Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation
<p>Rhythm annotation scheme during CPR: AED analysis periods (10 s of clean-ECG) were used for rhythm annotation. The rhythm during the preceding period with chest compressions (10 s of CC-ECG) was considered consistent.</p> "> Figure 2
<p>CNN architecture and regularization of the depth by random search selection of the hidden layer output of an arbitrary convolutional block N = {2, 3…7} for classification. The width of the red horizontal bars is scaled to represent the sizes of the input and hidden layers, illustrating the true model shrinkage from top to bottom due to MP = 2 after each convolutional block.</p> "> Figure 3
<p>Random search choice of the hyperparameters <math display="inline"><semantics> <mrow> <mi>HP</mi> <mo>=</mo> <msubsup> <mrow> <mrow> <mo>{</mo> <mrow> <mi mathvariant="normal">N</mi> <mo>,</mo> <msub> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">i</mi> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">K</mi> <mi mathvariant="normal">i</mi> </msub> </mrow> <mo>}</mo> </mrow> </mrow> <mrow> <mi mathvariant="normal">i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi mathvariant="normal">N</mi> </msubsup> <mo> </mo> </mrow> </semantics></math>: grid of values (<b>left</b>) and allowed trends of change from first to last CNN layer (<b>right</b>). The lines on the right correspond to the seven possible scenarios for driving F<sub>N</sub> and K<sub>N</sub> values over layers, i.e., constant (green), continuous increasing (red), constant+continuous increasing (red), continuous+constant increasing (red), continuous decreasing (blue), constant+continuous decreasing (blue), continuous+constant decreasing (blue).</p> "> Figure 4
<p>Validation BAC performance of all random search CNN models in function of the number of trainable parameters. The scatter plot is depicted in six groups for the number of convolutional blocks N = {2, 3, 4, 5, 6, 7}. The highlighted scatter point is corresponding to the best performance model CNN3-CC-ECG.</p> "> Figure 5
<p>Categorical analysis of random search models: (<b>a</b>) Number of trainable parameters; (<b>b</b>) Validation BAC performance, according to the number of convolutional blocks N = {2, 3, 4, 5, 6, 7}. The statistically different distributions are highlighted at <span class="html-italic">p</span> < 0.05 (*).</p> "> Figure 6
<p>Training process of CNN3-CC-ECG network run over 400 epochs, showing the accuracy (<b>a</b>) and loss (<b>b</b>) curves for the training and validation dataset. The best model (validation accuracy→max) was achieved in 213 epochs. Further training epochs were discarded as being ineffective (gray area).</p> "> Figure 7
<p>ROC curves of CNN3-CC-ECG network for the validation and test databases. The threshold of the ROC operating point for the validation database (black ‘o’ mark) was computed at BAC→max. The same threshold was applied to report the final performance for the test database (red ‘o’ mark).</p> "> Figure 8
<p>Features of CNN3-CC-ECG network computed for two examples of 10 s ECG-CC strips annotated as VF (<a href="#sensors-21-04105-f001" class="html-fig">Figure 1</a>) with different shock advisory decisions: (<b>a</b>) VF (SNR = −6.5 dB) correctly detected as Sh rhythm with output probability pSh = 0.97; (<b>b</b>) VF (SNR = −12.9 dB) erroneously detected as NSh rhythm with output probability pSh = 0.007. The underlined 50 filters at the output of CONV1D (N = 3) are used for computation of 50 classifier features (bottom plot), grouped for Sh (red) and NSh (blue) rhythms in accordance with the sign of the classifier weights.</p> "> Figure 9
<p>Features of CNN3-CC-ECG network computed for two examples of 10 s ECG-CC strips annotated as OR (<a href="#sensors-21-04105-f001" class="html-fig">Figure 1</a>) with different shock advisory decisions: (<b>a</b>) OR (SNR = −14.6 dB) correctly detected as NSh rhythm with output probability pSh = 0.001; (<b>b</b>) OR (SNR = −18.4 dB) erroneously detected as Sh rhythm with output probability pSh = 0.858. The underlined 50 filters at the output of CONV1D (N = 3) are used for computation of 50 classifier features (bottom plot), grouped for Sh (red) and NSh (blue) rhythms in accordance with the sign of the classifier weights.</p> "> Figure 10
