Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms
<p>Examples of 5 s electrocardiogram (ECG) strips, extracted according to the defined annotation scheme for shockable (ventricular fibrillation—VF, rapid ventricular tachycardia—VT) and nonshockable (normal sinus rhythms—NSR, other nonshockable rhythms—ONR, asystole—ASYS) rhythms, found in Holter (left panel) and out-of-hospital cardiac arrests (OHCA) (right panel) databases.</p> "> Figure 2
<p>Еnd-to-end architecture of the proposed convolutional neural networks (CNN) model, showing input layer of raw ECG signal (one channel × length L<sub>1</sub>) followed by N consecutive blocks with a common fully-convolutional three-layer structure (1D convolution—Conv1D; max-pooling; dropout). The final diagnostic probability for Sh/NSh rhythm detection <span class="html-italic">p</span> ∈ [0: Sh, 1: NSh] is derived after global max pooling (GMP) and a dense layer binary classifier.</p> "> Figure 3
<p>Process of hyperparameter (HP) search, analysis and optimization for justification of the best deep neural network (DNN) model.</p> "> Figure 4
<p>Analysis of validation balanced accuracy (BAC) performance for all CNN models trained with random search: (<b>a</b>) scatterplot of BAC in function of the number of trainable parameters; (<b>b</b>) box plots of BAC categorized to the network depth <span class="html-italic">N</span> = {1, 2, 3, 4, 5, 6, 7}; (<b>c</b>) BAC histograms categorized to N and highlighting the selected models with top-ranked performance (red arrow).</p> "> Figure 5
<p>Analysis of <math display="inline"><semantics> <mrow> <mi>H</mi> <mi>P</mi> <mi>s</mi> <mo>=</mo> <mrow> <mo>{</mo> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>F</mi> <mn>1</mn> </msub> <mo>,</mo> </mrow> <mo>}</mo> </mrow> </mrow> </semantics></math> of random search CNNs with one convolutional block (<span class="html-italic">N</span> = 1):(<b>a</b>) Colormap of validation performance: <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>A</mi> <mi>C</mi> <mo>=</mo> <mi>f</mi> <mrow> <mo>{</mo> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>F</mi> <mn>1</mn> </msub> <mo>,</mo> </mrow> <mo>}</mo> </mrow> </mrow> </semantics></math> generated in a fine surface grid by four-nearest-neighbors interpolation between the measurement points of the search grid (blue dots). The highlighted white zone covers the HPs of the top-ranked performance models, i.e., <span class="html-italic">HPrank</span> quartile range (rectangle) and <span class="html-italic">HPopt = HPrank</span> median value (square); (<b>b</b>) statistical distributions of <math display="inline"><semantics> <mrow> <mrow> <mo>{</mo> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>F</mi> <mn>1</mn> </msub> <mo>,</mo> </mrow> <mo>}</mo> </mrow> <mo>=</mo> <mi>f</mi> <mrow> <mo>(</mo> <mrow> <mi>B</mi> <mi>A</mi> <mi>C</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, presented as median values (dots) and quartile ranges (whiskers). <span class="html-italic">HPrank</span> quartile range of the top ranked performance models is highlighted in the rightmost distributions, corresponding to BAC ≥ 96.5%.</p> "> Figure 6
<p>Distributions of <math display="inline"><semantics> <mrow> <mi>H</mi> <mi>P</mi> <mi>s</mi> <mo>=</mo> <msubsup> <mrow> <mrow> <mo>{</mo> <mrow> <msub> <mi>K</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>F</mi> <mi>i</mi> </msub> <mo>,</mo> </mrow> <mo>}</mo> </mrow> </mrow> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </msubsup> </mrow> </semantics></math> of random search CNNs with more than one convolutional block (<span class="html-italic">N</span> = 2, 3… 7), corresponding to <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mi>i</mi> </msub> <mo>=</mo> <mi>f</mi> <mrow> <mo>(</mo> <mrow> <mi>B</mi> <mi>A</mi> <mi>C</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> (top plots) and <math display="inline"><semantics> <mrow> <msub> <mi>F</mi> <mi>i</mi> </msub> <mo>=</mo> <mi>f</mi> <mrow> <mo>(</mo> <mrow> <mi>B</mi> <mi>A</mi> <mi>C</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> (bottom plots) as median values (dots) and quartile ranges (whiskers). <span class="html-italic">HPrank</span> quartile ranges of the top-ranked performance models are highlighted in the rightmost distributions.</p> "> Figure 7
