Effect of power feature covariance shift on BCI spatial-filtering techniques: A comparative study

Miladinović, Aleksandar; Ajčević, Miloš; Jarmolowska, Joanna; Marušič, Uroš; Colussi, Marco; Silveri, Giulia; Battaglini, Piero Paolo; Accardo, Agostino

doi:10.1016/j.cmpb.2020.105808

Cited by 31 publications

(25 citation statements)

References 48 publications

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“…Despite the growing use of machine learning-based prediction models in medicine [5][6][7][8][9], clinicians still struggle to rely on these models in clinical practice [10]. Machine learning methods were also applied to produce heart disease detection and prediction models [11] based on clinical history and ECG features [12], magnetocardiography [13], photoplethysmography signal parameters [14], and HRV and blood pressure variability features [15].…”

Section: Introductionmentioning

confidence: 99%

Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability

Accardo

Restivo

Ajčević

et al. 2022

Med Biol Eng Comput

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Diagnosis of etiology in early-stage ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) patients may be challenging. We aimed at investigating, by means of classification and regression tree (CART) modeling, the predictive power of heart rate variability (HRV) features together with clinical parameters to support the diagnosis in the early stage of IHD and DCM. The study included 263 IHD and 181 DCM patients, as well as 689 healthy subjects. A 24 h Holter monitoring was used and linear and non-linear HRV parameters were extracted considering both normal and ectopic beats (heart rate total variability signal). We used a CART algorithm to produce classification models based on HRV together with relevant clinical (age, sex, and left ventricular ejection fraction, LVEF) features. Among HRV parameters, MeanRR, SDNN, pNN50, LF, LF/HF, LFn, FD, Beta exp were selected by the CART algorithm and included in the produced models. The model based on pNN50, FD, sex, age, and LVEF features presented the highest accuracy (73.3%). The proposed approach based on HRV parameters, age, sex, and LVEF features highlighted the possibility to produce clinically interpretable models capable to differentiate IHD, DCM, and healthy subjects with accuracy which is clinically relevant in first steps of the IHD and DCM diagnostic process. Graphical abstract

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Section: Introductionmentioning

confidence: 99%

Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability

Accardo

Restivo

Ajčević

et al. 2022

Med Biol Eng Comput

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“…Furthermore, the design of Brain-Computer Interface motor rehabilitation [20], feasible in neurologic disorders [21,22], and in control of assistive robots [23] could be improved by understanding EEG changes in PDs. Indeed, EEG changes over time can introduce nonstationarities and diminish BCI accuracy [24]. The intersession transfer learning techniques improve the performance in PDs [25], but its further advance requires a better understanding of the PD pathology related EEG.…”

Section: Introductionmentioning

confidence: 99%

EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands

Miladinović

Ajčević

Busan

et al. 2021

Procedia Computer Science

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“…Noises and other external interferences are always present in raw EEG data of emotion recognition that is most robust [356]. It comprises undesired signals generated by changes in electrode location as well as noise from the surroundings [357].…”

Section: Non-stationaritymentioning

confidence: 99%

Brain-Computer Interface: Advancement and Challenges

Mridha

Das

Kabir

et al. 2021

Sensors

118

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Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

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Effect of power feature covariance shift on BCI spatial-filtering techniques: A comparative study

Cited by 31 publications

References 48 publications

Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability

Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability

EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands

Brain-Computer Interface: Advancement and Challenges

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