An intelligent system for the detection and interpretation of sleep apneas

Cabrero-Canosa, Mariano; Castro-Pereiro, M; Graña-Ramos, M; Hernández-Pereira, Elena; Moret‐Bonillo, Vicente; Martin-Egaña, M; Verea-Hernando, H

doi:10.1016/s0957-4174(02)00184-7

Cited by 58 publications

(20 citation statements)

References 13 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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“…For this reason, an event in oxygen saturation signal specified as start of fall or start of rise if [13]:…”

Section: B Oxygen Desaturationmentioning

confidence: 99%

“…The polysomnography recording need to be reviewed in order to detect events of SA, the manual review by experts is in fact so cost and time consuming, so several efforts has been done to develop systems that achieve this automatically [12][13][14]. For this reason several artificial intelligent algorithms are used in this area, like as fuzzy logic that applied to this problem [13,15,16].…”

Section: Introductionmentioning

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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“…Pandey and Mishra [1], [2] originated an intelligent model to diagnose the diseases based on ECG and EMG. Some analysts have described intelligent techniques for the diagnosis of sleep disorders [1], [2] and [3], but there is a lag to diagnose EEG based disease using physio-psycho factors including RBR and CBR. In this paper, an integrated method is proposed to diagnose sleep disorders by combining RBR and CBR.…”

Section: Introductionmentioning

confidence: 99%

Knowledge based System for the Diagnosis of Sleep Disorders

KumarGarg¹,

Bansal²

2015

IJCA

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Intelligent computing systems (ICS) and knowledge-based systems (KBS) have shown an imperative aspect in the detection and interpretation of electroencephalography (EEG) based sleep disorders that are associated by psychological and physiological factors like Sleep Apnea, Insomnia, Parasomnia and Snoring. Heuristic detection methods based on EEG parameters for sleep disorders have also been described in the paper, and a small attempt has been made to integrate rulebased reasoning (RBR) and case-based reasoning belongs to KBS. Integrated methods of RBR and CBR enhance the computation and logical competence of thought process involved to solve a problem. In this paper, an integrated model is developed in which RBR and CBR used for developing cases which are further used for detection and interpretation of sleep disorders. All these sleep disorders are framed into physio-psycho (muscular, cognitive and psychological) and EEG based parameters. The prime aim of this paper is to develop a combined model based on RBR and CBR in which RBR interacts with the sign and symptoms of the disorders, CBR is used for recognize the sleep disorders.

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An intelligent system for the detection and interpretation of sleep apneas

Cited by 58 publications

References 13 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

Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection

Knowledge based System for the Diagnosis of Sleep Disorders

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