<p>Number of trainable parameters in function of the validation BAC performance. The distributions are presented as median values (dots) and quartile ranges (whiskers). The number of parameters of the top-ranked performance models is highlighted in the rightmost distributions.</p> "> Figure 8
<p>Validation BAC performance of <span class="html-italic">HPopt</span> models with different depths (<span class="html-italic">N</span> = 1, 2, … 7), trained with different learning rates. The distributions are presented as median values (dots), quartile ranges (boxes) and min–max range (whiskers). The red arrow highlights the best model, i.e., <span class="html-italic">N</span> = 5 (<span class="html-italic">LR</span> = 0.001), having BAC→max.</p> "> Figure 9
<p>Validation receiver operating characteristic curves (ROC) of <span class="html-italic">HPopt</span> models with different depths (N = 1, 2 … 7). The dot marks correspond to the ROC point with maximal BAC (Se + Sp→max). The red ROC (<span class="html-italic">N</span> = 5) corresponds to the selected <span class="html-italic">HPbest</span>.</p> "> Figure 10
<p>Validation sensitivity (Se), specificity (Sp), and BAC of our best CNN model (<span class="html-italic">HPbest</span>) for different analysis durations of the input ECG signal.</p> "> Figure 11
<p>Comparative study of our best model to published fully convolutional DNNs, which are trained and evaluated under the same conditions on public Holter and OHCA databases. BAC performance is reported on our validation dataset using analysis durations between 2 s and 10 s. The performance of the reference automatic external defibrillator (AED) algorithm is reported for the same databases, taken from Krasteva et al. [<a href="#B19-sensors-20-02875" class="html-bibr">19</a>] for VFDB, Didon et al. [<a href="#B7-sensors-20-02875" class="html-bibr">7</a>] (3, 5, 7 s) and Krasteva et al. [<a href="#B25-sensors-20-02875" class="html-bibr">25</a>] (10 s) for the OHCA database.</p> ">
Abstract
:1. Introduction
2. ECG Databases
2.1. Public Holter Databases
- AHA fibrillation database (AHADB) [61], including 30 min ECG recordings from 10 patients (files A8001 to A8010); only the first out of the two available ECG channels is used;
- Massachusetts Institute of Technology – Beth Israel Hospital (MIT-BIH) malignant ventricular ectopy database (VFDB) [62,63,64], including 35 min ECG recordings from 22 patients (files 418 to 430; 602, 605, 607, 609, 610, 611, 612, 614, and 615); only the first out of the two available ECG channels is used;
2.2. OHCA Databases
- November 2010–December 2010 (OHCA1 from 226 patients);
- June 2011–September 2011 (OHCA2 from 733 patients).
2.3. Rhythm Annotation
- Shockable rhythms, including:
- ○
- Coarse ventricular fibrillation (VF) with amplitude >200 µV;
- ○
- Rapid ventricular tachycardia (VT) with rate >150 bpm;
- Nonshockable rhythms, including:
- ○
- Normal sinus rhythm (NSR) with visible P-QRS-T waves,
- ○
- Other nonshockable rhythms (ONR), such as supraventricular tachycardia, sinus bradycardia, atrial fibrillation and flutter, heart block, idioventricular rhythms, and premature ventricular contractions;
- ○
- Asystole (ASYS), representing ECG signal with peak to peak amplitude <100 µV, lasting more than 4 s;
- Intermediate rhythms, consisting of fine ventricular fibrillations with amplitude in the range 100–200 µV (i.e., between ASYS and VF), and slow ventricular tachycardia with rate <150 bpm. AHA does not set any performance goal for such rhythms [4];
- Inconsistent rhythm (i.e., transition from NSh to Sh);
- Strips that contain extreme artifacts, significant baseline wander, electromyogram noise, pacemaker impulses.
