Total views : 228

Heart Rate Variability using Neural Network


  • Department of Electronics and Communication, AIACTR, Delhi – 110031, India


Objective: This paper investigates heart rate variability using neural networks. Methods: A software is developed to detect the heart rate variation using ECG signals. For this purpose the signal is divided into sub samples that are overlapping to a certain extent. The signals are transformed into the frequency domain using FFT (Fast Frequency Transform) and to decompose the signal, wavelet transform is used. Further the features are extracted using the above transforms which are fed into the neural networks. Findings: Heart rate variation is calculated using neural networks where the features are used to train them. For training, two learning algorithms are used, LM (Levenberg Marquardt) and BR (Bayesian Regularization). LM is found to converge faster than BR but the latter has higher efficiency. Application: Variation in heart rate can be used for better detection of diseases.


Bayesian Regularization, Heart Rate Variability, Levenberg Marquardt Machine Learning, Signal Processing.

Full Text:

 |  (PDF views: 154)


  • Christoph B, Kurt S, Stijn WD, Leonhardt S. Adaptive BeattoBeat Heart Rate Estimation in Ballistocardiograms. IEEE Transactions on Information Technology in Biomedicine. 2011; 15(5): 778–86. Crossref PMid:21421447
  • A Brief Introduction to Neural Networks. David Kriesel
  • Neural Networks. Date Accessed: 14/12/2009.
  • Davis Artificial Intelligence Meetup Basic%20of%20 Artificial%20Neural%20Network.pdf. Date Accessed: 15/11/2016.
  • Hasan Muaidi. Levenberg-Marquardt Learning Neural Network For Part-of-Speech Tagging of Arabic Sentences. Wseas Transactions On Computers. 2014; 13: 300–09.
  • Levenberg–MarquardtTraining. Date Accessed: 25/02/2010.
  • Saini LM, Soni MK. Artificial Neural Network based Peak Load Forecasting using Levenberg-Marquardt and quasiNewton methods. IEEE Proceedings General Transmission Distribution. 2002. Crossref
  • Ji Q, Zhang L. A Bayesian Network Model for Automatic and Interactive Image Segmentation. IEEE - IEEE Transactions on Image Processing. 2011; 20(9):2582–93. Crossref PMid:21356618
  • Vaart V. Bayesian Regularization. International Congress of Mathematicians. Hyderabad: 2010 Aug.
  • Zhu J, Chen N, Xing EP. Bayesian Inference with Posterior Regularization and Applications to Infinite Latent SVMs. Journal of Machine Learning Research. 2014; 15: 1799– 1847.
  • Battiti R. First And second order for learning: between steepest descent and newton’s method. https:// www. ile/Roberto_Batt it i /publication/2498372_First_and_SecondOrder_Methods_for_Learning_Between_Steepest_Descent_and_Newton’s_ Method/links/5537b11c0cf2058efdeae0b6.pdf.
  • Signal Processing.
  • Dhiman R, Saini JS, Priyanka . Genetic Algorithm Tuned Expert Model for Detection of Epileptic Seizures from EEG Signals. Applied Soft Computing. 2014 June; 19: 8–17. Crossref
  • Mallat SG, Zhong S. Characterization of Signals from Multiscale Edges. IEEE Transactions Pattern Analysis and Machine Intelligence. 1992 July; 14(7): 1–23. Crossref


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.