Abstract
Deep learning architectures find applications where analysis of complex data inputs is demanding and regular neural networks may have problems. There are many types of deep learning models, however the most important to fit architecture and training model to the input data. In this article we propose a model of deep learning based on architecture in which we use BiLSTM neural network. Proposed model is trained by using Adam algorithm. For the research experiment we have examined also other latest algorithms to select the best configuration of proposed model. Results show that our proposed BiLSTM deep learning neural network archived over 99% of accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Xu, G., Meng, Y., Qiu, X., Yu, Z., Wu, X.: “Sentiment analysis of comment texts based on bilstm,” Ieee Access, vol. 7, pp. 51 522–51 532 (2019)
Pogiatzis, A., Samakovitis, G.: Using bilstm networks for context-aware deep sensitivity labelling on conversational data. Appl. Sci. 10(24), 8924 (2020)
Lu, W., Li, J., Wang, J., Qin, L.: A cnn-bilstm-am method for stock price prediction. Neural Comput. Appl. 33(10), 4741–4753 (2021)
Nowicki, R.K., Grzanek, K., Hayashi, Y.: “Rough support vector machine for classification with interval and incomplete data. J. Artif. Intell. Soft Comput. Res. 10 2020
Niksa-Rynkiewicz, T., Szewczuk-Krypa, N., Witkowska, A., Cpałka, K., Zalasiński, M., Cader, A.: “Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network. J. Artif. Intell. Soft Comput. Res. 11 (2021)
Jagvaral, B., Lee, W.-K., Roh, J.-S., Kim, M.-S., Park, Y.-T.: Path-based reasoning approach for knowledge graph completion using cnn-bilstm with attention mechanism. Expert Syst. Appl. 142, 112960 (2020)
Liu, K., Gao, W., Huang, Q.: Automatic modulation recognition based on a dcn-bilstm network. Sensors 21(5), 1577 (2021)
Xie, W., Wang, J., Xing, C., Guo, S., Guo, M., Zhu, L.: Variational autoencoder bidirectional long and short-term memory neural network soft-sensor model based on batch training strategy. IEEE Trans. Industr. Inf. 17(8), 5325–5334 (2020)
Nafea, O., Abdul, W., Muhammad, G., Alsulaiman, M.: Sensor-based human activity recognition with spatio-temporal deep learning. Sensors 21(6), 2141 (2021)
Zhao, C., Huang, X., Li, Y., Yousaf Iqbal, M.: “A double-channel hybrid deep neural network based on cnn and bilstm for remaining useful life prediction.” Sensors 20(24) 7109 (2020)
Jeong, J.-H., Shim, K.-H., Kim, D.-J., Lee, S.-W.: Brain-controlled robotic arm system based on multi-directional cnn-bilstm network using eeg signals. IEEE Trans. Neural Syst. Rehabil. Eng. 28(5), 1226–1238 (2020)
Beritelli, F., Capizzi, G., Sciuto, G.L., Napoli, C., Woźniak, M.: A novel training method to preserve generalization of rbpnn classifiers applied to ecg signals diagnosis. Neural Netw. 108, 331–338 (2018)
Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)
Zhai, X., Tin, C.: “Automated ecg classification using dual heartbeat coupling based on convolutional neural network.” IEEE Access. 6 27 465–27 472 (2018)
Huang, J., Chen, B., Yao, B., He, W.: “Ecg arrhythmia classification using stft-based spectrogram and convolutional neural network.” IEEE Access 7 92 871–92 880 (2019)
Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ecg classification by 1-d convolutional neural networks. IEEE Trans. Biomed. Eng. 63(3), 664–675 (2015)
Oh, S.L., Ng, E.Y., San Tan, R., Acharya, U.R.: “Automated diagnosis of arrhythmia using combination of cnn and lstm techniques with variable length heart beats.” Comput. Biol. Med. 102 278–287 (2018)
Acharya, U.R.: “A deep convolutional neural network model to classify heartbeats.” Comput. Biol. Med. 89 389–396 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Siłka, J., Wieczorek, M., Kobielnik, M., Woźniak, M. (2023). BiLSTM Deep Learning Model for Heart Problems Detection. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-23492-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23491-0
Online ISBN: 978-3-031-23492-7
eBook Packages: Computer ScienceComputer Science (R0)