&lt;em&gt;&lt;/em&gt;Heartbeat Abnormality Detection using Machine Learning Models and Rate Variability (HRV) Data

Vyas, Pratik; Pandit, Diptangshu

doi:10.20944/preprints201807.0488.v1

Cited by 3 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Exceptionally high values are removed by setting an upper threshold. Each outlier datum is replaced with the average of the data values immediately preceding and following it, rather than simply removing the datum as shown in (2). As shown in Fig.…”

Section: B Data Processingmentioning

confidence: 99%

“…There have been many studies using the characteristics of heart rate variability (HRV). However, previous studies have used the redundant features of HRV for learning [2]. There is a limitation that only learning and testing through open datasets were conducted.…”

Section: Introductionmentioning

confidence: 99%

“…ACCORDING TO WINDOW SIZEThe model training is performed using an open heart rate database[2]. The open heart rate data are divided into 3717 data points with an optimal window size of 30 s, and the training and test sets are separated in an 8:2 ratio.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Untact Abnormal Heartbeat Wave Detection Using Non-Contact Sensor through Transfer Learning

Lee

2020

IEEE Access

View full text Add to dashboard Cite

Section: B Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Untact Abnormal Heartbeat Wave Detection Using Non-Contact Sensor through Transfer Learning

Lee

2020

IEEE Access

View full text Add to dashboard Cite

“…In the medical field, this study builds upon a fast-expanding literature that applies machine learning tools in health and healthcare forecasting. For example, the risk of the onset of a disease, whether it be cardiovascular [ 26 , 27 ] or not [ 28 ]; hospital discharge volume [ 29 ]; arrhythmia prevention [ 30 ], response to training in healthy individuals [ 31 ] or in individuals with pathologies [ 32 ]; identification of the existence of heart diseases [ 33 ]; or evaluation of the risk of mortality in subjects who had a heart attack during the previous year [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning

Staffini

Svensson

Chung

et al. 2021

Sensors

View full text Add to dashboard Cite

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).

show abstract

Contact-free heart rate measurement system for monitoring HRV using face detection

Kanan,

Sekar,

Ravichandran

et al. 2023

SUSTAINABLE DEVELOPMENTS IN MATERIALS SCIENCE, TECHNOLOGY AND ENGINEERING: Sustainable Development in Material Science of Today

View full text Add to dashboard Cite

<em></em>Heartbeat Abnormality Detection using Machine Learning Models and Rate Variability (HRV) Data

Cited by 3 publications

References 12 publications

Untact Abnormal Heartbeat Wave Detection Using Non-Contact Sensor through Transfer Learning

Untact Abnormal Heartbeat Wave Detection Using Non-Contact Sensor through Transfer Learning

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning

Contact-free heart rate measurement system for monitoring HRV using face detection

Contact Info

Product

Resources

About