27 research outputs found
Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)
Epilepsy is a disease that attacks the brain and results in seizures due to neurological disorders. The electrical activity of the brain recorded by the EEG signal test, because EEG test can be used to diagnose brain and mental diseases such as epilepsy. This study aims to identify whether a person has epilepsy or not along with the result of accurate, sensitivity, and precision rate using Fast Fourier Transform (FFT) and Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The FFT is used to transform EEG signals from time-based into frequency-based and continued with feature extraction to take characteristics from each filtering signal using the median, mean, and standard deviations of each EEG signal. The results of the feature extraction used for input on the category process based on characteristics data (classification) using ANFIS. EEG signal data is obtained from epilepsy center online database of Bonn University, German. The results of the EEG signal classification system using ANFIS with two classes (Normal-Epilepsy) states accuracy, sensitivity, and precision of 100%. The classification systems with three class division (Normal-Not Seizure Epilepsy-Epilepsy) resulted in an accuracy of 89.33% sensitivity of 89.37% and precision of 89.33%.Epilepsi merupakan penyakit yang menyerang otak dan mengakibatkan seseorang mengalami kejang karena adanya gangguan system saraf pusat (neurologis) sehingga menyebabkan hilang kesadaran. Perekaman aktifitas listrik otak maka digunakan uji sinyal EEG, karena dengan uji EEG dapat digunakan untuk mendiagnosa penyakit otak dan mental seperti epilepsi. Tujuan yang hendak dicapai pada penelitian ini adalah agar dapat mengidentifikasi seseorang mengidap epilepsi atau tidak menggunakan metode Fast Fourier Transform (FFT) dan Adaptive Neuro Fuzzy Inference System (ANFIS) serta hasil tingkat akurasi, sensitivitas, dan presisi dari metode tersebut. Metode FFT digunakan untuk mentransformasikan sinyal EEG yang semula berbasis waktu menjadi sinyal EEG berbasis frekuensi dan dilanjutkan dengan proses ekstraksi fitur dari setiap sinyal hasil pemfilteran dengan menggunakan median, mean dan standart deviasi pada masing-masing sinyal EEG. Hasil dari ektraksi fitur digunakan sebagai input pada proses klasifikasi sinyal EEG menggunakan metode ANFIS. Hasil sistem klasifikasi dengan dua kelas (Normal-Epilepsi) menghasilkan akurasi, sensitivitas, dan presisi sebesar 100% dan sistem klasifikasi sinyal EEG menggunakan ANFIS dengan pembagian tiga kelas (Normal-Not Seizure Epilepsy-Epilepsy) menghasilkan akurasi sebesar 89.33% sensitivitas 89.37% dan presisi sebesar 89.33%
Health Electroencephalogram epileptic classification based on Hilbert probability similarity
This paper has proposed a new classification method based on Hilbert probability similarity to detect epileptic seizures from electroencephalogram (EEG) signals. Hilbert similarity probability-based measure is exploited to measure the similarity between signals. The proposed system consisted of models based on Hilbert probability similarity (HPS) to predict the state for the specific EEG signal. Particle swarm optimization (PSO) has been employed for feature selection and extraction. Furthermore, the used dataset in this study is Bonn University's publicly available EEG dataset. Several metrics are calculated to assess the performance of the suggested systems such as accuracy, precision, recall, and F1-score. The experimental results show that the suggested model is an effective tool for classifying EEG signals, with an accuracy of up to 100% for two-class status
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification
Identifying seizure activities in non-stationary electroencephalography (EEG)
is a challenging task, since it is time-consuming, burdensome, and dependent on
expensive human resources and subject to error and bias. A computerized seizure
identification scheme can eradicate the above problems, assist clinicians and
benefit epilepsy research. So far, several attempts were made to develop
automatic systems to help neurophysiologists accurately identify epileptic
seizures. In this research, a fully automated system is presented to
automatically detect the various states of the epileptic seizure. The proposed
method is based on sparse representation-based classification (SRC) theory and
the proposed dictionary learning using electroencephalogram (EEG) signals.
Furthermore, the proposed method does not require additional preprocessing and
extraction of features which is common in the existing methods. The proposed
method reached the sensitivity, specificity and accuracy of 100% in 8 out of 9
scenarios. It is also robust to the measurement noise of level as much as 0 dB.
