Automatic classification of respiratory patterns involving missing data imputation techniques

Hernández-Pereira, Elena; Álvarez-Estévez, Diego; Moret‐Bonillo, Vicente

doi:10.1016/j.biosystemseng.2015.06.011

Cited by 23 publications

(3 citation statements)

References 30 publications

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“…The strategy or algorithm to be used in a project as well as its effectiveness and performance are strongly dependent on the problem domain (e.g., data structure, database size, etc.) [53]. It is therefore impossible to choose a method as the best one regardless of domain intricacies.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Breathing Monitoring and Pattern Recognition with Wearable Sensors

Costa¹,

Vara²,

Cristino³

et al. 2019

Wearable Devices - The Big Wave of Innovation

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This chapter introduces the anatomy and physiology of the respiratory system, and the reasons for measuring breathing events, particularly, using wearable sensors. Respiratory monitoring is vital including detection of sleep apnea and measurement of respiratory rate. The automatic detection of breathing patterns is equally important in other respiratory rehabilitation therapies, for example, magnetic resonance exams for respiratory triggered imaging, and synchronized functional electrical stimulation. In this context, the goal of many research groups is to create wearable devices able to monitor breathing activity continuously, under natural physiological conditions in different environments. Therefore, wearable sensors that have been used recently as well as the main signal processing methods for breathing analysis are discussed. The following sensor technologies are presented: acoustic, resistive, inductive, humidity, acceleration, pressure, electromyography, impedance, and infrared. New technologies open the door to future methods of noninvasive breathing analysis using wearable sensors associated with machine learning techniques for pattern detection.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Breathing Monitoring and Pattern Recognition with Wearable Sensors

Costa¹,

Vara²,

Cristino³

et al. 2019

Wearable Devices - The Big Wave of Innovation

View full text Add to dashboard Cite

show abstract

“…The value of k chosen for the feature imputations is the same for each station and for each of the stations it is the root of n [9,15] where n is the number of complete observations of the station after pruning.…”

Section: K-nearest Neighbour Imputation (K-nn)mentioning

confidence: 99%

“…This absence of data creates an added difficulty in scientific research [14], firstly, because of the absence of the data itself, which impoverishes the data as a whole [15,16] and, secondly, because most of the existing data analysis procedures are not designed or adapted for the absence of observations [17].…”

Section: Introductionmentioning

confidence: 99%

A Method of Pruning and Random Replacing of Known Values for Comparing Missing Data Imputation Models for Incomplete Air Quality Time Series

et al. 2022

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The data obtained from air quality monitoring stations, which are used to carry out studies using data mining techniques, present the problem of missing values. This paper describes a research work on missing data imputation. Among the most common methods, the method that best imputes values to the available data set is analysed. It uses an algorithm that randomly replaces all known values in a dataset once with imputed values and compares them with the actual known values, forming several subsets. Data from seven stations in the Silesian region (Poland) were analyzed for hourly concentrations of four pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particles of 10 μm or less (PM10) and sulphur dioxide (SO2) for five years. Imputations were performed using linear imputation (LI), predictive mean matching (PMM), random forest (RF), k-nearest neighbours (k-NN) and imputation by Kalman smoothing on structural time series (Kalman) methods and performance evaluations were performed. Once the comparison method was validated, it was determine that, in general, Kalman structural smoothing and the linear imputation methods best fitted the imputed values to the data pattern. It was observed that each imputation method behaves in an analogous way for the different stations The variables with the best results are NO2 and SO2. The UMI method is the worst imputer for missing values in the data sets.

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Non-linear missing data imputation for healthcare data via index-aware autoencoders

Kabir

Farrokhvar

2022

Health Care Manag Sci

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Automatic classification of respiratory patterns involving missing data imputation techniques

Cited by 23 publications

References 30 publications

Breathing Monitoring and Pattern Recognition with Wearable Sensors

Breathing Monitoring and Pattern Recognition with Wearable Sensors

A Method of Pruning and Random Replacing of Known Values for Comparing Missing Data Imputation Models for Incomplete Air Quality Time Series

Non-linear missing data imputation for healthcare data via index-aware autoencoders

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