. [Google Scholar] [CrossRef] - Shadman Roodposhti, M.; Aryal, J.; Lucieer, A.; Bryan, B.A. Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest. Entropy
**2019**, 21, 78. [Google Scholar] [CrossRef]

**Figure 1.**Segments of 5 s corresponding to pulsed rhythm (PR) (

**a**) and pulseless electrical activity (PEA) (

**b**) from the study dataset.

**Figure 2.**Architectures of the proposed deep neural networks. The fully convolutional solution (${S}_{1}$), (

**a**), is fed with an electrocardiogram (ECG) segment of N samples and includes up to $\lambda $ convolutional blocks, a global maximum pooling layer (GMP), and a final fully connected layer which provides final likelihood of PR, ${p}_{PR}$. The ${S}_{2}$ solution, (

**b**), includes up to $\lambda $ convolutional blocks, a bidirectional gated recurrent unit (BGRU), an extra dropout layer, and a fully connected layer.

**Figure 3.**Results of the Bayesian optimization with tree-structured parzen estimators (BO-TPE) optimization algorithm for every hyper-parameter range in ${S}_{1}$. In the top row balanced error rate (BER) is shown for each continuous value (

**a**) or for each discrete value as median and 10–90 percentiles (

**b**–

**d**). The bottom figures show the probability of selection of the hyper-parameter values in the BO-TPE algorithm.

**Figure 4.**Results of the BO-TPE optimization algorithm for every hyper-parameter range in ${S}_{2}$. On the top BER is shown for each continuous value (

**a**) or for each discrete value as median and 10–90 percentiles (

**b**–

**e**). The bottom figures show the probability of selection of the hyper-parameter values in the BO-TPE algorithm.

**Figure 5.**Bayesian optimization with Gaussian processes (BO-GP) results for three different machine learning models. The BER is color-coded in (

**a**,

**b**) (kernel logistic regression (KLR) and support vector machine (SVM) classifiers) and each point represents the selected solution of the BO-GP in some iteration. In (

**c**) (random forest (RF) classifier), discrete values of $\psi $ are color-coded and BER plotted for a range of values for $\phi $.

**Figure 6.**Performance of RF, SVM, and KLR classifiers with hand-crafted features ($\mathit{v}$), and features extracted by the deep learning architectures ${S}_{1}$ and ${S}_{2}$ (${\mathit{v}}_{{D}_{1}}$ and ${\mathit{v}}_{{D}_{2}}$ respectively).

**Figure 7.**Performance of different models in terms of balanced accuracy (BAC) depending on the duration of the input ECG segment.

**Table 1.**Search space of Bayesian optimization (BO) for all models. Here $\mathcal{U}(\mathrm{min},\mathrm{max})$ denotes a uniform distribution between min and max values.

Model | Hyper-Parameters |
---|---|

RF | $\vartheta =\mathcal{U}(0.5,1)$ |

$\psi =\{1,\dots ,9\}$ | |

SVM | $C=\mathcal{U}$(0.001, 10,000) |

${\gamma}_{s}=\mathcal{U}$(0.001, 10,000) | |

KLR | ${\lambda}_{l}=\mathcal{U}(0.0001,0.2)$ |

${\gamma}_{s}=\mathcal{U}(0.0001,15)$ | |

${S}_{1}$ | $\lambda =\{1,2,3,4,5\}$ |

$M=\{8,16,24\}$ | |

$L=\{5,6,7,8\}$ | |

$\alpha =\mathcal{U}(0.05,0.5)$ | |

${S}_{2}$ | $\lambda =\{1,2,3,4,5\}$ |

$M=\{8,16,24\}$ | |

$L=\{5,6,7,8\}$ | |

$\alpha =\mathcal{U}(0.05,0.5)$ | |

$\vartheta =\{4,5,6,7,8\}$ |

**Table 2.**Summary of the performance of the deep learners and baseline models with the test set and the optimal hyper-parameters chosen by the Bayesian optimization with Gaussian processes (BO-GP) and Bayesian optimization with tree-structured parzen estimators (BO-TPE) algorithms with 5-s electrocardiogram (ECG) segments. DNN models outperformed baseline models in terms of BAC.

Se (%) | Sp (%) | BAC (%) | Hyper-Parameters | |
---|---|---|---|---|

Baseline models | ||||

RF | 96.0 | 87.4 | 91.7 | $\{\phi ,\psi \}=\{0.58,1\}$ |

SVM | 97.6 | 86.2 | 91.9 | $\{C,{\gamma}_{s}\}=\{2038,1246\}$ |

KLR | 97.5 | 86.2 | 91.8 | $\{{\lambda}_{l},{\gamma}_{s}\}=\{0.0013,7\}$ |

DNN models | ||||

${S}_{1}$ | 94.1 | 92.9 | 93.5 | $\{\lambda ,M,L,\alpha \}=\{4,8,7,0.2\}$ |

${S}_{2}$ | 95.5 | 91.6 | 93.5 | $\{\lambda ,M,L,\alpha ,\vartheta \}=\{2,24,6,0.4,6\}$ |

**Table 3.**Computation time to classify a 5-s segment for the baseline and deep neural network (DNN) models. The fastest model was ${S}_{1}$.

${\mathit{t}}_{1}$ (ms) | ${\mathit{t}}_{2}$ (ms) | Total (ms) | |
---|---|---|---|

Baseline models | |||

RF | 63.5 | 0.28 | 63.8 |

SVM | 63.5 | 0.35 | 63.9 |

KLR | 63.5 | 0.25 | 63.8 |

DNN models | |||

${S}_{1}$ | - | - | 1.6 |

${S}_{2}$ | - | - | 101.1 |

**Table 4.**Performance of ${S}_{1}$ with different degrees of uncertainty. Scores are given for the test set and the percentage of feedback in the test set are reported. The threshold for feedback was set in the training set.

Training Percentage | Testing Percentage | Se (%) | Sp (%) | BAC (%) |
---|---|---|---|---|

80 | 78.5 | 100 | 95.2 | 97.6 |

90 | 89.6 | 96.6 | 93.2 | 94.9 |

95 | 95.4 | 97.1 | 92.2 | 94.6 |

97.5 | 98.1 | 96.3 | 92.1 | 94.2 |

100 | 100 | 94.1 | 92.9 | 93.5 |

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## Share and Cite

**MDPI and ACS Style**

Elola, A.; Aramendi, E.; Irusta, U.; Picón, A.; Alonso, E.; Owens, P.; Idris, A.
Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest. *Entropy* **2019**, *21*, 305.
https://doi.org/10.3390/e21030305

**AMA Style**

Elola A, Aramendi E, Irusta U, Picón A, Alonso E, Owens P, Idris A.
Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest. *Entropy*. 2019; 21(3):305.
https://doi.org/10.3390/e21030305

**Chicago/Turabian Style**

Elola, Andoni, Elisabete Aramendi, Unai Irusta, Artzai Picón, Erik Alonso, Pamela Owens, and Ahamed Idris.
2019. "Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest" *Entropy* 21, no. 3: 305.
https://doi.org/10.3390/e21030305