Design of Orthogonal Wavelet Filters with Minimum RMS Bandwidth Using A Symbolic Approach

Luo, Ziwang; Tay, D.B.H.; Lai, Xiaoping; Lin, Zhiping

doi:10.1109/iscas51556.2021.9401592

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…There are two ways to improve the bandwidth: using symbolic methods or solving a constrained optimization problem. This problem can be transformed into a convex optimization problem, specifically a semidefinite programming (SDP) problem, that enforces nonnegativity constraints during optimization [40], [41], [42].…”

Section: B Orthogonal Filter Bank and Wavelet Decompositionmentioning

confidence: 99%

Automated Explainable Detection of Cyclic Alternating Pattern (CAP) Phases and Sub-Phases Using Wavelet-Based Single-Channel EEG Signals

et al. 2023

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Sleep is a crucial component of health and well-being. It maintains the metabolism of the body and covers one-third of total life. The assessment of sleep quality is typically done by evaluating the macrostructure-based sleep stages, however, it does not take into account transient phenomena like K-complexes and transient fluctuations, which are crucial for the diagnosis of various sleep disorders. Cyclic alternating pattern (CAP) is a recurrent physiological electroencephalogram (EEG) activity that takes place in the brain during sleep and it is considered as a microstructure of sleep that can provide more accurate and relevant evaluation of sleep. The traditional way of CAP phase division is done manually by sleep specialists, which is sensitive, time-consuming, and prone to inaccuracies. Hence, there is a need for automated detection techniques that can solve the problems. This study proposes an automated, computerized approach for developing a machine learning model with explainable artificial intelligence (XAI) capabilities, using wavelet-based Hjorth parameters for classifying CAP A & B phases and phases A sub-phases (A1, A2, A3). The study utilizes SHAP (SHapley Additive exPlanations)-based feature ranking to provide insights into the model. This study uses the publicly accessible Physionet CAP sleep database. The model is developed using single-channel standardized EEG recordings from healthy subjects and patients with five types of sleep disorders, namely, insomnia, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement disorder (PLM), rapid eye movement behavior disorder (RBD) and narcolepsy. The best performance is obtained using k-nearest neighbors (KNN) and ensemble bagged trees (EbagT) classifiers. The proposed model achieved a average classification accuracy of 91.6% for healthy subjects and 94.33%, 86.3%, 88.68%, 84.43%, and 88.5% for narcolepsy, RBD, PLM, NFLE, and insomnia subjects respectively, for classifying phases A and B. Our model achieved a average classification accuracy of 92.85% for healthy subjects and 93.9%, 84.9%, 88.0%, 80.92%, and 89.41% for narcolepsy, RBD, PLM, NFLE, and insomnia subjects, respectively while categorizing A subphases (A1, A2, A3). The proposed method may help sleep experts to examine a person's sleep quality automatically using the microstructure of sleep.INDEX TERMS Cyclic alternating patterns (CAP), machine learning, electroencephalogram (EEG), phase A and phase B detection, k-nearest neighbor (kNN), sleep disorders.The associate editor coordinating the review of this manuscript and approving it for publication was Paolo Crippa .

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Section: B Orthogonal Filter Bank and Wavelet Decompositionmentioning

confidence: 99%

Automated Explainable Detection of Cyclic Alternating Pattern (CAP) Phases and Sub-Phases Using Wavelet-Based Single-Channel EEG Signals

et al. 2023

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“…This study is accomplished using the public CAP sleep database, which contains nightlong sleep polysomnographic (PSG) recordings of 108 subjects logged at the Sleep Disorders Center of the Ospedale Maggiore of Parma, Italy. The database contains recordings of 16 healthy subjects and patients suffering from insomnia (9), narcolepsy (5), nocturnal frontal lobe epilepsy (40), periodic leg movement (10), REM behavious disorder (22), and sleep-disordered breathing (4). Each PSG recording contains multiple channels including EEG, electrocardiogram (ECG), electromyogram (EMG), electrooculogram (EOG), SpO2 and respiratory signals.…”

Section: Materials Usedmentioning

confidence: 99%

“…Among these filter banks, the orthogonal filter bank is remarkable due to its advantage of energy preservation [37][38][39]. In this study, we have used the orthogonal wavelet filter with minimum root-mean-square (RMS) bandwidth [40][41][42]. Generally, equiripple filter method and band-energy minimization method are used to achieve sharper roll-off.…”

Section: Orthogonal Filter Bank and Wavelet Decompositionmentioning

confidence: 99%

“…On the other hand, RMS bandwith simultaneously considers both transition band and pass/stopband, comprehensively describing a filter's frequency localization and avoiding influence of ripple amplitude specifications [43,44]. In this method, for the optimization of RMS bandwidth, either a symbolic method can directly applied [40] or constrained optimization problem that is transformable into a convex optimization problem like semidefinite programming (SDP) problem [45] can be used. The SDP includes non-negativity constraints during the optimization.…”

Section: Orthogonal Filter Bank and Wavelet Decompositionmentioning

confidence: 99%

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Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals

et al. 2021

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Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.

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Design of Orthogonal Wavelet Filters with Minimum RMS Bandwidth Using A Symbolic Approach

Cited by 2 publications

References 7 publications

Automated Explainable Detection of Cyclic Alternating Pattern (CAP) Phases and Sub-Phases Using Wavelet-Based Single-Channel EEG Signals

Automated Explainable Detection of Cyclic Alternating Pattern (CAP) Phases and Sub-Phases Using Wavelet-Based Single-Channel EEG Signals

Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals

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