Sensor-Driven Achieving of Smart Living: A Review

Leelaarporn, Pitshaporn; Wachiraphan, Patcharapol; Kaewlee, Thitikorn; Udsa, Tinnakit; Chaisaen, Rattanaphon; Choksatchawathi, Tanut; Laosirirat, Rawipreeya; Lakhan, Payongkit; Natnithikarat, Phantharach; Thanontip, Kamonwan; Chen, Wei; Mukhopadhyay, Subhas Chandra; Wilaiprasitporn, Theerawit

doi:10.1109/jsen.2021.3059304

Cited by 41 publications

(17 citation statements)

References 168 publications

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“…This has resulted in the emergence of geriatric healthcare homes, with round-the--the-clock staff support, albeit with high costs of maintenance. Technological advances in sensors, low power embedded systems, and machine learning have paved the way for more affordable and intelligent healthcare homes, with automatic monitoring of the subjects’ vital parameters [ 14 ]. One of the possibilities of such a system is the detection of sleep apnea.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Ramachandran

Karuppiah

2021

Healthcare

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Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.

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Section: Introductionmentioning

confidence: 99%

“…Leelaarporn et al [ 14 ] provide a comprehensive review of the utilization of sensors in four different areas of smart living, including sleep monitoring. Recent research trends in the area of sleep monitoring using several types of algorithms on pulse oximetry, ECG, sounds and respiration data are described in [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Ramachandran

Karuppiah

2021

Healthcare

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“…Second, it is an obtrusive approach, which requires the attachment of several sensors and wires. Sleep normality can be disrupted by the sensors and wires, making PSG inappropriate for long-term sleep researches [ 7 ]. So, developing methods that can accurately detect apnea with a few signals at home is critical.…”

Section: Introductionmentioning

confidence: 99%

“…When sleep apnea occurs, the oxygen saturation decreases and the cardiovascular system is activated to maintain the oxygen intake constant. Furthermore, according to a clinical study in [ 7 ], patients' compliance is extremely low when wearing the pressure transducer sensor to achieve nasal and oral respiration. Patients usually pulled out the nasal cannula and nasal airflow data can be unreliable compared to the ECG signal due to lead loss.…”

Section: Introductionmentioning

confidence: 99%

ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome

Faal

Almasganj

2021

Journal of Healthcare Engineering

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This study presents and evaluates the mathematical model to estimate the mean and variance of single-lead ECG signals in sleep apnea syndrome. Our objective is to use the volatility property of the ECG signal for modeling. ECG signal is a stochastic signal whose mean and variance are time-varying. So, we propose to decompose this nonstationarity into two additive components; a homoscedastic Autoregressive Integrated Moving Average (ARIMA) and a heteroscedastic time series in terms of Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH), where the former captures the linearity property and the latter the nonlinear characteristics of the ECG signal. First, ECG signals are segmented into one-minute segments. The heteroskedasticity property is then examined through various tests such as the ARCH/GARCH test, kurtosis, skewness, and histograms. Next, the ARIMA model is applied to signals as a linear model and EGARCH as a nonlinear model. The appropriate orders of models are estimated by using the Bayesian Information Criterion (BIC). We assess the effectiveness of our model in terms of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The data in this article is obtained from the Physionet Apnea-ECG database. Results show that the ARIMA-EGARCH model performs better than other models for modeling both apneic and normal ECG signals in sleep apnea syndrome.

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“…More convenient alternatives for assessing sleep apnea have been investigated [5,6], such as using only pulse oximetry [7,8]. Furthermore, recent papers have shown that radar technology could accurately assess sleep apnea without any sensors attached to the patient [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Pilot Study of Detecting Individual Sleep Apnea Events Using Noncontact Radar Technology, Pulse Oximetry, and Machine Learning

Toften¹,

Kjellstadli²,

Tyvold³

et al. 2021

Journal of Sensors

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The gold standard for assessing sleep apnea, polysomnography, is resource intensive and inconvenient. Thus, several simpler alternatives have been proposed. However, validations of these alternatives have focused primarily on estimating the apnea-hypopnea index (apnea events per hour of sleep), which means information, clearly important from a physiological point of view such as apnea type, apnea duration, and temporal distribution of events, is lost. The purpose of the present study was to investigate if this information could also be provided with the combination of radar technology and pulse oximetry by classifying sleep apnea events on a second-by-second basis. Fourteen patients referred to home sleep apnea testing by their medical doctor were enrolled in the study (6 controls and 8 patients with sleep apnea; 4 mild, 2 moderate, and 2 severe) and monitored by Somnofy (radar-based sleep monitor) in parallel with respiratory polygraphy. A neural network was trained on data from Somnofy and pulse oximetry against the polygraphy scorings using leave-one-subject-out cross-validation. Cohen’s kappa for second-by-second classifications of no event/event was 0.81, or almost perfect agreement. For classifying no event/hypopnea/apnea and no event/hypopnea/obstructive apnea/central apnea/mixed apnea, Cohen’s kappa was 0.43 (moderate agreement) and 0.36 (fair agreement), respectively. The Bland-Altman 95% limits of agreement for the respiratory event index (apnea events per hour of recording) were -8.25 and 7.47, and all participants were correctly classified in terms of sleep apnea severity. Furthermore, the results showed that the combination of radar and pulse oximetry could be more accurate than the two technologies separately. Overall, the results indicate that radar technology and pulse oximetry could reliably provide information on a second-by-second basis for no event/event which could be valuable for management of sleep apnea. To be clinically useful, a larger study is necessary to validate the algorithm on a general population.

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Sensor-Driven Achieving of Smart Living: A Review

Cited by 41 publications

References 168 publications

A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome

A Pilot Study of Detecting Individual Sleep Apnea Events Using Noncontact Radar Technology, Pulse Oximetry, and Machine Learning

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