Automatic Apnea Identification by Transformation of the Cepstral Domain

Travieso, Carlos M.; Alonso, Jesús B.; Pozo-Baños, Marcos del; Ticay-Rivas, Jaime R.; López-de-Ipiña, Karmele

doi:10.1007/s12559-012-9184-x

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…To detect OSA from RR-interval signals, various machine learning methods are devised, including support vector machines (SVM) [14], k-nearest neighbors (KNN) algorithm [16], linear discriminant analysis (LDA) [17], hidden Markov model (HMM) [18]. With the immense advances in deep learning [19], methods based on deep neural networks [12] and recurrent neural networks (RNNs) [20] are proven effective in this task.…”

mentioning

confidence: 99%

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

Yin

Tong

et al. 2021

IEEE J. Biomed. Health Inform.

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Streszczenie-Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the costly treatment for a single detection. Recently, many single-sensor alternatives have been proposed to improve the cost efficiency and convenience. Among these methods, solutions based on RR-interval (i.e., the interval between two consecutive pulses) signals reach a satisfactory balance among comfort, portability and detection accuracy.In this paper, we advance RR-interval based OSA detection by considering its real-world practicality from energy perspectives. As photoplethysmogram (PPG) pulse sensors are commonly equipped on smart wrist-worn wearable devices (e.g., smart watches and wristbands), the energy efficiency of the detection model is crucial to fully support an overnight observation on patients. This creates challenges as the PPG sensors are unable to keep collecting continuous signals due to the limited battery capacity on smart wrist-worn devices. Therefore, we propose a novel Frequency Extraction Network (FENet), which can extract features from different frequency bands of the input RRinterval signals and generate continuous detection results with downsampled, discontinuous RR-interval signals. With the help of the one-to-multiple structure, FENet requires only one-third of the operation time of the PPG sensor, thus sharply cutting down the energy consumption and enabling overnight diagnosis. Experimental results on real OSA datasets reveal the state-ofthe-art performance of FENet.

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confidence: 99%

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

Yin

Tong

et al. 2021

IEEE J. Biomed. Health Inform.

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“…The table is categorised into four sections, related to different approaches analysed. By analysing algorithms, the highest accuracy was reported by [14] based on oximetry, [18] and [21] using respiration analysis, [35] based on ECG and [40] with combined approaches. Maximum sensitivity was reported by [12] and [14] using oximetry analysis and by [29] using analysis of ECG signals.…”

Section: Resultsmentioning

confidence: 99%

“…Signals such as RR and EDR (ECG derived respiration) are decomposed by [34] using 14-levels of Daubechies wavelet and fed into SVM to classify OSA events. [35] Used coefficients of RR series with SVM classifier and Hidden Markov Model. [36] Used three stages to classify apnea data.…”

Section: Based On Ecgmentioning

confidence: 99%

Developmental Strategies in Diagnosing Obstructive Sleep Apnea

Santhiya,

Ravichandran,

Yamuna

et al. 2020

IJEAT

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A key mission in medical science is diagnosing a disease due to its criticality and accuracy in examining whether a patient is suffering from particular disease or not. Then, the most appropriate side of treatment can be decided. Obstructive Sleep Apnea (OSA) syndrome is the most widespread sleep disorder characterized by chronic episodes of reduction in the airflow or stoppage in airflow during sleep, being caused by blockage of upper airway. The intention of this review is to analyze already existing algorithms for detecting apnea all the way through usage of different sensors that have not been implemented on hardware. This study offers an exhaustive literature research value from 2003 to 2019 and setting a roadmap for bio-engineers and medical doctors thereby reducing research period and improving medical service efficiency concerning obstructive sleep apnea diagnosis.

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“…First, the noisy points were manually noted and then removed. Then, to remove the automatic noise of signals, the urban electricity and baseline deviation noises were filtered using Chebyshev Type II and Butterworth band-pass [24].…”

Section: B Preprocessingmentioning

confidence: 99%

Designing an Intelligent System for Diagnosing Type of Sleep Apnea and Determining Its Severity

2021

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Introduction: Sleep apnea syndrome can be considered as one of the most serious risk factors of sleep disorder. Due to the lack of information about this disease, many causes of unexpected deaths have been identified. With increasing the number of patients with this disease around the world, many patients suffer apnea complications. Most of them are not treated because of the complex and costly and time-consuming polysomnography (PSG) diagnostic procedure.Material and Methods: This descriptive-analytical study was performed on 50 patients referred to sleep clinic of Imam Khomeini Hospital in Tehran, Attempts to design, and develop a system for detection of sleep apnea and its severity using ECG signals, RR intervals and airflow. The random forest algorithm and MATLAB2016 were used in the design of the system that the algorithm inputs are extracted 8 features nonlinear in time-frequency domain from airflow and ECG signals and 10 nonlinear features of RR intervals.Results: The accuracy for normal, obstructive, central and mixed apnea was obtained at 95.3%, 97.92%, 99.60%, and 97.29%, respectively, and the accuracy For detection of normal, mild, moderate and severe apnea was obtained 96%, 94%, 94%, 96% respectively. According to the results, the proposed system can correctly classify the types of sleep apnea and its severity.Conclusion: The proposed system, which has high performance capability in addition to increasing the physician speed and accuracy in the diagnosis of apnea can be used in home systems and the areas where healthcare facilities are not sufficient.

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Automatic Apnea Identification by Transformation of the Cepstral Domain

Cited by 8 publications

References 13 publications

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

Developmental Strategies in Diagnosing Obstructive Sleep Apnea

Designing an Intelligent System for Diagnosing Type of Sleep Apnea and Determining Its Severity

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