2.4. Training/Validation Subsets
- A training dataset, including all Sh and NSh strips from AHA, CUDB and OHCA1 databases;
- A validation dataset, including all Sh and NSh strips from VFDB and OHCA2 databases.
3. Methods
3.1. DNN Architecture
- -
- i = [1, 2, … N] identifies the sequential number of the convolutional layer.
- -
- Si is the input vector of the ith convolutional layer, with size (1 × Li).
- -
- j = [0, 1, … Li – Ki + 1] indexes the output feature vector, applying convolutional operation with a valid padding [37].
- -
- are the weights and are the biases of the convolution kernel;
- -
- A is the applied nonlinear activation function ReLU (rectified linear unit);
3.2. Hyperparameters Optimization
- Number of sequential CNN blocks (N), which virtually represents the depth of the network;
- Number of filters (Fi) and kernel size (Ki) of Conv1D in each sequential block (i = 1, 2, … N) that majorly influence the feature map representations of the ECG signal;
- Max-pooling size: A minimal fixed setting MP = 2 is used to gradually subsample the feature space at each sequential CNN block N, thus providing conditions to build deeper networks;
3.2.1. Random HP Search
- N = {1, 2, 3, 4, 5, 6, 7};
- Fi = {5, 10, 15, 20, 25, 30, 40, 50}; additional range F1 = {75, 100, 125, 150, 200} is included in the search space only for the shallowest CNN (N = 1), aiming to increase the number of trainable parameters to levels comparable to deeper CNNs (N > 1);
- Ki = {5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 85, 100}; owing to the same reason as above, extra-large kernel sizes K1= {125, 150, 200} are included in the search space of CNN (N = 1);
- The vectors {Fi} and {Ki} are designed to follow a decreasing, increasing or constant trend from top to bottom layers (i = 1, 2, … N) in the same model.
3.2.2. HPs Analysis
3.2.3. Optimal HP Models
3.2.4. Best Model
3.3. Training of DNN Models
- Keep balanced training dataset by replicating the shockable cases four times, considering the ratio of total NSh/Sh cases in Table 1, i.e., after replication the number of Sh cases (4 × 720 = 2880) becomes roughly equal to the number of NSh cases (3170);
- Shuffle the training data for randomization before feeding it into batches;
- Split the training data into batches, because using small batch sizes achieves the best training stability and generalization performance;
- Normalization of the input data is not applied and the input signal resolution of 2.5 μV/LSB is maintained. We purposely keep the real ECG amplitude, since it is characteristic for some of the analyzed rhythms (e.g., ASYS peak-to-peak amplitude <100 µV).
- Training epochs: 400. Early stopping is applied if no improvement in performance is observed for more than 150 epochs;
- Batch size: 256;
- Kernel initializer: random uniform;
- Optimizer: ‘Adam’ with learning rate LR = 0.001, chosen as a good default setting [37], decay rate DR = LR/epochs, exponential decay rate for the first moment estimates β1 = 0.9 and exponential decay rate for the second moment estimates β2 = 0.999;
- Loss function: binary cross-entropy for 2 target classes (Sh/NSh);
- Metrics function: accuracy = (TP + TN)/(TP + TN + FP + FN). Owing to the concept for balanced training dataset during model fit, the metrics accuracy closely corresponds to BAC.
- Saved model: the model with maximal accuracy after all training epochs.
4. Results
4.1. Random HPs Search
- (N = 1): 195 models, which cover the full search grid (13 filters × 15 kernel sizes). They are trained for 202 (120–298) epochs, reported as median value (quartile range). All models converged within 400 epochs.
