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

Álvarez-Estévez, Diego; Moret‐Bonillo, Vicente

doi:10.1016/j.eswa.2008.11.043

Cited by 58 publications

(34 citation statements)

References 12 publications

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“…In the literature there are similar proposals that generate the same information. 2,4,5,22,23,32,33 Our algorithms are capable of identifying apneas, hypopneas, desaturations, thoracic and abdominal movement limitations and snoring in the polysomnogram. 25 For the study presented in this paper, they were only used to identify apneas, hypopneas and desaturations.…”

Section: Indexes Generationmentioning

confidence: 99%

An Evaluation of Indexes as Support Tools in the Diagnosis of Sleep Apnea

et al. 2012

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This article evaluates several indexes as support tools to diagnose patients with Sleep Apnea-Hypopnea Syndrome (SAHS). Some of these indexes, such as the Apnea-Hypopnea Index, have been standardized and studied in depth in the literature. Other indexes are used extensively in the reports that commercial polysomnographs generate. However, they have not been studied in detail and clinicians have no standardized guidelines for interpreting them. Examples are the mean and maximum duration of apneas and hypopneas. Finally, several novel indexes proposed by the authors are also evaluated. To evaluate the indexes, we have used a database of 274 patients who have undergone a polysomnographic test. Several feature selection techniques were used to assess the capability of each index to discriminate between healthy and SAHS patients. The capability of the indexes for diagnosing the patients was analyzed by using decision trees which were trained using each index individually, and all the indexes together. Our results suggest that some indexes which are often present in the reports of commercial polysomnographs provide little or no information. On the other hand, other indexes that are usually not considered have a great capability to discern between SAHS and control patients.

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Section: Indexes Generationmentioning

confidence: 99%

An Evaluation of Indexes as Support Tools in the Diagnosis of Sleep Apnea

et al. 2012

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“…The accuracy, sensitivity and specificity were 95.6%, 95.5% and 95.15 respectively. In [7], authors have presented a system for the detection of apneic events in sleep apnea/hypopnea. The sensitivity and specificity was 87% and 89% resp.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Fuzzy Inference Approach for the Diagnosis of Sleep Disorders

KumarGarg¹,

Bansal²

2015

IJCA

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Sleep plays a vital role in the life of human being and in convention of neuro-science. But some time, sleep is disrupted along with unusual behaviors associated with it. A numerous techniques and methods are adopted by many researchers for the diagnosis of disruptions to sleep along with the other sleep disorders and also for the diagnosis of unusual behavior linked with sleep that can also increase the sleep disruptions. In this paper, a fuzzy inference system (FIS) is developed for the diagnosis of sleep disorders like Sleep Apnea, Insomina, Parasomnia and Snoring. The dataset considered in this study is collected from various physicians. To construct the fuzzy inference system, three membership functions are used like low, medium and high. The range for all these membership functions is set according to their importance in the respective disease. A record of 140 patients is considered in this work. The accuracy achieved from the proposed system is 89.2%.

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“…In the next step signals are segmented to Reasoning Units (RUs) [16]. Each RU represents an interval which contains at least one event in airflow or oxygen saturation signals.…”

Section: B Oxygen Desaturationmentioning

confidence: 99%

“…5-Membership functions of each RU are computed regarding to the Table II and Table III [16]. where each input variable X takes a set of linguistic terms A as the value, whose members are joined by a disjunctive operator and the output variable remains a usual linguistic variable with a single label associated.…”

Section: Rus Algorithmmentioning

confidence: 99%

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Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection

Maali

Al-Jumaily

2012

IJMLC

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Abstract-Sleep Apnea (SA) is one of the most common and important part of sleep disorders. Unfortunately, sleep apnea may be going undiagnosed for years, because of the person's unawareness. The common diagnose procedure usually required an overnight sleep test. During the test, a recording of many biosignals, which related to breath, are obtained by polysomnography machine to detect this syndrome. The manual process for detecting the sleep Apnea by analysis the recording data is highly cost and time consuming. So, several works tried to develop systems that achieve this automatically. This paper proposes a genetic fuzzy approach for detecting Apnea/Hypopnea events by using Air flow, thoracic and abdominal respiratory movement signals and Oxygen desaturation as the inputs. Results show efficiently of this approach.

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

Cited by 58 publications

References 12 publications

An Evaluation of Indexes as Support Tools in the Diagnosis of Sleep Apnea

An Evaluation of Indexes as Support Tools in the Diagnosis of Sleep Apnea

A Fuzzy Inference Approach for the Diagnosis of Sleep Disorders

Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection

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