Non-Invasive Fetal Cardiac Arrhythmia Detection

Seck Miao Zhen Christabel
Temasek Polytechnic
Singapore

Soe Moe Hnin Sett
Temasek Polytechnic
Singapore

1907149G@student.tp.edu.sg 1901947J@student.tp.edu.sg
1. INTRODUCTION

Fetal cardiac arrhythmias are any irregular fetal cardiac rhythm or
regular rhythm at a rate outside the reference range of 100 to 200
beats per minute (bpm). Arrhythmias discovered in about 1% offetuses, with about 10% of these considered as potential sources ofmorbidity. Although most fetal arrhythmias are benign, some cancause fetal hydrops and fetal death [1].
The invasive direct fetal electrocardiogram monitors the fetal heart
rate variability. This method involves inserting scalp electrode into
the woman’s cervical opening and attaching it to the baby’s head.
The direct application of an electrode to the fetal scalp would
require adequate cervical dilation and may lead to rupturing ofamniotic membranes, which poses a risk of injury to the fetal scalp[2]. Conversely, non-invasive fetal electrocardiography (NI-FECG)
performed by placing surface electrodes on the women’s abdomen
to obtain FECG signals. This approach also offers clinicalinformation to assist in detecting fetal distress, and thus it provides
safe and novel diagnostic possibilities for prenatal treatment toarrhythmic fetuses.

Muhammad Adam
Temasek Polytechnic
Singapore

muhdadam@tp.edu.sg

Table 1. The studies on fetal cardiac arrhythmia detection
using NI-FECG.
Author/Year

MethodBest Result

Sharma et al., 2021 [3]

2D CNN

96.31 accuracy

Zhong et al., 2018 [4]1D CNN91.33% accuracyLee et al., 2018 [5]1D CNN

93.27 % accuracy

1.1 Literature Review

The studies on fetal cardiac arrhythmia detection using NI-FECG
as listed in Table 1.

2. MATERIALS AND METHODS
The following are the materials used:

Non-Invasive Fetal ECG Arrhythmia Database [1]

12 arrhythmias and 14 normal rhythm datasets

The following are the methods used:Noise and baseline wander removal

Segmentation

Multi-Layer Perceptron (MLP)1-Dimensional Convolutional Neural Network (1D CNN)

3. RESULTS

The preprocessed (noise and baseline wander removed)
arrhythmias and normal rhythm datasets are segmented into lengthsof four FECG beats. In total, 2376-segmented FECGs for eachclass. The segmented FECG are used to trained and test the baseline
MLP and 1D CNN models of various layers. Further, the models
experimented on model improvement methods, such as thecallbacks, dropout and weight regularization, together with K-foldcross validation and confusion matrix. Overall, 1D CNN model offive layers (with callbacks of patience=30) yielded the bestvalidation accuracy of 94.99% and training accuracy of 94.03% at
epoch 35 as shown in Figure 1.

Figure 1. The classification performance of 1D CNN
five layers model with callbacks.

ACKNOWLEDGMENTS

We deeply appreciate for the sponsors of HPC Asia 2022.

REFERENCES[1] Behar JA, Bonnemains L, Shulgin V, Oster J, Ostras O,
Lakhno I. Noninvasive fetal electrocardiography for thedetection of fetal arrhythmias. Prenatal diagnosis. 2019 Jan 2.
[2] Smith, V., Arunthavanathan, S., Nair, A., Ansermet, D., daSilva Costa, F., & Wallace, E. M. (2018). A systematic reviewof cardiac time intervals utilising non-invasive fetal
electrocardiogram in normal fetuses. BMC pregnancy andchildbirth, 18(1), 1-15.
[3] Sharma, K., & Masood, S. (2021). Deep Learning-Based Noninvasive Fetal Cardiac Arrhythmia Detection. In Applicationsof Artificial Intelligence and Machine Learning (pp. 511-523).
Springer, Singapore.
[4] Zhong, W., Liao, L., Guo, X., & Wang, G. (2018). A deeplearning
approach
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QRS
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[5] Lee, J. S., Seo, M., Kim, S. W., & Choi, M. (2018, September).
Fetal QRS detection based on convolutional neural networks
in noninvasive fetal electrocardiogram. In 2018 4thInternational Conference on Frontiers of Signal Processing(ICFSP) (pp. 75-78). IEEE.