- (N = 2): 1305 models trained for 140 (74–226) epochs. The relatively smaller number of trainable parameters than deeper networks resulted in a larger number of trained models generated during the training session;
- (N = 3): 707 models trained for 116 (57–204) epochs;
- (N = 4): 715 models trained for 69 (35–171) epochs;
- (N = 5): 716 models trained for 55 (25–143) epochs;
- (N = 6): 275 models trained for 44 (19–88) epochs;
- (N = 7): 303 models trained for 51 (20–62) epochs. Note the about 2.5-times smaller number of very deep models (N = 6, 7) than (N = 3, 4, 5), which is a consequence of the limited optional values for setting Ki in deeper CNN layers owing to the effect of reaching maximal model shrink with valid padding. In some iterations, the random search algorithm has spent abundant amount of time for finding a valid HPs setting.
- BAC ≥ 96.5% applied for N = 1 (selecting six models);
- BAC ≥ 98.9% for N = 2 (five models);
- BAC ≥ 99.1% for N = 3 (26 models);
- BAC ≥ 99.3% for N = 4 (seven models), N = 5 (seven models), N = 6 (seven models), N = 7 (14 models).
4.2. HP Statistical Analysis
4.3. Rank of HPs Importance
4.4. Optimal HP Models
4.5. Best Model
5. Discussion
5.1. HPs Optimization
- -
- Shallow and very deep DNNs (from five to 23 hidden layers), composed by three to 21 layers from one to seven CNN blocks × three layers (Conv1D, max-pooling, dropout) + one GMP + one dense layer for binary classification;
- -
- Different number of filters in each Conv1D layer: , i =1… N;
- -
- Different kernel sizes in each Conv1D layer: , i = 1 … N, valid for fs = 125 Hz.
5.2. Analysis of Our Best CNN Model
- The use of two ECG sources from the most famous public ventricular arrhythmia databases and private OHCA databases provides а robust setting for model optimization on a large scale of ECG rhythms that can be seen by Holters and defibrillators during treatment of cardiac arrest patients. This article uses the largest number of (Sh + NSh) samples for training (720 + 3170) and validation (739 + 5921), which is the important precondition for design of robust deep learning shock advisory systems;
- The application of the model on ECG signals with short and long durations (2–10 s), with maximal performance for 5 s analysis (BAC = 99.5%, Se = 99.6%, Sp = 99.4%, Table 4) and tolerable drop in performance (<2% points) for very short 2 s analysis (BAC = 98.2%, Se = 97.6%, Sp = 98.7%, Table 4) can satisfy the crucial AED requirements for providing shock advisory decision with minimal hands-off delay after end of chest compressions.
5.3. Comparative Study to Other Published CNN Models for ECG Classification
- -
- Our best model outperforms all other models for both Public and OHCA databases. Its configuration can be distinguished as the deepest among others with five convolutional layers, while the number of filters and kernel sizes looks balanced within the middle range found in other studies. This result is a certain proof that HP optimization has an important role in accuracy and should always be carefully performed during DNN design for specific applications;
- -
- The models of Elola et al. [56] and Kiranyaz et al. [44] are the next best models with up to about −1% points and −1.5% points’ drop in BAC, respectively. These models are good examples for successful transfer learning of CNNs in ECG signal processing, where CNN designs optimized for detection of pulseless rhythm and heartbeat classification are here successfully relearned for detection of shockable rhythms. The model of Elola et al. [56] can be distinguished as a deep model (four convolutional layers) with a small number of trainable parameters (1441, owing to the small number of filters and kernels), while the model of Kiranyaz et al. [44] can be distinguished as the shallowest model (two convolutional layers), but with the largest number of trainable parameters (8389 resulting from the largest number of filters and kernel sizes). These models are good examples to show that both deep and shallow networks can almost perform equally if their HPs are optimized in a specific ECG diagnostic application;
- -
- The models of Picon et al. [60], Zubair et al. [48] and Acharya et al. [53] present the largest BAC drop (from −1% points to −5% points). The common HP setting observed in these models is the very small kernel size (three to five), which has been proven in our optimization study to have the most important impact to BAC (see Table 2);
- -
- We note that the additional LSTM layer in the model of Picon et al. [60] provides evidence for inferiority, observing the considerable BAC drop (−2.5% points to −5% points) for short-duration signals < 5 s in Holter databases. This demonstrates that fully convolutional networks are indeed enough powerful to extract features for superior Sh/NSh detection performance at minimal computational cost than other DNN architectures.