<p>Features of CNN3-CC-ECG network computed for two examples of 10 s ECG-CC strips annotated as Asystole (<a href="#sensors-21-04105-f001" class="html-fig">Figure 1</a>) with different shock advisory decisions: (<b>a</b>) Asystole (SNR = −18 dB) correctly detected as NSh rhythm with output probability pSh = 0.006; (<b>b</b>) Asystole (SNR = −24 dB) erroneously detected as Sh rhythm with output probability pSh = 0.81. The underlined 50 filters at the output of CONV1D (N = 3) are used for computation of 50 classifier features (bottom plot), grouped for Sh (red) and NSh (blue) rhythms in accordance to the sign of the classifier weights.</p> "> Figure 11
<p>2D t-SNE distribution of GMP features in CNN3-CC-ECG network, estimated for the test database for different rhythms (OR, Asystole, VF). The color gradients represent the distribution of the features with respect to SNR.</p> "> Figure 12
<p>Dependency of CNN3-CC-ECG network performance on the CPR artifact corruption level in ECG estimated on the test database. The mean value and 95% confidence interval of Se (VF) and Sp (OR, Asystole) are presented in function of four SNR levels. Significant drop of performance is highlighted for Se (VF) and Sp (OR) at SNR < −9 dB (* <span class="html-italic">p</span> < 0.05).</p> "> Figure 13
<p>Dependency of CNN3-CC-ECG network performance on the CC rate in the test database. The mean value and 95% confidence interval of Se (VF) and Sp (OR, Asystole) are presented in function of four CC rate ranges: slow (<100 min<sup>−1</sup>), normal (100–110 min<sup>−1</sup>, 110–120 min<sup>−1</sup>) and rapid compressions (>120 min<sup>−1</sup>). Significant drop of performance is highlighted for Sp (OR) at CC rate >120 min<sup>−1</sup> (* <span class="html-italic">p</span> < 0.05).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. ECG Databases
- Coarse ventricular fibrillation (VF) with peak-to-peak ECG amplitude >200 μV;
- Organized rhythm (OR), including normal sinus rhythm, atrial flutter/fibrillation, premature atrial and ventricular contractions, heart blocks, supraventricular tachycardia, sinus bradycardia and idioventricular rhythms;
- Asystole with peak-to-peak ECG amplitude <100 μV.
- 596 interventions in 2011 [39] used for validation;
- 1545 interventions in 2017 used for the test. These interventions respected a strict inclusion criterion of CC-ECG strips with CC duration ≥10 s before the annotation window. This inclusion criterion was applied in order to guarantee fair report of the test performance of a DNN algorithm that should be run in presence of CC. The presented test database was novel and not used in any previous study.
2.2. CNN Architecture
- CONV1D: The 1D Convolution layer of the ith convolutional block contained Fi filters with kernel size (Ki). The output of the fth filter (f = 1,2,…Fi) was computed as:
- -
- i = [1, 2,…N] identified the sequential number of the convolutional layer;
- -
- Si was the input vector of the ith convolutional layer, with size (Li);
- -
- j = [0, 1, … Li − Ki + 1] indexed the output feature vector, applying convolutional operation with a valid padding [53];
- -
- , denoted the weights and biases of the ith convolution kernel, respectively;
- -
- Ψ was the applied rectified linear unit activation function ReLU.
- MAXPOOL (pool size = MP): Down-sampled the CONV1D layer output () with size (Li − Ki + 1) × Fi by applying maximum operation over non-overlapping segments of the feature vector , thus generating a new feature vector with MP times smaller width ( ).
- DROPOUT: This regularization layer with a dropout rate α ∈ [0; 1] was applied to avoid overfitting and improved the generalization during training. It generated an output vector ( ) with portion of ‘0’ nodes equal to α. In the test process , the input signal for the next convolutional layer was Si+1 = .
2.3. CNN Optimization
- N = {2, 3, 4, 5, 6, 7};
- Fi = {5, 10, 15, 20, 25, 30, 40, 50};
- Ki = {5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 85, 100};
- The vectors {Fi} and {Ki} were designed to follow a decreasing, increasing or constant trend from top to bottom (i = 1, 2, …N) in the same model (Figure 3).
- MP = 2 in MAXPOOL layer. This is the minimal value that allowed conditions to build deeper networks by gradual subsampling by two of the feature space after each convolutional block N;
2.4. CNN Training
3. Results
3.1. HP Optimization
3.2. Optimal CNN Model
3.3. CNN Features
3.4. SNR Impact
3.5. Impact of the Chest Compression Rate
4. Discussion
- Significant inferiority of all deepest models with 6 and 7 convolutional layers, suggesting that maximal shrinkage of the feature space has deteriorating impact on performance (Figure 5b).
5. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Training Database | Validation Database | Test Database | |
---|---|---|---|
Shockable rhythms | |||
VF | 408 | 151 | 301 |
Non-shockable rhythms | |||
OR | 1089 | 706 | 1640 |
Asystole | 1504 | 1671 | 3650 |
−9 dB ≤ SNR | −9 dB < SNR ≤ −6 dB | −6 dB < SNR ≤ −3 dB | SNR > −3 dB | |
---|---|---|---|---|
Shockable rhythms | ||||
VF | 97 | 53 | 71 | 80 |
Non-shockable rhythms | ||||
OR | 456 | 226 | 335 | 623 |
Asystole | 3266 | 149 | 122 | 113 |
Number of Convolutional Blocks (N) | Number of Trained Models | Training Epochs Median (Quartile Range) |
---|---|---|
2 | 259 | 204 (131–288) |
3 | 231 | 216 (137–291) |
4 | 243 | 186 (113–266) |
5 | 253 | 148 (83–243) |
6 | 240 | 131 (86–227) |
7 | 274 | 104 (58–210) |
Layer Type | Description | Params | Output Shape | |
---|---|---|---|---|
Input | - | - | (1250, 1) | |
Convolutional blocks | N = 1 | K1 = 5, F1 = 5, ReLU MP = 2, α = 0.3 | 55 | (620, 5) |
N = 2 | K2 = 20, F2 = 25, ReLU MP = 2, α = 0.3 | 2525 | (300, 25) | |
N = 3 | K3 = 20, F3 = 50, ReLU MP = 2, α = 0.3 | 25,050 | (140, 50) | |
GMP | - | - | (50) | |
DENSE | Units = 1, Sigmoid | 51 | (1) |
Performance | Training Database | Validation Database | Test Database |
---|---|---|---|
Se (Shockable rhythms: VF) | 100% (408/408) | 89.4% (135/151) | 89.0% (268/301) |
Sp (Non-shockable rhythms) | 97.1% (2518/2593) | 93.8% (2230/2377) | 91.3% (4830/5290) |
Sp (OR) | 97.0% (1056/1089) | 94.9% (670/706) | 91.7% (1504/1640) |
Sp (Asystole) | 97.1% (1460/1504) | 93.4% (1561/1671) | 91.1% (3325/3650) |
BAC | 98.6% | 91.6% | 90.2% |
ROC-AUC | 0.999 | 0.945 | 0.938 |
Study | Method | Test Data | Se % | Sp % | BAC % |
---|---|---|---|---|---|
de Gauna et al., 2008 [29] | CPR suppression via Kalman filter with reference channel based on ECG Subsequent ECG analysis via standard AED shock advisory algorithm Input information: filtered CC-ECG | Analysis duration: 9.6–14.4 s Database: CC-ECG from real OHCA Test dataset: Independent - 131 Sh samples - 197 OR, 150 Asystole samples | 90.1 | 80.4 | 85.3 |
Li et al., 2008 [40] | Wavelet transform and cross-correlation for ECG and CC morphology estimation. Evaluation of pattern differences. Input information: raw CC-ECG | Analysis duration: 10 s Database: CC-ECG from real OHCA Test dataset: Independent - 1256 Sh samples - 923 OR, 41 Asystole samples | 93.3 | 88.6 | 91.0 |
Krasteva et al., 2010 [38] | Time–frequency techniques for ECG and CC morphology estimation. ECG signal reconstruction by subtraction of CC patterns. Input information: raw CC-ECG | Analysis duration: 10 s Database: CC-ECG from real OHCA Test dataset: Independent - 172 Sh samples - 371 OR, 330 Asystole samples | 90.1 | 86.1 | 88.1 |
Issasi et al., 2020 [47] | CPR suppression via Recursive Least Squares filter with reference from a sternal CPR assist pad with an accelerometer. Subsequent ECG analysis via CNN classifier with three convolutional blocks and two fully connected layers. Input information: filtered CC-ECG | Analysis duration: 9 s Database: CC-ECG from real OHCA Test dataset: Not independent 5-fold cross-validation - 586 Sh samples - 1541 OR, 1192 Asystole samples | 95.8 | 96.1 | 96.0 |
Issasi et al., 2020 [48] | CPR suppression via Recursive Least Squares filter with reference from a Load Distributing Band mechanical chest compression device. Subsequent ECG analysis via CNN classifier with three convolutional blocks and two fully connected layers Input information: filtered CC-ECG | Analysis duration: 8 s Database: CC-ECG from real OHCA Test dataset: Not independent Database split at 80/20% for training/validation - 780 Sh samples - 2644 OR + Asystole samples Median performance of 100 random repetitions reported | 92.2 | 96.6 | 94.4 |
Hajeb-M et al., 2021 [49] | Hybrid DNN architecture: convolutional layers, residual blocks and bidirectional LSTM layers Input information: time and frequency domain ECG representations | Analysis duration: 8 s Database: Artificially mixed CC artifacts (OHCA Asystole) and clean-ECG (Holter) with fixed SNR = −3 dB. Test dataset: Not independent 4-fold cross-validation: - 3216 Sh rhythms - 6768 OR, missing Asystoles | 94.2 | 86.1 | 90.1 |
This study | Fully convolutional DNN with three convolutional blocks, GMP and DENSE layer Input information: raw CC-ECG | Analysis duration: 10 s Database: CC-ECG from real OHCA Test dataset: Independent - 301 VF samples - 1640 OR, 3650 Asystole samples | 89.0 | 91.3 | 90.2 |
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Jekova, I.; Krasteva, V. Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation. Sensors 2021, 21, 4105. https://doi.org/10.3390/s21124105
Jekova I, Krasteva V. Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation. Sensors. 2021; 21(12):4105. https://doi.org/10.3390/s21124105
Chicago/Turabian StyleJekova, Irena, and Vessela Krasteva. 2021. "Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation" Sensors 21, no. 12: 4105. https://doi.org/10.3390/s21124105
APA StyleJekova, I., & Krasteva, V. (2021). Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation. Sensors, 21(12), 4105. https://doi.org/10.3390/s21124105