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Detecting the Duration of Incomplete Obstructive Sleep Apnea Events Using Interhemispheric Features of Electroencephalography

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Detecting the Duration of Incomplete Obstructive Sleep Apnea Events Using Interhemispheric Features of Electroencephalography International Journal of Innovative Computing, Information and Control ICIC International c 2013 ISSN 1349-4198 Volume 9, Number 2, February 2013 pp. 705{725 DETECTING THE DURATION OF INCOMPLETE OBSTRUCTIVE SLEEP APNEA EVENTS USING INTERHEMISPHERIC FEATURES OF ELECTROENCEPHALOGRAPHY Chien-Chang Hsu1, Zhen-Gjia Cai1, Hsing Mei1 Hou-Chang Chiu2;3 and Chia-Mo Lin2;4 1Department of Computer Science and Information Engineering 2School of Medicine Fu Jen Catholic University No. 510, Chung Cheng Rd., Hsinchuang Dist., New Taipei City 242, Taiwan f cch; mei [email protected]; [email protected] 3Department of Neurology 4Sleep Center Shin Kong Wu Ho-Su Memorial Hospital No. 95, Wen Chang Rd., Shih Lin Dist., Taipei City 111, Taiwan f 054885; 081810 [email protected] Received November 2011; revised April 2012 Abstract. Obstructive sleep apnea not only affects sleep quality, it can also be life- threatening. For diagnosis and treatment, most clinicians use a patient's sleep data recorded by polysomnography. However, the amount of overnight sleep data is massive, which makes efficient and comprehensive data interpreting extremely challenging. Nu- merous detection methods have been developed; however, the accuracy of these methods must be improved. This work transforms electroencephalogram signals from the left and right hemispheres using a novel obstructive sleep apnea (OSA) detection system, and extracts signal features of delta waves using a bandpass filter, empirical mode decompo- sition, and Hilbert-Huang transformation. The start or end time of an incomplete OSA event is predicted based on the relationship between the time and frequency variation of detected complete OSA events. Experimental results demonstrate that the proposed sys- tem is accurate in detecting the frequency and duration of incomplete OSA events, and system performance is better than that of existing detection methods. The proposed sys- tem will provide useful auxiliary diagnostic data for physicians and technicians at sleep centers. Keywords: Obstructive sleep apnea, Electroencephalogram, Frequency variation, In- complete obstructive sleep apnea event, Start or end time prediction 1. Introduction. Obstructive sleep apnea (OSA) is a syndrome characterized by inter- ruption of airflow for 10 s to 2 min or obstruction of the upper respiratory tract. Once the brain senses that the oxygen supply is insufficient, the brain is aroused and instructs the body to move around or wake up to resume breathing. Notably, OSA affects the sleep quality of countless people. People with OSA are often unaware that they have OSA, even though their breathing is interrupted numerous times while sleeping, adversely affecting their sleep, resulting in lethargy during waking hours and poor concentration [2,11,21,35]. Over the long term, OSA affects cardiopulmonary functions and may cause sudden unex- pected nocturnal death syndrome [1]. Symptoms of OSA have garnered much attention from the medical community in recent years. Common indicators for sleep disorders in- clude the apnea index (AI), respiratory disturbance index (RDI), and apnea and hypopnea index (AHI) [2,30]. 705 706 C.-C. HSU, Z.-G. CAI, H. MEI, H.-C. CHIU AND C.-M. LIN Human brainwaves, which generate spontaneous and event-related electroencephalo- gram (EEG) signals, facilitate observations of various sleep signals [3,13,19,20,25,27,34]. Brainwave features are utilized when diagnosing various sleep disorders. Basically, the major spectrums of a sleep EEG are delta (0-4 Hz), theta (4-8 Hz), alpha (8-16 Hz), and beta (16-32 Hz) signals. Delta and theta signals provide useful data for identifying OSA events [30]. Currently, common methods for OSA event detection using EEG signals in- clude statistical, time-frequency, and frequency variation approaches [2,22,23,29,30]. The statistical approach uses statistical characteristics, such as mean and standard deviation, to identify correlations among various spectrum features. However, features of brainwaves are extremely complex, and identifying effective transient variations in the brain is diffi- cult. Additionally, identifying pathological correlations using a large number of statistical computations is a very complex process [10]. Therefore, precisely detecting the duration of an OSA event is a significant challenge. The time-frequency approach uses non-linear signal features to quantify time-frequency relationships such as approximate entropy and complexity calculations. The disadvantage of the time-frequency approach is that only a portion of each band of brainwave can be observed. Additionally, this approach cannot manage transient variations