The design and implementation of cardiotocography signals classification algorithm based on neural network. (English) Zbl 1431.92101
Summary: Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named “MKNet” and recurrent neural network named “MKRNN” are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification.
MSC:
| 92C55 | Biomedical imaging and signal processing |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
References:
| [1] | Watson, D., The detection, investigation and management of hypertension in pregnancy, Australian and New Zealand Journal of Obstetrics and Gynaecology, 40, 139-155 (2010) |
| [2] | Rosendorff, C.; Lackland, D. T.; Allison, M., Treatment of hypertension in patients with coronary artery disease: a scientific statement from the American Heart Association, American College of Cardiology, and American Society of Hypertension, Circulation, 131, 19, e435-e470 (2015) · doi:10.1161/cir.0000000000000207 |
| [3] | Johnson, P.; Andrews, D. C., Remote continuous physiological monitoring in the home, Journal of Telemedicine and Telecare, 2, 2, 107-113 (1996) · doi:10.1177/1357633x9600200207 |
| [4] | Salamalekis, E.; Thomopoulos, P.; Giannaris, D., Computerised intrapartum diagnosis of fetal hypoxia based on fetal heart rate monitoring and fetal pulse oximetry recordings utilising wavelet analysis and neural networks, BJOG: An International Journal of Obstetrics and Gynaecology, 109, 10, 1137-1142 (2002) · doi:10.1016/s1470-0328(02)01988-2 |
| [5] | Macones, G. A.; Hankins, G. D.; Spong, C. Y.; Hauth, J.; Moore, T., The 2008 National Institute of Child Health and Human Development workshop report on electronic fetal monitoring: update on definitions, interpretation, and research guidelines, Journal of Obstetric Gynecologic and Neonatal Nursing, 37, 510-515 (2010) |
| [6] | Yeh, S. Y.; Betyar, L.; Hon, E. H., Computer diagnosis of fetal heart rate patterns, American Journal of Obstetrics and Gynecology, 114, 7, 890-897 (1972) · doi:10.1016/0002-9378(72)90093-2 |
| [7] | Amato, F.; López, A.; Pena-Mendez, E.; Vanhara, P.; Hampl, A.; Havel, J., Artificial neural networks in medical diagnosis, Journal of Applied Biomedicine, 11, 2, 47-58 (2013) · doi:10.2478/v10136-012-0031-x |
| [8] | Sundar, C.; Chitradevi, M.; Chitradevi, M.; Geetharamani, G., Classification of cardiotocogram data using neural network based machine learning technique, International Journal of Computer Applications, 47, 19-25 (2013) |
| [9] | Peterek, T.; Gajdoš, P.; Dohnálek, P.; Krohová, J., Human Fetus Health Classification on Cardiotocographic Data Using Random Forests (2014), Switzerland: Springer International Publishing, Switzerland |
| [10] | Karpathy, A.; Toderici, G.; Shetty, S.; Leung, T.; Sukthankar, R.; Li, F. F., Large-scale video classification with convolutional neural networks, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition |
| [11] | Bradley, A. P., The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition, 30, 1145-1159 (1997) · doi:10.1016/s0031-3203(96)00142-2 |
| [12] | Dong, C.; Chen, C. L.; Tang, X., Accelerating the Super-5 Resolution Convolutional Neural Network, Computer Vision-ECCV 2016, 391-407 (2016), Berlin, Germany: Springer International Publishing, Berlin, Germany |
| [13] | Spence, A. M., The learning curve and competition, Bell Journal of Economics, 12, 1, 49-70 (2000) · doi:10.2307/3003508 |
| [14] | Rubin, J.; Parvaneh, S.; Rahman, A.; Conroy, B.; Babaeizadeh, S., Densely connected convolutional networks and signal quality analysis to detect atrial fibrillation using short single-lead ECG recordings, Proceedings of Computing in Cardiology Conference |
| [15] | Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R., Dropout: a simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, 15, 1929-1958 (2014) · Zbl 1318.68153 |
| [16] | Lee, J. S., Digital Image Enhancement and Noise Filtering by Use of Local Statistics (1980), Washington, DC, USA: IEEE Computer Society, Washington, DC, USA |
| [17] | Jezewski, J.; Roj, D.; Wrobel, J.; Horoba, K., A novel technique for fetal heart rate estimation from doppler ultrasound signal, BioMedical Engineering OnLine, 10, 1, 92 (2011) · doi:10.1186/1475-925x-10-92 |
| [18] | Spilka, J.; Georgoulas, G.; Karvelis, P., Automatic evaluation of FHR recordings from CTU-UHB CTG database, Proceedings of International Conference on Information Technology in Bio- and Medical Informatics, Springer |
| [19] | Krupa, B. N.; Mohd Ali, M. A.; Zahedi, E., The application of empirical mode decomposition for the enhancement of cardiotocograph signals, Physiological Measurement, 30, 729-743 (2009) · doi:10.1088/0967-3334/30/8/001 |
| [20] | Chen, J.; Jönsson, P.; Tamura, M.; Gu, Z.; Matsushita, B.; Eklundh, L., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter, Remote Sensing of Environment, 91, 332-344 (2004) · doi:10.1016/j.rse.2004.03.014 |
| [21] | Paul, R. H.; Suidan, A. K.; Yeh, S.; Schifrin, B. S.; Hon, E. H., Clinical fetal monitoring. VII. The evaluation and significance of intrapartum baseline FHR variability, American Journal of Obstetrics and Gynecology, 123, 206-210 (1975) · doi:10.1016/0002-9378(75)90528-1 |
| [22] | Spilka, J.; Chudáček, V.; Koucký, M., Using nonlinear features for fetal heart rate classification, Biomedical Signal Processing and Control, 7, 4, 350-357 (2012) · doi:10.1016/j.bspc.2011.06.008 |
| [23] | Russakovsky, O.; Deng, J.; Su, H., ImageNet large scale visual recognition challenge, International Journal of Computer Vision, 115, 211-252 (2015) · doi:10.1007/s11263-015-0816-y |
| [24] | Ketkar, N., Convolutional neural networks, Deep Learning with Python (2017), Berkeley, CA, USA: Apress, Berkeley, CA, USA |
| [25] | Erhan, D.; Manzagol, P. A.; Bengio, Y.; Bengio, S.; Vincent, P., The difficulty of training deep architectures and the effect of unsupervised pre-training, Immunology of Fungal Infections, 5, 153-160 (2009) |
| [26] | Manaswi, N. K., Understanding and working with Keras, Deep Learning with Applications Using Python (2018), Berkeley, CA, USA: Apress, Berkeley, CA, USA |
| [27] | Graves, A.; Mohamed, A. R.; Hinton, G., Speech recognition with deep recurrent neural networks, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 |
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