Matrix of Lags: A tool for analysis of multiple dependent time series applied for CAP scoring

Mendonça, Fábio; Mostafa, Sheikh Shanawaz; Morgado‐Dias, Fernando; Ravelo-García, Antonio G.

doi:10.1016/j.cmpb.2020.105314

Cited by 9 publications

(7 citation statements)

References 52 publications

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“…On the other hand, a plethora of studies are available on identification of sleep macro-structures events including sleep stage scoring and identification of sleep disorders. Moreover, these handful of studies on CAP phase identification have used only healthy controls with few exceptions of Hartmann et al [26] and Mendonca et al [28], which have included either NFLE or SDB patients. In this proposed study, we have performed CAP phase identification using all 77 subjects comprising six types of sleep disordered patients having NFLE, SDB, narcolepsy, PLM, insomnia and RBD along with good sleepers.…”

Section: Discussionmentioning

confidence: 99%

“…They used Wigner-Ville distribution based feature extraction and support vector machine (SVM) classifier to achieve classification accuracy of 72.35%. Mendonca et al [28] have used time series analysis, Matrix of Lags and SVM classifier and obtained classification accuracy of 77% using ECG signals of 60 s duration. Recently, Loh et al [68] developed a deep neural network (1D-CNN) model for CAP phase classification and obtained an accuracy of 73.64%.…”

Section: Discussionmentioning

confidence: 99%

“…However, the CAP detection in human beings is prone to errors and a cumbersome task. In literature, few attempts have been made for identifying CAP phases automatically using computer aided systems [24][25][26][27][28]. However, the studies on the automated detection of sleep phasic events are very few.…”

Section: Amplitude( V)mentioning

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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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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“…This ratio was compared with a threshold to define if the minute corresponds to CAP or non-CAP, thus producing the label for the minuteby-minute CAP (CAPm) assessment used for the SQ-m estimation. This threshold was chosen to be 35% since it was indicated as the more suitable for CAP analysis based on the ECG signal [25] [27]. It considers the CAP periods that are longer than 21 s, filtering the short duration cycles (that may not significantly manifest in the ECG signal) but still covering the majority of the events since the average CAP cycle duration is 26.9 ± 4.1 s [42].…”

Section: A Databasesmentioning

confidence: 99%

“…The features were then ranked by a Minimum Redundance Maximum Relevance (mRMR) procedure and the more relevant were fed to the DSAE for classification. Mendonça et al [27] proposed a tool for time series analysis, named matrix of lags, that evaluated the connection between the Normal-to-Normal sinus interbeat intervals (N-N series) and the ECG Derived Respiration (EDR) by feeding the information regarding the energy of lags to a Support Vector Machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

A Method for Sleep Quality Analysis Based on CNN Ensemble With Implementation in a Portable Wireless Device

et al. 2020

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The quality of sleep can be affected by the occurrence of a sleep related disorder and, among these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable to a large group of the world population. To address these issues, the main goal of this work was to develop an automatic scoring algorithm to analyze the single-lead electrocardiogram signal, performing a minute-byminute and an overall estimation of both quality of sleep and obstructive sleep apnea. The method employs a cross-spectral coherence technique which produces a spectrographic image that fed three one-dimensional convolutional neural networks for the classification ensemble. The predicted quality of sleep was based on the electroencephalogram cyclic alternating pattern rate, a sleep stability metric. Two methods were developed to indirectly evaluate this metric, creating two sleep quality predictions that were combined with the sleep apnea diagnosis to achieve the final global sleep quality estimation. It was verified that the quality of sleep of the nineteen tested subjects was correctly identified by the proposed model, advocating the significance of clinical analysis. The model was implemented in a non-invasive and simple to self-assemble device, producing a tool that can estimate the quality of sleep and diagnose the obstructive sleep apnea at the patient's home without requiring the attendance of a specialized technician. Therefore, increasing the accessibility of the population to sleep analysis.

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Towards automatic EEG cyclic alternating pattern analysis: a systematic review

et al. 2023

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This study conducted a systematic review to determine the feasibility of automatic Cyclic Alternating Pattern (CAP) analysis. Specifically, this review followed the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to address the formulated research question: is automatic CAP analysis viable for clinical application? From the identified 1,280 articles, the review included 35 studies that proposed various methods for examining CAP, including the classification of A phase, their subtypes, or the CAP cycles. Three main trends were observed over time regarding A phase classification, starting with mathematical models or features classified with a tuned threshold, followed by using conventional machine learning models and, recently, deep learning models. Regarding the CAP cycle detection, it was observed that most studies employed a finite state machine to implement the CAP scoring rules, which depended on an initial A phase classifier, stressing the importance of developing suitable A phase detection models. The assessment of A-phase subtypes has proven challenging due to various approaches used in the state-of-the-art for their detection, ranging from multiclass models to creating a model for each subtype. The review provided a positive answer to the main research question, concluding that automatic CAP analysis can be reliably performed. The main recommended research agenda involves validating the proposed methodologies on larger datasets, including more subjects with sleep-related disorders, and providing the source code for independent confirmation.

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Matrix of Lags: A tool for analysis of multiple dependent time series applied for CAP scoring

Cited by 9 publications

References 52 publications

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

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

A Method for Sleep Quality Analysis Based on CNN Ensemble With Implementation in a Portable Wireless Device

Towards automatic EEG cyclic alternating pattern analysis: a systematic review

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