Diego Álvarez-Estévez

Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome

The sleep apnea/hypopnea syndrome is a very common sleep disorder, characterised by disrupted breathing during sleep. Depending on the extent of the disruptions to sleep, these are classified as apneas or hypopneas. In order to locate these apneic events an analysis of respiratory signals recorded for an entire nights sleep is necessary. However, identifying and classifying apneic events is a complex task, given the error associated with the process for digitising signals, variability in expert criteria and the complexity of the signals themselves. This article describes a fuzzy-logic-based automated system for detecting apneic events and classifying them as apneas or hypopneas. The aim is to equip this system with mechanisms for dealing with imprecision and reasoning affected by uncertainty. The ultimate goal was to assist the physician in diagnosing the sleep apnea/hypopnea syndrome. Results in terms of locating events in the polysomnogram showed sensitivity and specificity of 0.87 and 0.89, respectively. A receiver operating curve index of 0.88 was obtained for the classification of events as apneas or hypopneas.

A method for the automatic analysis of the sleep macrostructure in continuum

Sleep staging is one of the most important tasks within the context of sleep studies. For more than 40 years the gold standard to the characterization of patients sleep macrostructure has been based on set of rules proposed by Rechtschaffen and Kales and recently modified by the American Academy of Sleep Medicine. Nevertheless the resulting map of sleep, the so-called hypnogram, has several limitations such as its low temporal resolution and the unnatural characterization of sleep through the assignment of discrete sleep states. This study reports an automatic method for the characterization of the structure of the sleep. The main intention is to overcome limitations of epoch-based sleep staging by obtaining a more continuous evolution of the sleep of the patient. The method is based on the use of fuzzy inference in order to avoid binary decisions, provide soft transitions and enable concurrent characterization of the different states. It is proven, in addition, how the new proposed continuous representation can still be used to generate the classical epoch-based hypnogram.

Identification of Electroencephalographic Arousals in Multichannel Sleep Recordings

Electroencephalographic arousals are defined as abrupt shifts in electroencephalogram (EEG) frequency during sleep. Occurrence of arousals results in fragmented sleep, being one of the most important causes of daytime sleepiness among sleep disorders. Detection of arousals requires a polysomnographic (PSG) recording to be made during the patients sleep. The resulting PSG is then analyzed offline by the physician. This is a time-consuming task, hence, automation of this process is pursued. The analysis, which involves the correlation of various events in time occurring among the different channels, in conjunction with the complexity of the related biomedical signals, makes this task also difficult to achieve in the computer algorithm. In this paper, we present a method for the detection of EEG arousals working on multichannel PSGs. The algorithm detects arousals using the information available through two EEG channels and the electromyography. A signal-processing technique is first proposed for the analysis of biomedical signals and extraction of relevant information. Individual events are detected from the signals and subsequently are related in time. Finally, a classification phase carries out the final decision on the presence of the event. Classifiers based on Fishers linear and quadratic discriminants, support vector machines and artificial neural networks are compared at this phase. Experiments conducted on 20 patients reported a sensitivity and specificity respectively of 0.86 and 0.76 in the detection of the arousal events.

A comparison of performance of K-complex classification methods using feature selection

The main objective of this work is to obtain a method that achieves the best accuracy results with a low false positive rate in the classification of K-complexes, a kind of transient waveform found in the Electroencephalogram. With this in mind, the capabilities of several machine learning techniques were tried. The inputs for the models were a set of features based on amplitude and duration measurements obtained from waveforms to be classified. Among all the classifiers tested, the Support Vector Machine obtained the best results with an accuracy of 88.69%. Finally, to enhance the generalization capabilities of the classifiers, while at the same time discarding the existing irrelevant features, feature selection methods were employed. After this process, the classification performance was significantly improved. The best result was obtained applying a correlation-based filter, achieving a 91.40% of accuracy using only 36% of the total input features.

