The Impact of Missing Data on Heart Rate Variability Features: A Comparative Study of Interpolation Methods for Ambulatory Health Monitoring

Benchekroun, Mouna; Chevallier, Baptiste; Zalc, Vincent; Istrate, Dan; Lenné, Dominique; Vera, Nicolas

doi:10.1016/j.irbm.2023.100776

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…The concept assumes a linear relationship between data points and fills in missing values by estimating them as a linear function of neighboring data points. We determined the missing value (y) using the equation of a straight line to connect the two data points as shown in Equation ( 4) [32].…”

Section: Data Pre-processingmentioning

confidence: 99%

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis

Maciejewska,

Azizah,

Szczurek

2023

Sensors

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High-quality indoor air is essential in open-kitchen restaurants for ensuring a healthy workplace and comfortable conditions for visitors. In this study, indoor air quality interdependence between the kitchen and the dining zones in open-kitchen restaurants was analyzed. The method was based on measurements of selected air parameters using a sensor technique and mutual information (MI) analysis. A long-term approach (based on a several-hour time series) and a short-term approach (based on a several-minute time series) were applied. This study involved four open-kitchen restaurants. The indoor conditions were represented by the temperature, relative humidity, CO2 concentration, and content of the total volatile organic compounds (TVOC) in the air. The MI analyses showed that the long-term co-dependence of the indoor conditions between the kitchen and the dining zones was smaller during business hours (MI = 0.12 ÷ 0.40) compared to night hours (MI = 0.24 ÷ 0.58). The ranking of the long-term MI values for the individual air parameters was MICO2 (0.34) ≅ MIT (0.34) > MIRH (0.28) > MITVOC (0.23). The short-term interdependencies were smaller during night hours (median MI = 0.01 ÷ 0.56) compared to business hours (MI = 0.23 ÷ 0.61). Additionally, the short-term MI was subject to high temporal variability. The ranking of the short-term MI values for the individual air parameters was MICO2 (0.48) > MIT (0.46) > MIRH (0.37) > MITVOC (0.26). Due to the weak and highly variable co-dependence of the air parameters between the kitchen and dining areas, multi-zone monitoring of air parameters with an emphasis on TVOC measurements is recommended to ensure proper indoor conditions in open-kitchen restaurants. The presented approach may be applied to design indoor air quality monitoring and ventilation systems not only in open-kitchen restaurants but also in other interiors with functionally different zones.

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Section: Data Pre-processingmentioning

confidence: 99%

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis

Maciejewska,

Azizah,

Szczurek

2023

Sensors

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show abstract

“…The normal human IBI range varies from 0.6-1 s that corresponds to normal sinus rhythms of 100-60 bpm [1] [7]. Modeling the human heartbeat based on previous observations is useful in various applications, e.g., heart rate variability (HRV) analysis [8], error correction [9], personalized healthcare monitoring, and early detection of heart abnormalities [10]. Therefore, paying attention to the sufficiency and relevance of the recorded data when such models are trained is crucial.…”

mentioning

confidence: 99%

Temporal Modeling of Instantaneous Interbeat Interval Based on Physical Activity

Mojtahed,

Rao,

Paolini

et al. 2023

IEEE Access

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Heartbeat serves as a vital sign of health, aiding the diagnosis of various health issues. The autonomic nervous system (ANS) is responsible for regulating heartbeat, and physical activity (PA) can influence the heart rhythm. Changes in heart rate can signal alterations in PA. However, poor measurements or extreme conditions can result in the loss of heartbeat data, which can negatively impact the analysis of heartbeats. To accurately monitor the Heart Rate Variability (HRV) and cardiovascular health, a proper model to compensate for lost data is necessary. This study investigated the effect of different PAs on InterBeat Interval (IBI) prediction and the possibility of using models trained on unrelated activities to predict the next IBI. The IBI series is divided into piecewise stationary sections based on PA, that is, running, walking, and sitting, as verified by a statistical test. Various machine and deep learning methods have been used to model the IBIs related to specific activities. The models were then used to predict the next IBI for the testing sets of related and unrelated activities, and the error changes were compared for each permutation of training and testing. The models were tested using a Physionet archived dataset. The findings suggest that linear models offer the least prediction error, whereas PA-relevant training could minimize errors in most scenarios. However, in cases where specific PA data are not available, the proposed CNN model demonstrated superior generalization capabilities. These findings can improve HRV error correction techniques and enhance cardiovascular health and ANS monitoring.INDEX TERMS Machine learning, piecewise stationarity, time series prediction, heart rate, interbeat interval, physical activity I. INTRODUCTION

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Machine Learning-Driven Improvements in HRV Artifact Correction for Psychosis Prediction in the Schizophrenia Spectrum

Tsakmaki,

Tasoulis,

Georgakopoulos

et al. 2024

Communications in Computer and Information Science

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The Impact of Missing Data on Heart Rate Variability Features: A Comparative Study of Interpolation Methods for Ambulatory Health Monitoring

Cited by 5 publications

References 20 publications

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis

Temporal Modeling of Instantaneous Interbeat Interval Based on Physical Activity

Machine Learning-Driven Improvements in HRV Artifact Correction for Psychosis Prediction in the Schizophrenia Spectrum

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