Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet’s cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen’s Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen’s Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.

Wavelet Filter Banks Using Allpass Filters

Allpass filter is a computationally efficient versatile signal processing building block. The interconnection of allpass filters has found numerous applications in digital filtering and wavelets. In this chapter, we discuss several classes of wavelet filter banks by using allpass filters. Firstly, we describe two classes of orthogonal wavelet filter banks composed of two real allpass filters or a complex allpass filter, and then consider design of orthogonal filter banks without or with symmetry, respectively. Next, we present two classes of filter banks by using allpass filters in lifting scheme. One class is causal stable biorthogonal wavelet filter bank and another class is orthogonal wavelet filter bank, all with approximately linear phase response. We also give several design examples to demonstrate the effectiveness of the proposed method.

Three Channel Wavelet Filter Banks With Minimal Time Frequency Spread for Classification of Seizure-Free and Seizure EEG Signals

The authors compute the classification accuracy of minimal time-frequency spread wavelet filter bank with three channels in discriminating seizure-free and seizure electroencephalogram (EEG) signals. Wavelet filter bank with three channels generates two wavelet functions and one scaling function at the first level of wavelet decomposition. A time-frequency localized filter bank can be generated by minimizing the time spread and frequency spread of any one or all the functions simultaneously. The minimal time-frequency spread wavelet filter bank with three channels of regularity order, one designed with several different time-frequency optimality criteria and length six, are chosen, and the effect of each optimality criterion on the discrimination of seizure-free and seizure EEG signals is computed. The classification accuracy for five different optimality criteria are computed. Time-frequency localized three-band filter bank of length six classifies, the seizure-free and seizure EEG signals of Bonn University EEG database, with 98.25% of accuracy.

Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank

Hypertension (HT) is an extreme increment in blood pressure that can prompt a stroke, kidney disease, and heart attack. HT does not show any symptoms at the early stage, but can lead to various cardiovascular diseases. Hence, it is essential to identify it at the beginning stages. It is tedious to analyze electrocardiogram (ECG) signals visually due to their low amplitude and small bandwidth. Hence, to avoid possible human errors in the diagnosis of HT patients, an automated ECG-based system is developed. This paper proposes the computerized segregation of low-risk hypertension (LRHT) and high-risk hypertension (HRHT) using ECG signals with an optimal orthogonal wavelet filter bank (OWFB) system. The HRHT class is comprised of patients with myocardial infarction, stroke, and syncope ECG signals. The ECG-data are acquired from physionet’s smart health for accessing risk via ECG event (SHAREE) database, which contains recordings of a total 139 subjects. First, ECG signals are segmented into epochs of 5 min. The segmented epochs are then decomposed into six wavelet sub-bands (WSBs) using OWFB. We extract the signal fractional dimension (SFD) and log-energy (LOGE) features from all six WSBs. Using Student’s t-test ranking, we choose the high ranked WSBs of LOGE and SFD features. We develop a novel hypertension diagnosis index (HDI) using two features (SFD and LOGE) to discriminate LRHT and HRHT classes using a single numeric value. The performance of our developed system is found to be encouraging, and we believe that it can be employed in intensive care units to monitor the abrupt rise in blood pressure while screening the ECG signals, provided this is tested with an extensive independent database.