in brainwaves during OSA events and effectively detect the duration of OSA events [29,30]. By exploiting the sensitivity of delta waves in a sleep cycle, the frequency variation approach transforms the frequency ratio of delta wave in sleep signals into frequency variations. This approach accurately estimates the start and end times of OSA events rather than simply classifies information into different categories. However, the principal drawback of the frequency variation approach is that it can only detect the duration of complete OSA events, not the start and end time points of incom- plete events [15]. Additionally, the approach may also misclassify a single OSA event as multiple events, or vice versa. The relationship between the human left and right brain is sustained by biological path- ways that deliver messages and maintain close connections between the two hemispheres [2]. The functional asymmetry of the brain has attracted significant interest in recent years, and discoveries made by recent studies have been applied to different diseases such as Alzheimer's disease and epilepsy [2,10,12]. The EEG spectral energy in the transition from non-rapid eye movement (NREM) to rapid eye movement (REM) varies for certain bands [35]. Abeyrantne et al. utilized interhemispheric signals to identify correlations between the interhemispheric synchrony index (IHSI) and RDI [2]. However, the IHSI must be converted according to the whole data of each case, hindering detection of the duration of each OSA event. Moreover, the time resolution for an OSA event is low and detecting its precise time of occurrence is difficult. Therefore, OSA events cannot be identified effectively. This work uses the brainwaves features of the left and right hemispheres to estimate the duration of incomplete OSA events. By detecting changes in EEG signals from the left and right hemispheres during OSA events, the start and end time of incomplete OSA events can be inferred based on the detected duration of complete OSA events. Experimental results show that the proposed approach substantially improves the accuracy of OSA event detection in terms of the aspects of time and the performance of overall system. The remainder of this paper is organized as follows. Section 2 reviews related work of feature extraction and interhemispheric analysis of EEG signals. Section 3 introduces the architecture of the proposed system. Section 4 discusses experiment results. Section 5 gives conclusions. DETECTING THE DURATION OF INCOMPLETE OSA EVENTS 707 2. Feature Extraction and Interhemispheric Analysis of EEG Signals. Exist- ing EEG feature extraction approaches can be classified into statistical and frequency- space approaches [7-9,19,24,26,28,40,41]. Statistical approaches usually combine neural networks with statistical features to determine whether variations among samples are apparent based on deviations in statistical calculations. These approaches analyze the relationships among numerous unknown nonlinear and decision variables through neu- ronal interactions processed by neural networks [31]. For example, Subasi extracted the wavelet coefficient as features to detect epileptic seizures using dynamic fuzzy neural net- works [32]. Subasi further extracted wavelet coefficients by transforming EEG signals through the discrete wavelet [33]. Subasi then applied the extracted wavelet coefficients to calculate statistical features, such as the mean absolute value of coefficients, average power of wavelet coefficients, standard deviation of coefficients, and ratio of absolute mean values of adjacent sub-bands. The acquired values were subsequently used to analyze the correlation between awake and sleep states. Ubeyli, who also used EEG signals to ex- tract discrete wavelet coefficients, employed the wavelet coefficient to calculate statistical features, such as mean absolute value of wavelet coefficient, absolute maximum value of wavelet coefficient, average power, standard deviation, ratio of absolute mean values of adjacent sub-bands, comparisons of distortions in the distribution coefficient [37], and maximum, minimum, mean, and standard deviation to detect epilepsy through multilayer perceptron neural networks (MLPNNs) [38,39]. Kurt used EEG signals to identify stages 0 and 1 in sleep cycles [18]. The signals of EEGs, electrooculograms, and electromyograms were first transformed by a discrete wavelet, filter, and the filtered frequency spectrum. Data obtained were subsequently analyzed using MLPNNs to identify the correlation be- tween awake and sleep states. Guo, who used the frequency variation and amplitude of signals as a line length feature, employed the length of the delta wave line in EEG signals to detect epilepsy, Alzheimer's disease, and Parkinson's disease [10]. The approach using frequency-space relationships quantifies the frequency and space features of various abnormal brainwaves using EEG signal-frequency-related
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