- -
- Our best model outperforms a reference shock-advisory system of a commercial AED (Fred Easy, Schiller Médical, France) based on hand-crafted ECG morphology features and a decision tree classifier [7,19,25] by about (+0.5% points to +3% points) for analysis durations of 10 s and 2 s, respectively. Indeed, the AED shock advisory system does not show inferior performance to three DNNs [48,58,60], which is a clear indication that unoptimized deep networks have no benefit compared to traditional machine learning algorithms.
6. Conclusions
7. Limitations
Author Contributions
Funding
Conflicts of Interest
References
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Training Dataset | Validation Dataset | ||||||
---|---|---|---|---|---|---|---|
Rhythm | AHADB | CUDB | OHCA1 | Total | VFDB | OHCA2 | Total |
VF | 430 | 93 | 66 | 589 | 308 | 221 | 529 |
VT | 20 | 93 | 18 | 131 | 202 | 8 | 210 |
NSR | 499 | 304 | 42 | 845 | 1023 | 154 | 1177 |
ONR | 550 | 776 | 334 | 1660 | 1425 | 1063 | 2488 |
ASYS | 6 | 4 | 655 | 665 | 4 | 2252 | 2256 |
All Sh | 450 | 186 | 84 | 720 | 510 | 229 | 739 |
All NSh | 1055 | 1084 | 1031 | 3170 | 2452 | 3469 | 5921 |
N | F1 | F2 | F3 | F4 | F5 | F6 | F7 | K1 | K2 | K3 | K4 | K5 | K6 | K7 | Param |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0.15 | 1.00 | 0.56 | ||||||||||||
2 | 0.36 | 0.35 | 0.80 | 1.00 | 1.00 | ||||||||||
3 | 0.28 | 0.29 | 0.05 | 1.00 | 0.64 | 0.44 | 0.58 | ||||||||
4 | 0.32 | 0.20 | 0.21 | 0.16 | 1.00 | 0.62 | 0.39 | 0.33 | 0.56 | ||||||
5 | 0.37 | 0.27 | 0.17 | 0.18 | 0.23 | 1.00 | 0.73 | 0.30 | 0.06 | 0.19 | 0.61 | ||||
6 | 0.27 | 0.22 | 0.18 | 0.17 | 0.17 | 0.24 | 1.00 | 0.72 | 0.40 | 0.18 | 0.46 | 0.55 | 0.63 | ||
7 | 0.18 | 0.68 | 0.75 | 0.68 | 0.41 | 0.18 | 0.09 | 0.73 | 0.27 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
N | F1 | F2 | F3 | F4 | F5 | F6 | F7 | K1 | K2 | K3 | K4 | K5 | K6 | K7 | Param | LR | Max BAC |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 113 | 50 | 5877 | 0.001 | 96.70% | ||||||||||||
2 | 10 | 15 | 60 | 25 | 4391 | 0.001 | 98.99% | ||||||||||
3 | 5 | 10 | 20 | 10 | 30 | 50 | 11606 | 0.001 | 99.31% | ||||||||
4 | 15 | 15 | 15 | 10 | 20 | 25 | 30 | 40 | 18741 | 0.001 | 99.31% | ||||||
5 * | 20 | 15 | 15 | 10 | 5 | 10 | 10 | 10 | 10 | 10 | 7521 | 0.001 | 99.50% | ||||
6 | 30 | 30 | 15 | 15 | 10 | 5 | 15 | 10 | 10 | 10 | 10 | 10 | 18311 | 0.0005 | 99.38% | ||
