# An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram

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## Abstract

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_{1}score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Experimental Data

#### 2.2. Model Architecture

#### 2.3. Theoretical Gains of 1-D OctConv

#### 2.4. Visualization of Class Discriminative Regions

## 3. Results and Discussion

#### 3.1. Experiment Setup

#### 3.2. Evaluation Metrics

_{1}score. Precision is a measure of exactness that depicts the capacity of the model at detecting true fQRS complexes out of all the detections it makes. Recall is a measure of completeness that depicts the model’s capacity at finding the true fQRS complexes. F

_{1}score is the harmonic mean of precision and recall. They are calculated as

#### 3.3. Results and Interpretations

_{1}score on the test dataset (F

_{1}-test) first increased marginally and then slowly declined with the growth of α. The highest F

_{1}score of 0.911 was reached at α = 0.25 when the computation of the convolutional layers (CNN-GFLOPs) was reduced by around 20%. We attributed the increase in F

_{1}score to OctConv’s effective design of multi-frequency processing and the resultant contextual-information augmentation by the enlargement of receptive fields. It is interesting to note that the compression to half the resolution of 75% feature maps resulted in only 1.4% F

_{1}score drop. To better support the generalizability of our approach, we also performed cross-validation to obtain means and standard deviations of the model’s performance over 10 folds of the dataset (F

_{1}-cross). We observed that the cross-validated performance showed a similar trend with the results on the selected test set, with even smaller F

_{1}score gaps between different α. Likewise, the GPU inference time also diminished with the drop in the number of FLOPs and the increase in α. These results demonstrated 1-D OctConv’s capability of grouping and compressing the smoothly changed time-series feature maps. Note that OctConv is orthogonal and complementary to existing methods for improving CNNs’ efficiency. By combining OctConv with popular techniques, such as pruning [29] and depth-wise convolutions [30], we can further cut down the computing cost of the model.

_{1}-score grew sharply with the increase in the sequence length and reached the peak at 10 timesteps before leveling off. Also, it is worth noting that the computational cost and memory footprint of the model grow with the increase in the input sequence length. This result validated our input segmentation strategy as well as showed the necessity of the recurrent layer to model the sequential nature of the fQRS complexes.

_{1}score was 0.815 at noise level 3 but decreased to 0.627 at noise level 9 when data was completely corrupted. Besides, the model achieved the F

_{1}score of 0.844 on the dataset disturbed with motion artifacts. These promising results demonstrate the robustness of our method in practical scenarios against different types of noise.

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Decomposition of fetal/maternal ECG (f/mECG) into low-frequency and high-frequency components (from data a74-channel 1): (

**a**) Low-frequency f/mECG part with the dominant frequencies of below 1 Hz, as shown in power spectral density (PSD) plot in the top right corner; (

**b**) high-frequency f/mECG part dominantly belongs to the frequency of above 100 Hz, as shown in the PSD plot in the top right corner.

**Figure 2.**Model architecture for the fetal QRS complex detection. High-frequency and low-frequency feature maps are denoted by blue and yellow rectangles, respectively. The sizes of the feature maps are indicated inside the rectangles, with the hyper-parameter α that controls the ratio of channels allocated to the low-resolution features. Shortcut connections are denoted by the arrows with the plus signs.

**Figure 3.**(

**a**) 1-D vanilla convolution operation on mixed-frequency feature maps of the same resolution. (

**b**) 1-D octave convolution on decomposed feature maps where low-frequency channels have 50% resolution. Red arrows denote inter-frequency information exchange $({f}^{H\to L},{f}^{L\to H})$, while green arrows denote intra-frequency information update $({f}^{H\to H},{f}^{L\to L})$, where f

^{A}

^{→B}denotes the convolutional operation from feature map group A to group B. The l and c denote the temporal dimension and the number of channels, respectively. The ratio α of input channels (α

_{in}) and output channels (α

_{out}) are set at the same value throughout the network, except that the first OctConv has α

_{in}= 0 and α

_{out}= α, while the last OctConv has α

_{in}= α and α

_{out}= 0.

**Figure 4.**The impact of the number of recurrent timesteps that corresponds to the input sequence length.

**Figure 6.**Examples of class activation maps for fetal QRS detections: (

**a**) 4 × 100 window frames with annotated fQRS complex (marked in gray); (

**b**) and (

**d**) two input sequences of 4 × 1000 with two fQRS complexes and three fQRS complexes annotated, respectively; (

**c**) two main spikes correspond to the fQRS complex positions of the signals in (

**b**); (

**e**) three main spikes correspond to the fQRS complex positions of the signals in (

**d**).

**Table 1.**Theoretical gains of computational cost and memory consumption of 1-D OctConv (α = 0 is the case of vanilla convolution).

Ratio (α) | 0 | 0.25 | 0.5 | 0.75 |
---|---|---|---|---|

#FLOPs Cost | 100% | 78% | 63% | 53% |

Memory Cost | 100% | 88% | 75% | 63% |

**Table 2.**Performance on the original PhysioNet dataset. Inference time was measured on 420 4 × 1000 instances with Nvidia GeForce GTX 1060 Max-Q GPU (Nvidia, Santa Clara, CA, USA).

α | F_{1}-Test | F_{1}-Cross | CNN-GFLOPs | GRU-FC-GFLOPs | Inference Time (s) |
---|---|---|---|---|---|

0 | 0.907 | 0.872 ± 0.048 | 0.52 | 3 × 10^{−4} | 0.59 |

0.25 | 0.911 | 0.874 ± 0.054 | 0.42 | 3 × 10^{−4} | 0.55 |

0.5 | 0.901 | 0.869 ± 0.059 | 0.34 | 3 × 10^{−4} | 0.48 |

0.75 | 0.894 | 0.866 ± 0.058 | 0.29 | 3 × 10^{−4} | 0.45 |

Types of Noise | SNR Level (dB) | Motion Noise | ||
---|---|---|---|---|

50.6 | 36.8 | 29.12 | ||

F_{1} | 0.815 | 0.739 | 0.627 | 0.844 |

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**MDPI and ACS Style**

Vo, K.; Le, T.; Rahmani, A.M.; Dutt, N.; Cao, H.
An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram. *Sensors* **2020**, *20*, 3757.
https://doi.org/10.3390/s20133757

**AMA Style**

Vo K, Le T, Rahmani AM, Dutt N, Cao H.
An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram. *Sensors*. 2020; 20(13):3757.
https://doi.org/10.3390/s20133757

**Chicago/Turabian Style**

Vo, Khuong, Tai Le, Amir M. Rahmani, Nikil Dutt, and Hung Cao.
2020. "An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram" *Sensors* 20, no. 13: 3757.
https://doi.org/10.3390/s20133757