Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach

Igbe, Tobore; Kandwal, Abhishek; Li, Jingzhen; Yan, Yan; Omisore, Olatunji Mumini; Enitan, Efetobore; Li, Sinan; Liu, Yu‐Hang; Wang, Lei

doi:10.1016/j.knosys.2020.106464

Cited by 22 publications

(15 citation statements)

References 47 publications

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“…Depending on the data and the information to be extracted from the data, various machine learning algorithms for analysis and preprocessing are used. Support vector machines are used for analysing the ECG and EEG data for diabetes classification [19], Random Forest algorithms are used for prediction tasks like diabetes or hypertension prediction [20] [21]. Neural network algorithms have been used for the prediction task for disease diagnosis using data from wearable medical sensors [22], multilayer perceptrons are used for classification of different types of diabetes and for behavioural analysis for Parkinson's disease [23] [3].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…For ECG signals, features such as HRV (Heart Rate Variability), HR (Heart Rate), QT (time from the start of the Q wave to the end of the T wave), QRS (The period of QRS complex) can be obtained to represents various characteristics of heart [19]. In case of EEG signals, certain frequency bands such as theta, delta, alpha corresponding to different locations of brain helps in extracting the important features from the EEG signals [19]. So time domain and frequency domain features are used in most of the studies.…”

Section: B Feature Type Distributionsmentioning

confidence: 99%

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Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis

2021

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In this digitized world, data has become an integral part in any domain, including healthcare. The healthcare industry produces a huge amount of digital data, by utilizing information from all sources of healthcare, including the patients' demographics, medications, vital signs, physician's observations, laboratory data, billing data, data from various wearable sensors, etc. With the rapid growth of the wireless technology applications, there has also been a significant increase in the digital health data. New medical discoveries and new eHealth-related technologies, such as mobile apps, novel sensors, and wearable technology have contributed as important data sources for healthcare data. Nowadays, there is a huge potential to improve the healthcare quality and customer satisfaction with the help of machine learning (ML) algorithms applied on time-domain and frequency-domain healthcare data obtained from wearables and sensors. This systematic literature review examines in depth how health data from sensors can be processed and analyzed using ML techniques. The review focuses on the following diseases for obtaining the eHealth data: diabetes mellitus type 1 and type 2, hypertension and hypotension, atrial fibrillation, bradykinesia, dyskinesia, and fever related diseases. The data for the systematic literature review was collected from four databases, Medline, Proquest, Scopus, and Web of Science. We selected 67 studies for the final in-depth review out of the initial pre-selected papers. Our study identified that the major part of eHealth data is obtained from the sensors such as accelerometer, gyroscopes, ECG (Electrocardiogram), EEG (Electroencephalogram ) monitors, and blood glucose sensors. This study also examines the different feature types, feature extraction methods, and ML algorithms used for eHealth data analysis. Our review also shows that neural network (NN) algorithms and support vector machines (SVM) have shown so far the best performance for analyzing the healthcare data among other ML algorithms studied in the literature.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Section: B Feature Type Distributionsmentioning

confidence: 99%

Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis

2021

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“…The MP150 model 16channel multichannel physiological recorder is a computer-based data acquisition system, which can collect and analyze various electrophysiological signals, such as ECG, EEG, and EMG. It is widely used in physiological signal measurement [58,59].…”

Section: Data Collectionmentioning

confidence: 99%

Effects of the Spatial Structure Conditions of Urban Underpass Tunnels’ Longitudinal Section on Drivers’ Physiological and Behavioral Comfort

Feng

Yang

et al. 2021

IJERPH

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To investigate the physiological and behavioral comfort of drivers traversing urban underpass tunnels with various spatial structure conditions, a driving simulator experiment was conducted using 3DMAX and SCANeRTM studio software. Three parameters, including the slope, slope length, and height of a tunnel, were selected as research objects to explore the optimal combination of structural parameters in urban underpass tunnels. The heart rate (HR), interbeat (RR) interval, speed, and lane centerline offset value were collected for 30 drivers. Then, a measurement model of the relationship among HR, RR interval, speed, lane centerline offset value, and structural parameters was established by using partial correlation analyses and the stepwise regression method. On this basis, a structural constraint model based on the drivers’ physiological and behavioral comfort thresholds was also constructed. The results show that the driver’s HR, RR interval, speed, and lane centerline offsets are significantly related to the tunnel height, slope, and slope length. More importantly, this paper not only analyzed the effects of various structural parameters on drivers’ physiology and behavior but also proposed an optimized combination of structural parameters based on drivers’ physiological and behavioral comfort. It can reasonably improve tunnel design in China, ensure tunnel traffic safety, and seek the maximum comfort of the driver in the driving process.

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“…The approximation of time series is extremely important, both to reduce the volume of stored data and to reduce the amount of data transmitted and processed. In several applications, such as the Financial Market [Xin et al 2019] and the Health area [Tobore et al 2020], time series are widely used in solutions that generate a large volume of data in short periods of time. Because of this, data approximation techniques are valuable tools to obtain satisfactory system performance.…”

Section: Literature Review/related Workmentioning

confidence: 99%

Effects of agro-sensor time series approximation on plant stress detection: an experimental study

Júnior¹,

Sedrez²,

Monteiro

et al. 2021

Anais Do XIII Congresso Brasileiro De Agroinformática (SBIAGRO 2021)

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This paper describes an experimental study on the effect of reducing time series collected from IoT electrical agro-sensors through approximation techniques, in time series classification tasks, for plant stress detection. From large sets of real data, stored in time series format, experiments were carried out to analyze: (i) performance of mathematical methods to reduce the dimensionality of time series - PAA, SAX and MCB; and (ii) Whether the application of these techniques influences the performance of time series classification models for plant stress detection, using machine learning algorithms KNN, SVM and ANN. Both in terms of data volume reduction and time series classification, the experiment showed significant improvements in terms of compression rate and accuracy, with the best result found in the use of PAA+SAX techniques for reduction and SVM for classification.

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Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach

Cited by 22 publications

References 47 publications

Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis

Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis

Effects of the Spatial Structure Conditions of Urban Underpass Tunnels’ Longitudinal Section on Drivers’ Physiological and Behavioral Comfort

Effects of agro-sensor time series approximation on plant stress detection: an experimental study

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