Automatic detection of sleep-disordered breathing from a single-channel airflow record

Nakano, Hiroshi; Tanigawa, Takeshi; Furukawa, Tomokazu; Nishima, Sankei

doi:10.1183/09031936.00091206

Cited by 81 publications

(73 citation statements)

References 27 publications

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“…Various features extracted from respiration signals suitable for detection of sleep apnea by using non-linear [14], time-frequency [15], time [16] and frequency domain [17] algorithms have already been studied. These algorithms can detect episodes of apnea at 80-95% precision.…”

Section: Introductionmentioning

confidence: 99%

Sleep apnea classification based on respiration signals by using ensemble methods

Avci

Akbaş

2015

BME

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Abstract. In this study, an efficient and robust method classifying the minute based occurrence of sleep apnea is aimed. Three respiration signals obtained from abdominal, chest and nasal way extracted from polysomnography recordings. Wavelet transform based on feature extraction methods are applied on the 1 minute length respiration signals. Dimension reduction process is facilitated by using principal component analysis. The features obtained from 8 recordings are used for the classification sleep apnea by using three ensemble classifiers. According to the results, the classification accuracies have been obtained between 92.07-98.43%, 92.75-98.68% and 92.42-98.61% by using three different ensemble classifier based on abdominal, chest and nasal based analysis, respectively for AdaBoost, Random Forest and Random Subspace. However the best result is obtained analyzing nasal based respiratory signal by using Random Forest method. In this case accuracy is 98.68%.

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Section: Introductionmentioning

confidence: 99%

Sleep apnea classification based on respiration signals by using ensemble methods

Avci

Akbaş

2015

BME

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“…In addition, automated signal processing techniques have been widely applied during the last years to improve the diagnostic ability of single-channel cardiorespiratory recordings. In this regard, single-lead ECG [27,28], single-channel AF [29,30,31] and blood oxygen saturation (SpO2) from nocturnal pulse oximetry [25,32,33] have been predominantly studied. In order to standardize portable monitoring assessment and help 7 physicians to uniformly classify and properly evaluate sleep testing devices, different studies have been carried out during the last years [24,26,34].…”

Section: Osas Diagnosismentioning

confidence: 99%

Positive airway pressure and electrical stimulation methods for obstructive sleep apnea treatment: a patent review (2005 – 2014)

Álvarez

Gutiérrez‐Tobal

Campo

et al. 2015

Expert Opinion on Therapeutic Patents

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Introduction. Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a major health problem with significant negative effects on the health and quality of life. Continuous positive airway pressure (CPAP) is currently the primary treatment option and it is considered the most effective therapy for OSAHS. Nevertheless, comfort issues due to improper fit to patient's changing needs and breathing gas leakage limit the patient's adherence to treatment.

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“…Recent studies on apnea detection in adults have utilized frequency energy calculated by fast Fourier transformation (FFT) 17) or waveform shape 18) . Fast…”

Section: Cheyne-stokes Respiration (Csr) Is An Abnormalmentioning

confidence: 99%

[Paper] Cheyne-Stokes Respiration Detection Method for Newborns with Apnea

Niimi

Itoh

Natori

et al. 2013

MTA

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278 IntroductionIn recent years, the rate of low birth weight has been increasing. Low birth weight (under 2500 g) may be caused by preterm delivery or agenesis in utero. The mortality rate of low birth weight babies is high because their respiratory and other body functions are immature.Prevalent respiratory diseases in preterm babies include neonatal respiratory distress syndrome, episodes of apnea, and sudden infant death syndrome (SIDS). The incidence of these diseases increases with lower gestational age. People with CSR do not respond quickly enough to changes in blood levels of oxygen and carbon dioxide.CSR has a high prevalence among newborns, especially among preterm newborns, whose central nervous systems are immature. CSR is triggered by hypoxia associated with hypoventilation 3) , because respiratory control is unstable when the central nervous Abstract Cheyne-Stokes respiration (CSR) has a high prevalence among newborns, especially among preterm babies. Although doctors generally recognize the phenomenon, they are not able to assess the severity of CSR in individual infants. CSR is characterized by cyclical weakening and strengthening of respirations with apnea. In this study, we developed an apnea detection method, and a CSR detection method using detected apneic events. We detected apnea using two features of respiratory waveforms. The first feature is frequency information calculated from wavelet coefficients. The second is information based on the shape of the waveform.In our CSR detection method, we used a spurious periodicity feature to determine CSR sections. The waveform is calculated by a respiratory monitoring system that uses a fiber-grating vision sensor to measure the vertical motion of the infant's thoracic and abdominal regions during respiration. Our method is effective at detecting apnea (sensitivity: 94.3 %, specificity: 99.7 %). To solve these problems, we developed a respiratory monitoring system for newborns using a fiber-grating (FG) vision sensor 7) . The FG vision sensor is an active stereo 3D sensor that allows noncontact 3D measurements 8) . This system can calculate more accurate respiratory waveforms than traditional medical sensors. Using this system, we developed a novel method to estimate the severity of CSR.Previous work using only the respiratory waveform shape or only frequency information is not sufficiently robust for apnea detection in newborns. Moreover, using a single feature or detection function results in large trade-offs between detection accuracy and threshold sensitivity.In this paper, we propose methods to detect apnea and CSR. Duration of apnea in newborns is shorter than in adults and neonatal respiratory waveforms tend to be distorted. To increase the robustness of detection accuracy, we adopted three different apnea detection functions using features of respiratory frequency and waveform shape information. The use of two different features improved detection accuracy. Our method also reduces false negatives by employing three different functions to ...

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Automatic detection of sleep-disordered breathing from a single-channel airflow record

Cited by 81 publications

References 27 publications

Sleep apnea classification based on respiration signals by using ensemble methods

Sleep apnea classification based on respiration signals by using ensemble methods

Positive airway pressure and electrical stimulation methods for obstructive sleep apnea treatment: a patent review (2005 – 2014)

[Paper] Cheyne-Stokes Respiration Detection Method for Newborns with Apnea

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