Reducing dimensionality in a database of sleep EEG arousals

Research highlights? Feature selection methods (filters and wrappers) are used to eliminate redundant features in a dataset for detection of EEG arousals. ? The objective is to identify the minimum possible subset of features while preserving good classification performance. ? Combination of various filter and wrapper methods through the union and the intersection of their respective selected features is additionally explored. ? Feature selection is adequate both drastically reducing the number of features and improving classification accuracy. ? The union of relevant features shows the best results among all tested methods. Sleep studies are carried out in order to diagnose those diseases associated with the sleep. The standard technique consists on monitoring various bio-physiological signals of the patient during sleep. The resulting recording, the polysomnography (PSG) is then analyzed offline by the physician. This supposes a very time-consuming task and therefore automation of these analyses is desirable. An arousal during sleep is defined as an abrupt shift in EEG frequency. Normal structure of sleep is altered by the presence of these events, thus being an important factor that influences on the quality of sleep. The use of computing assistance for the detection of these events on the PSG is aimed at reducing the cost of the PSG test, both in economical and human resources. In this work, a dataset containing PSGs of real patients was used for the detection of arousals in sleep. A total of 42 features were extracted from biosignals for the detection of these events. Our aim was to use different feature selection methods to eliminate the redundant features studying their influence on the identification of sleep arousals, checking whether classification could be improved. The objective is to reduce the number of features, identifying the subset of those with more relevance while preserving a good performance on the classifier. Two approximations are explored, wrappers and filters, using different methods of both, and also combinations of each of the methods by means of the union and the intersection of the relevant features obtained. The results showed that discarding the irrelevant features by these methods is feasible, reducing the dimensionality on the input space and also improving the accuracy of the classifiers.

Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review

Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of computer-assisted diagnosis of SAHS has been performed comprising the last 15 years of research in the field. Screening approaches, methods for the detection and classification of respiratory events, comprehensive diagnostic systems, and an outline of current commercial approaches are reviewed. An overview of the different methods is presented together with validation analysis and critical discussion of the current state of the art.

A continuous evaluation of the awake sleep state using fuzzy reasoning

Sleep staging is one of the most important tasks on the context of sleep studies. From more than 40 years the gold standard to the characterization of patient’s sleep is the use of the rules proposed by Rechtschaffen and Kales (R&K). However this method has the limitation of the unnatural assignment of discrete stages instead of doing it in a continuous manner. As part of a more general framework, this paper reports an automatic method for the characterization of the R&K’s awake sleep stage through the use of fuzzy reasoning. A continuous evolution of the wakefulness state of the patient during the night is provided as output.

Intelligent approach for analysis of respiratory signals and oxygen saturation in the sleep apnea/hypopnea syndrome.

This work deals with the development of an intelligent approach for clinical decision making in the diagnosis of the Sleep Apnea/Hypopnea Syndrome, SAHS, from the analysis of respiratory signals and oxygen saturation in arterial blood, SaO2. In order to accomplish the task the proposed approach makes use of different artificial intelligence techniques and reasoning processes being able to deal with imprecise data. These reasoning processes are based on fuzzy logic and on temporal analysis of the information. The developed approach also takes into account the possibility of artifacts in the monitored signals. Detection and characterization of signal artifacts allows detection of false positives. Identification of relevant diagnostic patterns and temporal correlation of events is performed through the implementation of temporal constraints.

Model Comparison for the Detection of EEG Arousals in Sleep Apnea Patients

Sleep Apnea/Hypopnea Syndrome (SAHS) is a very common sleep disorder characterized by the repeated occurrence of involuntary breathing pauses during sleep. Cessation in breathing often causes Electroencephalographic (EEG) arousals as a response, and therefore detection of arousals is important since they provide important evidence for localization of apneic events and their number is directly related with SAHS severity. Arousals result in fragmented sleep and so they are one of the most important causes of daytime sleepiness. In this paper we present an approach to detect these arousals over polysomnographic recordings based on the machine learning paradigm. First a signal processing technique is proposed for the construction of learning patterns. Subsequently classifiers based on Fishers linear and quadratic discriminates, Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are compared for the learning process. The more suitable model was chosen finally showing an accuracy of 0.92.

Spectral Heart Rate Variability analysis using the heart timing signal for the screening of the Sleep Apnea-Hypopnea Syndrome

Some approaches have been published in the past using Heart Rate Variability (HRV) spectral features for the screening of Sleep Apnea-Hypopnea Syndrome (SAHS) patients. However there is a big variability among these methods regarding the selection of the source signal and the specific spectral components relevant to the analysis. In this study we investigate the use of the Heart Timing (HT) as the source signal in comparison to the classical approaches of Heart Rate (HR) and Heart Period (HP). This signal has the theoretical advantage of being optimal under the Integral Pulse Frequency Modulation (IPFM) model assumption. Only spectral bands defined as standard for the study of HRV are considered, and for each method the so-called LF/HF and VLFn features are derived. A comparative statistical analysis between the different resulting methods is performed, and subject classification is investigated by means of ROC analysis and a Naïve-Bayes classifier. The standard Apnea-ECG database is used for validation purposes. Our results show statistical differences between SAHS patients and controls for all the derived features. In the subject classification task the best performance in the testing set was obtained using the LF/HF ratio derived from the HR signal (Area under ROC curve=0.88). Only slight differences are obtained due to the effect of changing the source signal. The impact of using the HT signal in this domain is therefore limited, and has not shown relevant differences with respect to the use of the classical approaches of HR or HP.