7 | 40 | 30 | 25 | 15 | 10 | 5 | 5 | 15 | 5 | 5 | 5 | 5 | 5 | 5 | 13486 | 0.0005 | 99.45% |
Se/Sp (rhythm) | Analysis Duration | |||||
---|---|---|---|---|---|---|
2 s | 3 s | 4 s | 5 s | 7 s | 10 s | |
Validation dataset: Total | ||||||
Se (all Sh), % | 97.6 | 98.7 | 98.9 | 99.6 | 99.5 | 99.3 |
Sp (all NSh), % | 98.7 | 99.1 | 99.3 | 99.4 | 99.5 | 99.7 |
BAC, % | 98.2 | 98.9 | 99.1 | 99.5 | 99.5 | 99.5 |
Validation dataset: Holter (VFDB) | ||||||
Se (all Sh), % | 98.6 | 99.4 | 99.8 | 100 | 99.8 | 100 |
Sp (all NSh), % | 98.6 | 99.5 | 99.4 | 99.8 | 99.5 | 99.4 |
BAC, % | 98.6 | 99.5 | 99.6 | 99.9 | 99.7 | 99.7 |
Validation dataset: OHCA2 | ||||||
Se (all Sh), % | 95.2 | 96.9 | 96.9 | 98.7 | 98.7 | 97.8 |
Sp (all NSh), % | 98.7 | 98.8 | 99.2 | 99.2 | 99.6 | 99.1 |
BAC, % | 97.0 | 97.9 | 98.1 | 99.0 | 99.2 | 99.2 |
CNN Layers | LSTM Layer | Dense Layers | ||||||
---|---|---|---|---|---|---|---|---|
Methods | Original Application | N | Filters | Kernel Size | Max-Pool | Kernel Size | N (Kernel Size) | Trainable Params |
This study | Sh/NSh detection | 5 | 20, 15, 15, 10, 5 | 10, 10, 10, 10, 10 | 2 | - | 1 (2) | 7521 |
Picon et al. [60] | Sh/NSh detection | 2 | 32, 32 | 3, 3 | 7 | 20 | 1 (2) | 7493 |
Acharya et al. [53] | Sh/NSh detection | 4 | 3, 5, 10, 10 | 5, 5, 5, 4 | 2 | - | 3 (10, 5, 2) | 939 |
Elola et al. [56] 1 | Pulseless rhythm detection | 4 | 8, 8, 8, 8 | 7, 7, 7, 7 | 2 | - | 1 (2) | 1441 |
Kiranyaz et al. [44] 1,2 | Heartbeat classification | 2 | 32, 16 | 15, 15 | 6 | - | 2 (10, 2) | 8389 |
Zubair et al. [48] 1,2 | Heartbeat classification | 3 | 32, 16, 8 | 5, 5, 5 | 2 | - | 1 (2) | 3425 |
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Krasteva, V.; Ménétré, S.; Didon, J.-P.; Jekova, I. Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms. Sensors 2020, 20, 2875. https://doi.org/10.3390/s20102875
Krasteva V, Ménétré S, Didon J-P, Jekova I. Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms. Sensors. 2020; 20(10):2875. https://doi.org/10.3390/s20102875
Chicago/Turabian StyleKrasteva, Vessela, Sarah Ménétré, Jean-Philippe Didon, and Irena Jekova. 2020. "Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms" Sensors 20, no. 10: 2875. https://doi.org/10.3390/s20102875
APA StyleKrasteva, V., Ménétré, S., Didon, J. -P., & Jekova, I. (2020). Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms. Sensors, 20(10), 2875. https://doi.org/10.3390/s20102875