Heart rate modeling and prediction using autoregressive models and deep learning
(2021) In Sensors 22(1).- Abstract
Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network)... (More)
Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual’s age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others).
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- author
- Staffini, Alessio ; Svensson, Thomas LU ; Chung, Ung Il and Svensson, Akiko Kishi LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Autoregressive model, Deep learning, Forecasting, Heart rate, Modeling, Prediction, Time series analysis
- in
- Sensors
- volume
- 22
- issue
- 1
- article number
- 34
- publisher
- MDPI AG
- external identifiers
-
- scopus:85121442286
- pmid:35009581
- ISSN
- 1424-8220
- DOI
- 10.3390/s22010034
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: Funding: This research was supported by the Center of Innovation Program from the Japan Science and Technology Agency (Grant Number JPMJCE1304), and Kanagawa Prefecture’s “Project to expand the use of metabolic syndrome risk index in municipalities” (2018). Funding Information: The study was conducted in accordance with the relevant ethical guidelines and regulations in Japan. All participants received detailed information about the original study and its purpose and provided written consent to participate. The study was approved by the Ethics Committee of the School of Engineering, The University of Tokyo (approval number: KE18-44), and the research was supported by the Center of Innovation Program from the Japan Science and Technology Agency (Grant Number JPMJCE1304) and Kanagawa Prefecture’s “A project to expand the use of metabolic syndrome risk index in municipalities” (2018). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- id
- 13d42979-c4db-41e4-8b19-fec71d5eac44
- date added to LUP
- 2022-01-01 15:24:23
- date last changed
- 2024-07-14 01:56:29
@article{13d42979-c4db-41e4-8b19-fec71d5eac44,
abstract = {{<p>Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual’s age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others).</p>}},
author = {{Staffini, Alessio and Svensson, Thomas and Chung, Ung Il and Svensson, Akiko Kishi}},
issn = {{1424-8220}},
keywords = {{Autoregressive model; Deep learning; Forecasting; Heart rate; Modeling; Prediction; Time series analysis}},
language = {{eng}},
number = {{1}},
publisher = {{MDPI AG}},
series = {{Sensors}},
title = {{Heart rate modeling and prediction using autoregressive models and deep learning}},
url = {{http://dx.doi.org/10.3390/s22010034}},
doi = {{10.3390/s22010034}},
volume = {{22}},
year = {{2021}},
}