Browsing by Subject "Neurology"

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    Detection of cardiac arrhythmia using autonomic nervous system, Gaussian mixture model and artificial neural network
    (Institute of Electrical and Electronics Engineers, 2020) Terzi, Merve Begüm; Arıkan, Orhan
    In this study, a new technique which detects anomalies in skin sympathetic nerve activity (SKNA) by using state-of-the-art signal processing and machine learning methods is developed to perform the robust detection of cardiac arrhythmia (CA). For this purpose, a signal processing technique that simultaneously obtains SKNA and ECG from wideband recordings on MIT-BIH database is developed. By using preprocessed data, a novel feature extraction technique which obtains SKNA features that are critical for the reliable detection of CA is developed. By using extracted features, a supervised learning technique based on artificial neural network (ANN) and an unsupervised learning technique based on Gaussian mixture model (GMM) are developed to perform the robust detection of SKNA anomalies. A Neyman-Pearson type of approach is developed to perform the robust detection of outliers that correspond to CA. The performance results of the proposed technique over MIT-BIH database showed that the technique provides highly reliable detection of CA by performing the robust detection of SKNA anomalies. Therefore, in cases where the diagnostic information of ECG is not sufficient for the reliable diagnosis of CA, the proposed technique can provide early diagnosis of the disease, which can lead to a significant reduction in the mortality rates of cardiovascular diseases.
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    Detection of epileptic indicators on clinical subbands of EEG
    (IEEE, 2008-08) Yücel, Zeynep; Özgüler, A. Bülent
    Symptoms of epilepsy, which is characterized by abnormal brain electrical activity, can be observed on electroencephalography (EEG) signal. This paper employs models of chaotic measures on standard clinical subbands of EEG and aims to help detection of epilepsy seizures and diagnosis of epileptic indicators in interictal signals. copyright by EURASIP.
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    ItemOpen Access
    Detection of myocardial infarction using autonomic nervous system, Gaussian mixture model and artificial neural network
    (Institute of Electrical and Electronics Engineers, 2020) Terzi, Merve Begüm; Arıkan, Orhan
    In this study, a new technique which detects anomalies in skin sympathetic nerve activity (SKNA) and ECG by using state-of- the-art signal processing and machine learning methods is developed to perform the robust detection of myocardial infarction (MI). For this purpose, a signal processing technique that simultaneously obtains SKNA and ECG from wideband recordings on PTB-EKG database is developed. By using preprocessed data, a novel feature extraction technique which obtains SKNA features that are critical for the reliable detection of MI is developed. By using extracted features, a supervised learning technique based on artificial neural network (ANN) and an unsupervised learning technique based on Gaussian mixture model (GMM) are developed to perform the robust detection of SKNA anomalies. A Neyman-Pearson type of approach is developed to perform the robust detection of outliers that correspond to MI. The performance results of the proposed technique over PTB-EKG database showed that the technique provides highly reliable detection of MI by performing the robust detection of SKNA anomalies. Therefore, in cases where the diagnostic information of ECG is not sufficient for the reliable diagnosis of MI, the proposed technique can provide early diagnosis of the disease, which can lead to a significant reduction in the mortality rates of cardiovascular diseases.