Time-Series Forecasting of Chaotic Dynamic Signals by Machine Learning Methods for Heart Rate Variability

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Document Type : Original Article

Authors

1 Assistant professor of Electrical and Computer engineering Faculty. University of Kashanu. Kashan. Iran

2 Control Engineering Group, Department of Electrical and computer Engineering, University of Kashan, Kashan, Iran

10.22034/tjee.2026.66884.5011

Abstract

Heart rate variability (HRV), obtained from RR intervals in ECG signals, reflects the activity of the autonomic nervous system, which is challenging to forecast due to its nonlinear and chaotic nature. In this paper, labeled ECG signals were first obtained from the Physionet database. Then, fourth-order Butterworth bandpass filter and phase space reconstruction with a sliding window approach were applied to these chaotic signals and trained with four machine learning methods. Since LSTM and CNN showed better performance than other methods due to their temporal and frequency capabilities, respectively, a combined LSTM+CNN method was proposed, which improved the RMSE and R² evaluation criteria and was shown to have the following capabilities: robustness against white and pink noise, the ability to distinguish unlabeled arrhythmia patient signals from healthy signals, and the ability to forecast both healthy and unhealthy HRV signals.

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Tabriz Journal of Electrical Engineering

Articles in Press, Corrected Proof
Available Online from 11 April 2026

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