Implementation of wavelet packet transform and non linear analysis for emotion classification in stroke patient using brain signals

Bong, Siao Zheng; Khairunizam, Wan; Murugappan, M.; Ibrahim, Norlinah Mohamed; Yuvaraj, Rajamanickam; Mohamad, Khairiyah

doi:10.1016/j.bspc.2017.03.016

Cited by 74 publications

(26 citation statements)

References 15 publications

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“…In this case, J contains the first derivative of the network error against network weight and bias. The Gauss-Newton method was modified as in (7) for the matrix with the expression of the Hessian equation.…”

Section: ( ) = (6)mentioning

confidence: 99%

“…In meanwhile, using all features obtained an accuracy of 70% [4]. However, other study involved emotional variables of post-stroke patients, which used Wavelet, KNN, and Probabilistic Neural Networks provided 82% of accuracy [7]]. This result is considering that emotions reflect the overall stroke disorder.…”

Section: B Backpropagation Levenberg-marquardt Compare Than Backpropmentioning

confidence: 99%

“…Meanwhile, implementation of wavelet transforms for the classification of emotions in post-stroke patients [7] and identifying variables that affect post-stroke patients [4]. EEG signals are also used to detect electrical abnormalities in the brain, such as Epilepsy [8].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Motor Imagery and Synchronization of Post-Stroke Patient EEG Signal

Fadiyah¹,

Djamal²

2019

EECSI

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Stroke attacks often cause disability, so the need for rehabilitation to restore patient's motor skills. Electroencephalogram (EEG) is an instrument that can capture electrical activity in the brain. Some post-stroke patients have brain electrical dysfunction so that EEG signal can achieve such as amplitude decrease, and wave differences from symmetric channels. However, EEG signal analysis is not easy because it has high complexity and small amplitude. However, information from EEG signals is beneficial, including for stroke identification. This study proposes the identification of EEG signals from post-stroke patients using wavelet extraction and Backpropagation Levernberg-Marquardt. EEG signals are recorded, extracted imagery motor variables, and synchronization of symmetric channels. The results of the study provide that the accuracy for identifying post-stroke EEG signals is 100% for training data and 79.69 % for new data. Research also shows that the use of learning rates affects accuracy. The smaller the learning rate provided accuracy is better. However, it had consequences for computing time so that the optimal learning rate is 0.0001.

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Section: ( ) = (6)mentioning

confidence: 99%

Section: B Backpropagation Levenberg-marquardt Compare Than Backpropmentioning

confidence: 99%

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Classification of Motor Imagery and Synchronization of Post-Stroke Patient EEG Signal

Fadiyah¹,

Djamal²

2019

EECSI

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“…The EEG recording device used for this study is Emotiv EPOC. The complete EEG data acquisition process and experiment protocol is described precisely in previous work [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Effectiveness of Tuned Q-factor Wavelet Transform in Emotion Recognition Among Left-brain Damaged Stroke Patients

Zheng

Khairunizam

Murugappan

et al. 2019

International Journal of Simulation: Systems, Science &Amp; Technology

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Emotion recognition is impaired among stroke patients due to brain injury. It has given negative impact towards stroke patients because of the difficulty in expressing themselves. Hence, the vision is to overcome this problem by creating a platform to predict the emotion of stroke patients for them so that recurrent stroke events can be avoided. EelectroEncephaloGraph (EEG) of 19 Left Brain Damage patients (LBD) are used as database. The objective of this paper is to compare the accuracy between Tuned Q-factor Wavelet Transform (TQWT) and Wavelet Packet Transform (WPT). The collected raw EEG signals are de-noised by using 6 th order Buttterworth filter. Then, filtered signals were fed into time-frequency analysis tools namely TQWT and WPT to transform the time domain signals into time-frequency domain signals. Hurst exponent feature is extracted from the corresponding time-frequency domain signals before enhancement by using principal component analysis (PCA). Lastly, classification is done through K-nearest neighbour (KNN), probabilistic neural network (PNN) and random forest (RF) in order to evaluate the performance of the recognition system. From the result, it is found that the classification accuracy is consistently higher in TQWT method.

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“…Hence, wearable technology delivering physiological information (Martínez‐Rodrigo et al, ) is a growing field of consumer electronics called to revolutionize personal health care, as it provides an inexpensive and non‐intrusive alternative to continuous health monitoring. Although an important number of wearable devices are able to detect some specific emotions (mostly, distress), only a few works have evaluated a wider range of emotions (Liu & Sourina, ; Yuvaraj & Murugappan, ; Bong et al, ).…”

Section: Introductionmentioning

confidence: 99%

Film mood induction and emotion classification using physiological signals for health and wellness promotion in older adults living alone

et al. 2019

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This paper introduces a wearable hardware/software system specifically tailored to detect seven emotions (neutral, tenderness, amusement, anger, disgust, fear, and sadness) aimed at promoting health and wellness in older adults living alone at home. The complete software and hardware architectures acquiring and processing electrodermal activity and photoplethysmography signals are introduced. The wearable emotion detection system is trained by eliciting the desired emotions on 39 older adults through a film mood induction procedure. Seventeen features are calculated on skin conductance response and heart rate variability data, grouped into five statistical, four temporal, and eight morphological features. Then, these features are used to run emotion classification considering support vector machines, decision trees, and quadratic discriminant analysis. In line with psychological findings, the results offer a global accuracy of 82% in negative emotion (anger, disgust, fear, and sadness) classification. For positive emotions (tenderness and amusement), also in conformity with previous psychological outcomes, amusement shows the highest ratio of hits (92%) but tenderness the lowest one (66%). These results demonstrate that our wearable emotion detection system can be used by ageing adults, especially for detecting negative emotions that usually damage health and wellness and lead to social isolation.

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Implementation of wavelet packet transform and non linear analysis for emotion classification in stroke patient using brain signals

Cited by 74 publications

References 15 publications

Classification of Motor Imagery and Synchronization of Post-Stroke Patient EEG Signal

Classification of Motor Imagery and Synchronization of Post-Stroke Patient EEG Signal

Effectiveness of Tuned Q-factor Wavelet Transform in Emotion Recognition Among Left-brain Damaged Stroke Patients

Film mood induction and emotion classification using physiological signals for health and wellness promotion in older adults living alone

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