Detection of airway obstruction and sleep apnea by analyzing the phase relation of respiration movement signals

Varady, P.; Bongár, Szabolcs

doi:10.1109/imtc.2001.928810

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…3-For each breathing cycle, define how the corresponding local extremum points of both respiration signals are related to each other by calculating T as the displacements between thoracic and abdominal extremum positions. In this study, three cases were considered [6]: Assigning the commonphase as C; out of phase as O and opposite phase as OP to each breathing cycle, the number of C , O and OP breathing cycles for each Normal ,OSA and Hypopnea events are calculated . It has to be noticed that in case of Central Apnea events, thoraco-abdominal signals are ceased.…”

Section: Bottom-up Algorithmmentioning

confidence: 99%

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K9. Detection of Sleep Apnea Events using analysis of thoraco-abdominal excursion signals and adaptive neuro-fuzzy inference system (ANFIS)

Abdel-Mageed

Chadi

Salah

et al. 2012

2012 29th National Radio Science Conference (NRSC)

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This paper describes an adaptive neuro-fuzzy inference system (ANFIS) for detection of Sleep Apnea Events using Thoracic and Abdominal Excursion signals. Mean amplitude sum analysis, and phase angle difference analysis using both Piecewise Linear Approximation (PLA) and phase difference measurements have been used to classify Normal, Obstructive Sleep Apnea (OSA), Hypopnea and Central Apnea events. A hybrid learning algorithm using a combination of Steepest Descent and Least Squares Estimation (LSE) was used to identify the parameters of ANFIS. The performance of ANFIS was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the Sleep Apnea events with an accuracy level of more than 95%.

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Section: Bottom-up Algorithmmentioning

confidence: 99%

“…Phase angle difference analysis has been found useful in assessing the degree of Thoraco-Abdominal asynchrony or phase opposition by calculating the phase shift between thoracic and abdominal excursion signals [6], [7].…”

Section: Introductionmentioning

confidence: 99%

K9. Detection of Sleep Apnea Events using analysis of thoraco-abdominal excursion signals and adaptive neuro-fuzzy inference system (ANFIS)

Abdel-Mageed

Chadi

Salah

et al. 2012

2012 29th National Radio Science Conference (NRSC)

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show abstract

“…5 Another study used the RR interval, QRS waves, and T waves from 35 ECGs as its research data, and employed a two-layer sensor framework in conjunction with the dynamic Markovian state model to classify SAS. 5,9,10 Furthermore, various physiological signals can be used for analysis and recognition of different diseases and physiological states as shown in Table 1. When sleep stage is in nonrapid eye movement (NREM), and sleep apnea occurs and eventually returns to normal breathing state, continuous signal with frequency relatively higher than Delta wave often appears in EEG.…”

Section: Literature Reviewmentioning

confidence: 99%

Sleep Apnea Syndrome Recognition Using the Greyart Network

Lee

Chen

Hsiao

et al. 2011

Biomed. Eng. Appl. Basis Commun.

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This study employs relational analysis and the GreyART network to identify and study the characteristics of electroencephalogram signals of sleep apnea syndrome (SAS). Seventeen raw electroencephalogram data records from the sleep database compiled by Massachusetts Institute of Technology (MIT) and Beth Israel Hospital (BIH) were used in conjunction with four wavelet decomposition steps to obtain the cD4 wavelet coefficient as input for the GreyART network. The GreyART network was then used for simulation training and testing in order to achieve the best recognition results. This study achieved an average recognition rate of 93.33% for electroencephalogram data record slp01b, and recognition rates during the training and testing stage for this record were 95.80% and 92.12%, respectively. This was the best recognition result for any of the 17 records. The overall average recognition rate for all 17 records was 78.10%. In comparison with past literature, this study's use of the GreyART network to recognize electroencephalogram signal characteristics of SAS possesses excellent reference value. To further reduce the costs and the physiological signal measurement items to make it easier for patients to use, this research also proposes to use a single type of physiological signals, electroencephalographic (EEG) signals, as the sole input as identification information to identify SAS diseases. EEG signal detection is utilized because it is nonintrusive, suitable for long-term monitoring and most importantly, it can be used to detect various types of abnormal physiological conditions in SAS, moods, sleep stages, heart rate abnormalities and mental states. In addition, constant monitoring of users in their familiar home environment can also be utilized as self-screening at home to reduce the burden of sleep medicine centers.

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“…As mentioned earlier, a lot of studies have been done to detect sleep apnea; however, only few have classified the types of condition. Varady et al diagnosed sleep apnea and its different types by using the phase difference between 2 signals received from thoracic and abdomen respiratory efforts. In a natural breath, air circulation in the abdomen and in the chest is simultaneous; therefore, they occur in the same phases.…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of sleep apnea using genetic algorithms and SVM‐based classification of thoracic respiratory effort and oximetric signal features

Abedi

Naghavi

Rezaeitalab

2017

Computational Intelligence

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Sleep apnea is a relatively prevalent breathing disorder characterized by temporary interruptions in airflow during sleep. There are 2 major types of sleep apnea. Obstructive sleep apnea occurs when air cannot flow through the upper airway despite efforts to breathe. Central sleep apnea occurs when the brain fails to signal to the muscles to maintain breathing. The standard diagnostic test is polysomnography, which is expensive and time consuming. The aim of this study was to design an automatic diagnostic and classifying algorithm for sleep apneas employing thoracic respiratory effort and oximetric signals. This algorithm was trained and tested applying a database of 54 subjects who had undergone polysomnography. A feature extraction stage was conducted to compute features. An optimal genetic algorithm was applied to select optimal features of these 2 kinds of signals. The classification technique was based on the support vector machine classifier to classify the selected features in 3 classes as healthy, obstructive, and central sleep apnea events. The results show that our automated classification algorithm can diagnose sleep apnea and its types with an average accuracy level of 90.2% (87.5‐95.8) in the test set and 90.9% in the validation set with high acceptable accuracy.

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Detection of airway obstruction and sleep apnea by analyzing the phase relation of respiration movement signals

Cited by 5 publications

References 3 publications

K9. Detection of Sleep Apnea Events using analysis of thoraco-abdominal excursion signals and adaptive neuro-fuzzy inference system (ANFIS)

K9. Detection of Sleep Apnea Events using analysis of thoraco-abdominal excursion signals and adaptive neuro-fuzzy inference system (ANFIS)

Sleep Apnea Syndrome Recognition Using the Greyart Network

Detection and classification of sleep apnea using genetic algorithms and SVM‐based classification of thoracic respiratory effort and oximetric signal features

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