Entropy Analysis of Acoustic Signals Recorded With a Smartphone for Detecting Apneas and Hypopneas: A Comparison With a Commercial System for Home Sleep Apnea Diagnosis

Castillo-Escario, Yolanda; Ferrer-Lluis, Ignasi; Montserrat, Josep M.; Jané, Raimon

doi:10.1109/access.2019.2939749

Cited by 28 publications

(50 citation statements)

References 38 publications

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“…Contrarily, fSampEn can be directly applied to the data without the need for any prior calculations, while also being very stable to large outliers or artifacts. Although fSampEn has been previously used in acoustic breathing signals for apnea detection [16], its use as an estimator of LSI is unexplored. In this study, we have investigated the performance of fSampEn for LSI estimation in a wide variety of LS signals.…”

Section: Discussionmentioning

confidence: 99%

Performance Evaluation of Fixed Sample Entropy for Lung Sound Intensity Estimation

Lozano-García

Nuhić

Moxham

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Lung sound (LS) signals are often contaminated by impulsive artifacts that complicate the estimation of lung sound intensity (LSI) using conventional amplitude estimators. Fixed sample entropy (fSampEn) has proven to be robust to cardiac artifacts in myographic respiratory signals. Similarly, fSampEn is expected to be robust to artifacts in LS signals, thus providing accurate LSI estimates. However, the choice of fSampEn parameters depends on the application and fSampEn has not previously been applied to LS signals. This study aimed to perform an evaluation of the performance of the most relevant fSampEn parameters on LS signals, and to propose optimal fSampEn parameters for LSI estimation. Different combinations of fSampEn parameters were analyzed in LS signals recorded in a heterogeneous population of healthy subjects and chronic obstructive pulmonary disease patients during loaded breathing. The performance of fSampEn was assessed by means of its cross-covariance with flow signals, and optimal fSampEn parameters for LSI estimation were proposed.Clinical Relevance-LSI is considered to be an indirect measurement of the airflow entering the lungs, so that LSI can be used to detect airway obstruction and assess lung disorders that affect ventilation. The clinical potential of LSI analysis depends, however, on the ability to obtain accurate estimates of LSI. The fSampEn parameters proposed in this study allow fSampEn to be optimally applied to LS signals, thus contributing to improving LSI estimation in health and disease.

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Section: Discussionmentioning

confidence: 99%

Performance Evaluation of Fixed Sample Entropy for Lung Sound Intensity Estimation

Lozano-García

Nuhić

Moxham

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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“…The Apnealink device was also placed over the sternum, below the smartphone, based on that described in the Apnealink guidelines. This device placement configuration had already been tested successfully in previous studies by our group [30], [31], [34], [35].…”

Section: A Home Database Acquisition Protocolmentioning

confidence: 90%

“…Despite these good results, there are differences between the two methods, which can be explained by the limitations of each system. In a recent study conducted by our group, Castillo-Escario et al [30] demonstrated the potential of smartphone audio in the detection of oral and nasal breathing. These findings exposed a limitation of the Apnealink device, which assesses airflow only by means of a nasal canula.…”

Section: A Accelerometry: Sleep-disordered Breathing Assessmentmentioning

confidence: 99%

“…This data acquisition protocol was used to compile the database used in this paper to analyze sleep-disordered breathing and pOSA. The same database was used by Castillo-Escario et al [30] in a previous study by our group. The database is composed of 13 different subjects, eight men and five women, with an average age of 48 [24]-[83] and an average BMI of 27 [20]- [34], containing three healthy, three mild, four moderate and three severe OSA subjects as detected by the manually reviewed Apnealink events.…”

Section: A Home Database Acquisition Protocolmentioning

confidence: 99%

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Analysis of Smartphone Triaxial Accelerometry for Monitoring Sleep-Disordered Breathing and Sleep Position at Home

et al. 2020

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Obstructive sleep apnea (OSA) is a sleep disorder in which repetitive upper airway obstructive events occur during sleep. These events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. OSA is also known to be position-dependent in some patients, which is referred to as positional OSA (pOSA). Screening for pOSA is necessary in order to design more personalized and effective treatment strategies. In this article, we propose analyzing accelerometry signals, recorded with a smartphone, to detect and monitor OSA at home. Our objectives were to: (1) develop an algorithm for detecting thoracic movement associated with disordered breathing events; (2) compare the performance of smartphones as OSA monitoring tools with a type 3 portable sleep monitor; and (3) explore the feasibility of using smartphone accelerometry to retrieve reliable patient sleep position data and assess pOSA. Accelerometry signals were collected through simultaneous overnight acquisition using both devices with 13 subjects. The smartphone tool showed a high degree of concordance compared to the portable device and succeeded in estimating the apnea-hypopnea index (AHI) and classifying the severity level in most subjects. To assess the agreement between the two systems, an event-by-event comparison was performed, which found a sensitivity of 90% and a positive predictive value of 80%. It was also possible to identify pOSA by determining the ratio of events occurring in a specific position versus the time spent in that position during the night. These novel results suggest that smartphones are promising mHealth tools for OSA and pOSA monitoring at home.

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“…In recent years, scholars have proposed a variety of OSAHS disease discrimination techniques based on various symptom characteristics. Among them, A. Garde used the visual midpoint (radius and angle) distribution characteristics of SpO 2 signals to distinguish OSAHS symptoms [ 6 ]; Kim used the patient's breathing sound signal to develop a classification of OSAHS severity model [ 7 ]; Volak made preliminary judgments on OSAHS through image recognition of children's dental features [ 8 ]; Castillo-Escario et al develop an algorithm for detecting silence events and classifying them into apneas and hypopneas [ 9 ]. The current medical research reports show that the clinical apnea syndrome events manifestations of an adult are as follows [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Snore from OSAHS Patients Based on Deep Learning

Shen

Cheng

Zhu

et al. 2020

Journal of Healthcare Engineering

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Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively affecting the cardiovascular, kidney, and mental systems. Clinically, doctors usually use standard PSG (Polysomnography) to assist diagnosis. PSG determines whether a person has apnea syndrome with multidimensional data such as brain waves, heart rate, and blood oxygen saturation. In this paper, we have presented a method of recognizing OSAHS, which is convenient for patients to monitor themselves in daily life to avoid delayed treatment. Firstly, we theoretically analyzed the difference between the snoring sounds of normal people and OSAHS patients in the time and frequency domains. Secondly, the snoring sounds related to apnea events and the nonapnea related snoring sounds were classified by deep learning, and then, the severity of OSAHS symptoms had been recognized. In the algorithm proposed in this paper, the snoring data features are extracted through the three feature extraction methods, which are MFCC, LPCC, and LPMFCC. Moreover, we adopted CNN and LSTM for classification. The experimental results show that the MFCC feature extraction method and the LSTM model have the highest accuracy rate which was 87% when it is adopted for binary-classification of snoring data. Moreover, the AHI value of the patient can be obtained by the algorithm system which can determine the severity degree of OSAHS.

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Entropy Analysis of Acoustic Signals Recorded With a Smartphone for Detecting Apneas and Hypopneas: A Comparison With a Commercial System for Home Sleep Apnea Diagnosis

Cited by 28 publications

References 38 publications

Performance Evaluation of Fixed Sample Entropy for Lung Sound Intensity Estimation

Performance Evaluation of Fixed Sample Entropy for Lung Sound Intensity Estimation

Analysis of Smartphone Triaxial Accelerometry for Monitoring Sleep-Disordered Breathing and Sleep Position at Home

Detection of Snore from OSAHS Patients Based on Deep Learning

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