Quantile graphs for EEG-based diagnosis of Alzheimer’s disease

Pineda, Aruane M.; Ramos, Fernando M.; Betting, Luiz Eduardo; Campanharo, Andriana S. L. O.

doi:10.1371/journal.pone.0231169

Cited by 41 publications

(38 citation statements)

References 53 publications

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“…They also verified that as the time series changes from periodic to chaotic, the corresponding networks, initially regular, become more and more random with larger values of the average path length 10 and clustering coefficient. The work developed by Campanharo, Doescher, and Ramos (2018) and Pineda, Ramos, Betting, and Campanharo (2020) takes advantage of the QGs to classify EEG data.…”

Section: Mapping Univariate Time Series Into Complex Networkmentioning

confidence: 99%

Time series analysis via network science: Concepts and algorithms

Silva

Ribeiro

et al. 2021

WIREs Data Min & Knowl

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There is nowadays a constant flux of data being generated and collected in all types of real world systems. These data sets are often indexed by time, space, or both requiring appropriate approaches to analyze the data. In univariate settings, time series analysis is a mature field. However, in multivariate contexts, time series analysis still presents many limitations. In order to address these issues, the last decade has brought approaches based on network science. These methods involve transforming an initial time series data set into one or more networks, which can be analyzed in depth to provide insight into the original time series. This review provides a comprehensive overview of existing mapping methods for transforming time series into networks for a wide audience of researchers and practitioners in machine learning, data mining, and time series. Our main contribution is a structured review of existing methodologies, identifying their main characteristics, and their differences. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified way and language. We first describe the case of univariate time series, which can be mapped to single layer networks, and we divide the current mappings based on the underlying concept: visibility, transition, and proximity. We then proceed with multivariate time series discussing both single layer and multiple layer approaches. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic. This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Knowledge Representation

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Section: Mapping Univariate Time Series Into Complex Networkmentioning

confidence: 99%

Time series analysis via network science: Concepts and algorithms

Silva

Ribeiro

et al. 2021

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…Nonetheless, visual analysis of EEG data is time-consuming, requires specialized training, and is error-prone [16][17][18]. However, we can consider automatic evaluation of EEG time series using modern classification algorithms, which can help to improve the efficiency and accuracy of AD and SZ diagnosis, as verified in previous works [19][20][21][22].…”

Section: Introductionmentioning

confidence: 87%

EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia

Alves¹,

Pineda²,

Roster³

et al. 2021

Preprint

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Mental disorders are among the leading causes of disability worldwide. The first step in treating these conditions is to obtain an accurate diagnosis, but the absence of established clinical tests makes this task challenging. Machine learning algorithms can provide a possible solution to this problem, as we describe in this work. We present a method for the automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG time series and deep learning. We show that our approach can classify patients with Alzheimer's disease and schizophrenia with a high level of accuracy. The comparison with the traditional cases, that use raw EEG time series, shows that our method provides the highest precision. Therefore, the application of deep neural networks on data from brain connections is a very promising method to the diagnosis of neurological disorders.

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“…Kim et al [ 45 ] demonstrated that selected set of features from EDA signal can be used as a good biomarker for automatic detection of major depressive disorder using a decision tree classifier. Pineda et al [ 46 ] presented a machine learning model for diagnosis of Alzheimer’s disease using EEG signal. They extracted five typological network features from quantile graphs of EEG signal and presented them to SVM classifier to detect the disease using data collected from 24 healthy and 24 patient subjects.…”

Section: Related Workmentioning

confidence: 99%

Exploration of physiological sensors, features, and machine learning models for pain intensity estimation

2021

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In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device.

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Quantile graphs for EEG-based diagnosis of Alzheimer’s disease

Cited by 41 publications

References 53 publications

Time series analysis via network science: Concepts and algorithms

Time series analysis via network science: Concepts and algorithms

EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia

Exploration of physiological sensors, features, and machine learning models for pain intensity estimation

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