Portable diagnosis of sleep apnea with the validation of individual event detection

Saha, Shumit; Kabir, Muammar; Ghahjaverestan, Nasim Montazeri; Hafezi, Maziar; Gavrilovic, Bojan; Zhu, Kevin; Alshaer, Hisham; Yadollahi, Azadeh

doi:10.1016/j.sleep.2019.12.021

Cited by 21 publications

(21 citation statements)

References 34 publications

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“…It is important to validate any detection method through a temporal event-byevent evaluation. Additionally, previous studies have suggested that respiratory event duration is an important physiological biomarker of SA and can be used for better management of the pathophysiology of this disorder [40]. Here, the rule-based algorithm can provide information about individual event durations since it is based on the temporal information of events in signal sequences.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

Azimi

Bouchard

et al. 2020

IEEE Access

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Polysomnography (PSG) is the standard test for diagnosing sleep apnea. However, the approach is obtrusive, time-consuming, and with limited access for patients in need of sleep apnea diagnosis. In recent years, there have been many attempts to search for an alternative device or approach that avoids the limitations of PSG. Pressure-sensitive mats (PSM) have proven to be able to detect central sleep apneas (CSA) and be a potential alternative for PSG. In the current study, we combine advanced machine learning approaches with a practical unobtrusive home monitoring device (PSM) to detect CSA events from data collected nocturnally and unattended. Two deep learning methods are implemented for the automatic detection of CSA events: a temporal convolutional network (TCN) and a bidirectional long short-term memory (BiLSTM) network. The deep learning models are compared to a classical machine learning approach (linear support vector machine, SVM) and a simple threshold-based algorithm. Considering the characteristics of each method, we choose strategies, including resampling and weighted cost-functions, to optimize the methods and to perform CSA detection as anomaly detection in an imbalanced data set. We evaluate the performance of all models on a database containing 7 days of data from 9 elderly patients. From the resulting 63 days, data from 7 patients (49 days) are devoted to training for optimizing hyperparameters, and data from 2 patients (14 days) are devoted to testing. Experimental results indicate that the bestperforming model achieves an accuracy of 95.1% through training an BiLSTM network. Overall, the implemented deep learning methods achieve better performance than the conventional classification approach (SVM) and the simple threshold-based method, and show good potential for the use of PSM for practical unobtrusive monitoring of CSA. INDEX TERMS Biomedical measurement, data analysis, deep learning, machine learning, patient monitoring, pressure measurement, central sleep apnea detection.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

Azimi

Bouchard

et al. 2020

IEEE Access

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“…SA, characterized by repetitive breathing stops during sleep, is highly common, and it is a difficult disease to detect [4]. Sleep apnea is known to be a respiratory disease and approximately affecting 10% of the adult population [5]. Sleep-disordered breathing in elderly population is more prevalent, and it involves a longer event than young and middle-aged groups [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Portable Real-Time Low-Cost Sleep Apnea Monitoring System based on the Global System for Mobile Communications (GSM) Network

Sümbül

Yüzer

Şekeroğlu

2022

Med Biol Eng Comput

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Background and objective Continuous monitoring of breathing activity plays a vital role in the detection of respiratory-based diseases (SA, COPD, etc.). Sleep Apnea (SA) is characterized by recurrent upper airway obstruction during sleep associated with arterial blood desaturation, sympathetic nervous system activation, and cardiovascular impairment. Untreated patients with SA have increased mortality rates compared to the general population. This study aims to design a remote monitoring system for sleep apnea to ensure patient safety and ease the workload of doctors in the Covid-19 era. Methods This study aims to design a remote monitoring system for sleep apnea to ensure patient safety and ease the workload of doctors. Our study focuses on a novel portable real-time low-cost sleep apnea monitoring system utilizing the GSM network (GSM Shield Sim900a). Proposed system is a remote monitoring and patient tracking system to detect the apnea event in real time, and to provide information of the sleep position, pulse, and respiratory and oxygen saturation to the medical specialists (SpO 2 ) by establishing a direct contact. As soon as an abnormal condition is detected in the light of these parameters, the condition is reported (instant or in the form of short reports after sleep) to the patient relatives, the doctor's mobile telephone or to the emergency medical centers (EMCs) through a GSM network to handle the case depending on the patient's emergency condition. Results A study group was formed of six patients for monitoring apnea events (three males and three females) between the ages of 20 and 60. The patients in the study group have sleep apnea (SA) in different grades. All the apnea events were detected, and all the patients were successfully alerted. Also, the patient parameters were successfully sent to all patient relatives. Patients who could not get out of apnea were called through the CALL feature, and they were informed about their ongoing apnea event and told that intervention was necessary. The proposed system is tested on six patients. The beginning moment of apnea was successfully detected and the SMS/CALL feature was successfully activated without delay. During the testing, it has been observed that while some of the patients start breathing after the first SMS, some others needed the second or the third SMS. According to the measurement result, the maximum breathless time is 46 s among the patients, and a SMS is sent every 15 s. In addition, in cases where the patient was breathless for a long time, the CALL feature was actively sought from the relatives of the patient and enabled him to intervene. The proposed monitoring system could be used in both clinical and home settings. Conclusions The monitoring of a patient in real time allows to intervene in any unexpected circumstances about the patient. The proposed work uses an acceleration sensor as a reliable method of the sleep apnea for monitoring and prevention. The developed device is more economical, comfortable, and convenient than existing sy...

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“…Varon et al [13] introduced a method for the automatic detection of sleep apnea from single-lead electrocardiogram by training a least-squares support vector machines classifier on the features extracted from the electrocardiogram signal. Several studies estimated AHI and respiratory events from analyzing tracheal sound or tracheal movements, or the combination of tracheal sound with oxygen saturation [14][15][16][17][18]. Lévy et al [19] utilized pulse oximetry to quantify arterial oxygen saturation and to diagnose sleep apnea.…”

Section: Introductionmentioning

confidence: 99%

Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation

Akbarian¹,

Ghahjaverestan²,

Yadollahi³

et al. 2021

J Med Internet Res

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Background Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list. Objective The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea. Methods A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. Results The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83% accuracy and an F1-score of 86%. Conclusions This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app.

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Portable diagnosis of sleep apnea with the validation of individual event detection

Cited by 21 publications

References 34 publications

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

A Novel Portable Real-Time Low-Cost Sleep Apnea Monitoring System based on the Global System for Mobile Communications (GSM) Network

Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation

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