Sleep Apnea Hypopnea Syndrome classification in SpO&lt;inf&gt;2&lt;/inf&gt; signals using wavelet decomposition and phase space reconstruction

Morales, John; Varon, Carolina; Deviaene, Margot; Borzée, Pascal; Testelmans, Dries; Buyse, Bertien; Huffel, Sabine Van

doi:10.1109/bsn.2017.7936003

Cited by 18 publications

(14 citation statements)

References 12 publications

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“…This section presents experiment analysis of proposed motion artifact detection model performance using improved support vector machine classifier over exiting classifier model [4], [10], [33], [34], [35], and [36]. The system environment used for experiment analysis is Windows 10 enterprises edition, Intel Pentium I-7 class Quad core processor, 16 gigabits memory, and NVIDIA graphical processing unit that has CUDA compatibility.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…Further, experiment is carried out to detect people suffering from sleep apnoea. For experiment analysis, classification of apnoea and its severity, similar setup as in [33], [34] is considered. In [33] performed classification task of detecting people suffering from sleep hypopnea and sleep apnea condition.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…The outcome attained shows a positivity predicted (PP) and sensitivity outcomes of 63.4%and 87.5% for performing classification of hypopnea, 87.5% and 74.2% for performing classification of apnea, and 92.8% and 92.4% for hypopnea + apnea, respectively. In [34] showed that utilizing Apnea Hypopnea Index it is better to classify the severe nature of SAHS (Sleep Apnea-Hypopnea Syndrome) utilizing Polysomnography signal information. The results show an Accuracy, Specificity, Sensitivity of 88.61%, 64.29% 93.85% using Least Square Support Vector Machines (LS-SVM).…”

Section: Simulation Results and Analysismentioning

confidence: 99%

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An Efficient Motion and Noise Artifacts Removal Method using GAIT and Machine Learning Model

Pushpalatha*,

Math

2019

IJITEE

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obtaining an exact measurement of oxygen saturation (SpO2) using a finger-probe based pulse oximeter is dependent on both artifact-free infrared (IR) and red (R) Photoplethysmographic signals. However, in actual real-time environment condition, these Photoplethysmographic signals are corrupted due to presence of motion artifact (MA) signal that is produced due to the movement/motion from either hand or finger. To address this motion artifacts interference, the cause of the contamination of Photoplethysmographic signals by the motion artifacts signal is observed using GAIT. Motion and noise artifacts enforce constraints on the usability of the Photoplethysmographic, predominantly in the setting of sleep disorder detection and ambulatory monitoring. Motion and noise artifacts can alter Photoplethysmographic, resulting wrong approximation of physiological factors such as arterial oxygen saturation and heart rate. For overcoming issues and problems, this manuscript presented a new approach for detection of artifacts. First, present an adaptive filter and adaptive threshold model to detect artifact and obtain derivative of correlation coefficient (CC) for labelling artifacts, respectively. Lastly, Improved Support Vector Machine Model is presented to perform classification. Experiment are conducted on real-time dataset. Our approach attain significant performance in term of accuracy, sensitivity, specificity and positive prediction.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Simulation Results and Analysismentioning

confidence: 99%

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An Efficient Motion and Noise Artifacts Removal Method using GAIT and Machine Learning Model

Pushpalatha*,

Math

2019

IJITEE

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“…The same frequency band was also studied by Álvarez et al [27] to create features and a genetic algorithm was employed to select the optimal features (from a set of features that also included LZC, sample entropy and CTM) to fed a LR. A wavelet decomposition method, implemented using the Haar wavelet, was presented by Morales et al [30] using a k-nearest neighbor to perform the classification.…”

Section: Discussionmentioning

confidence: 99%

“…A better performance, regarding the global classification, was achieved by Jung et al [31] and Morales et al [30]. However, the developed algorithm uses a simple classifier (LR) and easy to implement features.…”

Section: Discussionmentioning

confidence: 99%

An Oximetry Based Wireless Device for Sleep Apnea Detection

Mendonça

Mostafa

Morgado‐Dias

et al. 2020

Sensors

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Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-intensive, and expensive. A non-invasive and easy to self-assemble home monitoring device was developed to address these issues. The device can perform the OSA diagnosis at the patient’s home and a specialized technician is not required to supervise the process. An automatic scoring algorithm was developed to examine the blood oxygen saturation signal for a minute-by-minute OSA assessment. It was performed by analyzing statistical and frequency-based features that were fed to a classifier. Afterward, the ratio of the number of minutes classified as OSA to the time in bed in minutes was compared with a threshold for the global (subject-based) OSA diagnosis. The average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for the minute-by-minute assessment were, respectively, 88%, 80%, 91%, and 0.86. The subject-based accuracy was 95%. The performance is in the same range as the best state of the art methods for the models based only on the blood oxygen saturation analysis. Therefore, the developed model has the potential to be employed in clinical analysis.

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Computer‐aided Diagnosis and Healthcare

2024

Biomedical Signal Analysis

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Sleep Apnea Hypopnea Syndrome classification in SpO<inf>2</inf> signals using wavelet decomposition and phase space reconstruction

Cited by 18 publications

References 12 publications

An Efficient Motion and Noise Artifacts Removal Method using GAIT and Machine Learning Model

An Efficient Motion and Noise Artifacts Removal Method using GAIT and Machine Learning Model

An Oximetry Based Wireless Device for Sleep Apnea Detection

Computer‐aided Diagnosis and Healthcare

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