Diagnosis of congestive heart failure patients using Poincare measures derived from ECG signals

İşler, Yalçın; Kuntalp, Mehmet

doi:10.1109/biyomut.2009.5130287

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…An ellipse is fitted to the Poincare plot graph, and the standard deviation of the distance of the points on these plots indicates the width ( SD 1) and length ( SD 2) of the ellipse (Brennan et al, 2001 ). Poincare plot measures can be calculated as follows (Isler, 2009 ; Isler and Kuntalp, 2009 ):…”

Section: Methodsmentioning

confidence: 99%

“…Its simple visual interpretation and its proven clinical ability as a predictor of disease and cardiac dysfunction made this technique popular in the analysis of different physiological signals (Isler and Kuntalp, 2007 ; Isler, 2009 ; Narin et al, 2014 ; Isler et al, 2019 ). Taking into account its performance in other studies (Isler and Kuntalp, 2009 ; Cancioglu et al, 2021 ), Poincare plot measurements can be an effective method for non-linear dynamics of EEG signals that complicate the processing of them. Considering the contributions and deficiencies of the existing studies, in this study, a feature extraction method based on the combination of Poincare plot measurements from the non-linear feature extraction methods with temporal features and spectral features is implemented for MI task classification.…”

Section: Related Workmentioning

confidence: 99%

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Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs

Degirmenci

Yüce²,

Perc

et al. 2023

Front. Hum. Neurosci.

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In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require effective feature extraction methods and classification algorithms to acquire discriminative features from EEG signals due to the non-linear and non-stationary structure of EEG signals. This study investigates the effect of statistical significance-based feature selection on binary and multi-class Motor Imagery EEG signal classifications. In the feature extraction process performed 24 different time-domain features, 15 different frequency-domain features which are energy, variance, and entropy of Fourier transform within five EEG frequency subbands, 15 different time-frequency domain features which are energy, variance, and entropy of Wavelet transform based on five EEG frequency subbands, and 4 different Poincare plot-based non-linear parameters are extracted from each EEG channel. A total of 1,364 Motor Imagery EEG features are supplied from 22 channel EEG signals for each input EEG data. In the statistical significance-based feature selection process, the best one among all possible combinations of these features is tried to be determined using the independent t-test and one-way analysis of variance (ANOVA) test on binary and multi-class Motor Imagery EEG signal classifications, respectively. The whole extracted feature set and the feature set that contain statistically significant features only are classified in this study. We implemented 6 and 7 different classifiers in multi-class and binary (two-class) classification tasks, respectively. The classification process is evaluated using the five-fold cross-validation method, and each classification algorithm is tested 10 times. These repeated tests provide to check the repeatability of the results. The maximum of 61.86 and 47.36% for the two-class and four-class scenarios, respectively, are obtained with Ensemble Subspace Discriminant among all these classifiers using selected features including only statistically significant features. The results reveal that the introduced statistical significance-based feature selection approach improves the classifier performances by achieving higher classifier performances with fewer relevant components in Motor Imagery task classification. In conclusion, the main contribution of the presented study is two-fold evaluation of non-linear parameters as an alternative to the commonly used features and the prediction of multiple Motor Imagery tasks using statistically significant features.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs

Degirmenci

Yüce²,

Perc

et al. 2023

Front. Hum. Neurosci.

Self Cite

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“…Therefore, we preferred the k-fold CV method as in our similar studies (Isler, 2009;Isler and Kuntalp, 2009;Degirmenci et al, 2022a,b) and the recent literature (Anam et al, 2019(Anam et al, , 2020Kato et al, 2020;Mwata-Velu et al, 2021Azizah et al, 2022;Zahra et al, 2022). Using k-fold cross-validation (CV), the training data set was divided into k equal-sized subsets.…”

Section: Performance Evaluationmentioning

confidence: 99%

EEG-based finger movement classification with intrinsic time-scale decomposition

Degirmenci,

Yuce,

Perc

et al. 2024

Front. Hum. Neurosci.

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IntroductionBrain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals.MethodsIn this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not.ResultsAs a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems.DiscussionWhen compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).

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“…Bu yöntem verilen noktalar arasında yumuşak bir geçiş yaparak gerçek sürekliliği göstermektedir. [11].…”

Section: Verilerin Elde Edilmesiunclassified

Diagnosis of congestive heart failure using poincare map plot

Yayık

Kutlu

2012

2012 20th Signal Processing and Communications Applications Conference (SIU)

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ÖZETÇEBu çalışmada, konjestif kalp yetmezligi (KKY) olan hastaların ayırt edilmesi için ham EKG verilerinden elde edilen Poincare haritası kullanılmıştır. internet üzerinden ücretsiz olarak sağlanan KKY ve normal EKG verileri incelenmiştir. Poincare haritası ızgaralara bölünmüş ve her hücreye düşen nokta sayısı belirlenerek bu değerler knn(k-enyakın komşu) sınıflandırıcıda kullanılmıştır. Sonuç olarak, normaller ile KKY hastalarının Ham EKG kayıtlarının Poincare haritasından elde edilen öznitelikler ile ayrıştırılabileceği görülmüştür. ABSTRACT In this study, in order to diagnose congestive heart failure patients (CHF), Poincare map obtained from raw ECG data is used. CHF and normal ECG data, which are distributed freely via internet, are analyzed. Poincare map is divided into equal rectangle cells and points in all of the cells are determined. These values are used for knn (k-nearest neighbour) classification. At the result of this study, it is considered that CHF patients and normal people can be separated each other using features obtained from Poincare map of Raw ECG record.

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Diagnosis of congestive heart failure patients using Poincare measures derived from ECG signals

Cited by 4 publications

References 15 publications

Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs

Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs

EEG-based finger movement classification with intrinsic time-scale decomposition

Diagnosis of congestive heart failure using poincare map plot

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