Compared to state-of-the-art algorithms and other common methods, the proposed
method outperformed them in terms of sensitivity, specificity and accuracy.
Moreover, it includes the most comprehensive scenarios for epileptic seizure
detection, including different combinations of 2 to 5 class scenarios. The
proposed automatic identification of epileptic seizures method can reduce the
burden on medical professionals in analyzing large data through visual
inspection as well as in deprived societies suffering from a shortage of
functional magnetic resonance imaging (fMRI) equipment and specialized
physician
VPNet: Variable Projection Networks
In this paper, we introduce VPNet, a novel model-driven neural network
architecture based on variable projections (VP). The application of VP
operators in neural networks implies learnable features, interpretable
parameters, and compact network structures. This paper discusses the motivation
and mathematical background of VPNet as well as experiments. The concept was
evaluated in the context of signal processing. We performed classification
tasks on a synthetic dataset, and real electrocardiogram (ECG) signals.
Compared to fully-connected and 1D convolutional networks, VPNet features fast
learning ability and good accuracy at a low computational cost in both of the
training and inference. Based on the promising results and mentioned
advantages, we expect broader impact in signal processing, including
classification, regression, and even clustering problems
Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory
COVID-19 is an infectious disease that mostly affects the respiratory system.
At the time of this research being performed, there were more than 1.4 million
cases of COVID-19, and one of the biggest anxieties is not just our health, but
our livelihoods, too. In this research, authors investigate the impact of
COVID-19 on the global economy, more specifically, the impact of COVID-19 on
financial movement of Crude Oil price and three U.S. stock indexes: DJI, S&P
500 and NASDAQ Composite. The proposed system for predicting commodity and
stock prices integrates the Stationary Wavelet Transform (SWT) and
Bidirectional Long Short-Term Memory (BDLSTM) networks. Firstly, SWT is used to
decompose the data into approximation and detail coefficients. After
decomposition, data of Crude Oil price and stock market indexes along with
COVID-19 confirmed cases were used as input variables for future price movement
forecasting. As a result, the proposed system BDLSTM+WT-ADA achieved
satisfactory results in terms of five-day Crude Oil price forecast.Comment: 26 pages, 9 figure
Caracterización de señales electroencefalográficas utilizando la transformada wavelet discreta como herramienta para apoyar el diagnóstico del trastorno por déficit de atención e hiperactividad TDAH
Despite arising in childhood, attention deficit hyperactivity disorder (ADHD) can persist into adulthood, compromising the individual’s social skills. ADHD diagnosis is a real chal- lenge due to its dependence on the clinical observation of the patient, the information provided by parents and teachers, and the clinicians’ expertise..
Entropy-based machine learning model for diagnosis and monitoring of Parkinson's Disease in smart IoT environment
The study presents the concept of a computationally efficient machine
learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) in
an Internet of Things (IoT) environment using rest-state EEG signals (rs-EEG).
We computed different types of entropy from EEG signals and found that Fuzzy
Entropy performed the best in diagnosing and monitoring PD using rs-EEG. We
also investigated different combinations of signal frequency ranges and EEG
channels to accurately diagnose PD. Finally, with a fewer number of features
(11 features), we achieved a maximum classification accuracy (ARKF) of ~99.9%.
The most prominent frequency range of EEG signals has been identified, and we
have found that high classification accuracy depends on low-frequency signal
components (0-4 Hz). Moreover, the most informative signals were mainly
received from the right hemisphere of the head (F8, P8, T8, FC6). Furthermore,
we assessed the accuracy of the diagnosis of PD using three different lengths
of EEG data (150-1000 samples). Because the computational complexity is reduced
by reducing the input data. As a result, we have achieved a maximum mean
accuracy of 99.9% for a sample length (LEEG) of 1000 (~7.8 seconds), 98.2% with
a LEEG of 800 (~6.2 seconds), and 79.3% for LEEG = 150 (~1.2 seconds). By
reducing the number of features and segment lengths, the computational cost of
classification can be reduced. Lower-performance smart ML sensors can be used
in IoT environments for enhances human resilience to PD.Comment: 19 pages, 10 figures, 2 table