Visualization and Sonification of Long-Term Epilepsy Electroencephalogram Monitoring

Lin, Jeng-Wei; Chen, Wei; Shen, Chia-Ping; Chiu, Ming‐Jang; Kao, Yu-Cheng; Lai, Feipei; Zhao, Qibin; Cichocki, Andrzej

doi:10.1007/s40846-017-0358-6

Cited by 15 publications

(12 citation statements)

References 22 publications

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“…This step is essential to distinguish between seizure itself-the ictal period-and the normal non-ictal period. Several algorithms have been used as a classifier such as artificial neural network (ANN) [31], support vector machine (SVM) [32], ensemble [33], K-nearest neighbors (KNN) [34,35], linear discriminant analysis (LDA) [36,37], logistic regression [38], decision tree [39], and Naïve Bayes [40,41].…”

Section: Related Workmentioning

confidence: 99%

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

et al. 2021

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Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure using several methods such as neurological examination, blood tests, computerized tomography (CT), magnetic resonance imaging (MRI) and electroencephalogram (EEG). Medical data, such as the EEG signal, usually includes a number of features and attributes that do not contains important information. This paper proposes an automatic seizure classification system based on extracting the most significant EEG features for seizure diagnosis. The proposed algorithm consists of five steps. The first step is the channel selection to minimize dimensionality by selecting the most affected channels using the variance parameter. The second step is the feature extraction to extract the most relevant features, 11 features, from the selected channels. The third step is to average the 11 features extracted from each channel. Next, the fourth step is the classification of the average features using the classification step. Finally, cross-validation and testing the proposed algorithm by dividing the dataset into training and testing sets. This paper presents a comparative study of seven classifiers. These classifiers were tested using two different methods: random case testing and continuous case testing. In the random case process, the KNN classifier had greater precision, specificity, positive predictability than the other classifiers. Still, the ensemble classifier had a higher sensitivity and a lower miss-rate (2.3%) than the other classifiers. For the continuous case test method, the ensemble classifier had higher metric parameters than the other classifiers. In addition, the ensemble classifier was able to detect all seizure cases without any mistake.

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Section: Related Workmentioning

confidence: 99%

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

et al. 2021

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“…This involves mapping the output of the binary classifier into two distinct audio waveforms representing the presence or absence of seizure. While the first approach discards valuable information in terms of the network interactions during a seizure event, the second approach conveys the decision of the classifier using sound [37]. It thus does not employ the end-user (clinician or caregiver) in the decision-making process.…”

Section: Experiments On Real Datasets: Sonification Of Eeg Recordings...mentioning

confidence: 99%

Using growth transform dynamical systems for spatio-temporal data sonification

Chatterjee¹,

Chakrabartty²

2021

Preprint

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Sonification, or encoding information in meaningful audio signatures, has several advantages in augmenting or replacing traditional visualization methods for human-in-the-loop decision-making. Standard sonification methods reported in the literature involve either (i) using only a subset of the variables, or (ii) first solving a learning task on the data and then mapping the output to an audio waveform, which is utilized by the end-user to make a decision. This paper presents a novel framework for sonifying high-dimensional data using a complex growth transform dynamical system model where both the learning (or, more generally, optimization) and the sonification processes are integrated together. Our algorithm takes as input the data and optimization parameters underlying the learning or prediction task and combines it with the psychoacoustic parameters defined by the user. As a result, the proposed framework outputs binaural audio signatures that not only encode some statistical properties of the high-dimensional data but also reveal the underlying complexity of the optimization/learning process. Along with extensive experiments using synthetic datasets, we demonstrate the framework on sonifying Electro-encephalogram (EEG) data with the potential for detecting epileptic seizures in pediatric patients.

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“…Manohar and Ganesan [4] investigate the specific texture features of schizophrenic MR images, with experimental results showing better accuracy than traditional classification methods. Lin et al [5] develop a novel EEG classification algorithm to identify normal, spike, and seizure EEG signals with high accuracy. Abdar et al [6] construct an early detection system integrating Multilayer Perceptron Neural Network (MLPNN) with various classification algorithms.…”

Section: Fewer Research Questions Diverse Fieldsmentioning

confidence: 99%