Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study

Mukherjee, Debadyuti; Dhar, Koustav; Schwenker, Friedhelm; Sarkar, Ram

doi:10.3390/s21165425

Cited by 16 publications

(15 citation statements)

References 50 publications

(81 reference statements)

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“…Tahani et al introduced Sugeno λ -measure technique for extraction of effective fuzzy measure. Mukherjee et al [ 107 ] used ensembled network method and Choquet integral-based fuzzy fusion technique for the identification of sleep apnea. The network combines the confidence values and all the possible combinations of data.…”

Section: Classificationmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

JeyaJothi

Anitha

Rani

et al. 2022

BioMed Research International

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Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night’s sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.

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Section: Classificationmentioning

confidence: 99%

“…Data augmentation is done to increase the number of training samples. The trainable ensemble network using MLP has achieved precision of 84.80, recall of 84.43%, F1 score of 84.67%, and specificity of 88.26% [ 107 ].…”

Section: Classificationmentioning

confidence: 99%

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

JeyaJothi

Anitha

Rani

et al. 2022

BioMed Research International

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show abstract

“…By contrast, Nasifoglu and Erogul [ 295 ] tested a fused CNN-ResNet approach for obstructive sleep apnea detection (accuracy 85.20%) and prediction (accuracy 82.30%) using data from a private database. Mukherjee et al [ 296 ] used a multilayer perceptron to detect sleep apnea from ECG recordings that originated from the Apnea-ECG Database, achieving an accuracy of 85.58%.…”

Section: Resultsmentioning

confidence: 99%

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Petmezas¹,

Stefanopoulos²,

Kilintzis³

et al. 2022

JMIR Med Inform

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Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

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“…Cheng et al [18], and Feng and Liu [19] [20] also use RR interval and QRS amplitude with several models, such as 1D-CNN, 1D-CNN+LSTM, and 1D-CNN+GRU, with the best accuracy results obtained from the 1D-CNN+LSTM model of 89.11%. Furthermore, Mukherjee et al [3] also perform manual feature extraction by taking three features, namely RRI, EDR, and RAMP, then using the ensemble learning method with the 1D-CNN model [20][21][22] with the best accuracy achieved is 85.58%. Apart from the RR interval, another RR amplitude feature that can be used to detect sleep apnea is the HRV.…”

Section: Related Workmentioning

confidence: 99%

“…Some examples of research conducted in detecting sleep apnea are supervised machine learning and deep learning methods. Mukherjee et al [3] used the deep learning ensemble model technique, which produces the highest accuracy of 85.58%. Then Banluesombatkul et al [4] tried to create a filter at the preprocessing stage.…”

Section: Introductionmentioning

confidence: 99%

Dealing with Imbalanced Sleep Apnea Data Using DCGAN

Wicaksono¹,

Philip²,

Alam³

et al. 2022

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Data in the health sector are often lacking and unbalanced. It is because collecting data takes time and many resources. One example is sleep apnea data which takes about 8–10 hours to get data and uses specialized hardware like polysomnography (PSG). This study proposes a data augmentation technique to handle unbalanced data using DCGAN and several deep learning models such as 1D-CNN, ANN, LSTM, and 1D-CNN+LSTM as a classifier for apnea detection. The DCGAN architecture used is CNN on the generator and discriminator. DCGAN will create new synthetic data by mimicking the original dataset. This experiment uses a dataset from PhysioNet, the Apnea-ECG, and the MIT-BIH PSG Database. Furthermore, the dataset is preprocessed to remove noise, and the features are extracted manually. The test scenario is to create 10% synthetic data and 50% sleep apnea data to be added to the original dataset. Then compare the performance of multiple deep learning models before and after adding data. The results indicate that augmentation with DCGAN can improve the performance of almost all models, with the highest increase of 1.78% on the 1D-CNN+LSTM model and 4.80% on the LSTM model for the Apnea-ECG and MIT-BIH datasets, respectively.

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Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study

Cited by 16 publications

References 50 publications

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Dealing with Imbalanced Sleep Apnea Data Using DCGAN

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