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1

Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea

Kandukuri Usha Rani, Prakash Allam Jaya, Patro Kiran Kumar, Neelapu Bala Chakravarthy, Tadeusiewicz Ryszard, Pławiak Paweł

International Journal of Applied Mathematics and Computer Science

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2023

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Vol. 33, no. 3

493--506

EN

Obstructive sleep apnea (OSA) is a long-term sleep disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) is the technique for monitoring different signals during the patient’s sleep cycle, including electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and oxygen saturation (SpO2). Due to the high cost and inconvenience of polysomnography, the usefulness of ECG signals in detecting OSA is explored in this work, which proposes a two-dimensional convolutional neural network (2D-CNN) model for detecting OSA using ECG signals. A publicly available apnea ECG database from PhysioNet is used for experimentation. Further, a constant Q-transform (CQT) is applied for segmentation, filtering, and conversion of ECG beats into images. The proposed CNN model demonstrates an average accuracy, sensitivity and specificity of 91.34%, 90.68% and 90.70%, respectively. The findings obtained using the proposed approach are comparable to those of many other existing methods for automatic detection of OSA.

2

Detection of Obstructive Sleep Apnea from ECG Signal Using SVM Based Grid Search

Valavan K. K., Manoj S., Abishek S., Gokull Vijay T. G., Vojaswwin P., Rolant Gini J., Ramachandran K. I.

International Journal of Electronics and Telecommunications

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2021

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Vol. 67, No. 1

5--12

EN

Obstructive Sleep Apnea is one common form of sleep apnea and is now tested by means of a process called Polysomnography which is time-consuming, expensive and also requires a human observer throughout the study of the subject which makes it inconvenient and new detection techniques are now being developed to overcome these difficulties. Heart rate variability has proven to be related to sleep apnea episodes and thus the features from the ECG signal can be used in the detection of sleep apnea. The proposed detection technique uses Support Vector Machines using Grid search algorithm and the classifier is trained using features based on heart rate variability derived from the ECG signal. The developed system is tested using the dataset and the results show that this classification system can recognize the disorder with an accuracy rate of 89%. Further, the use of the grid search algorithm has made this system a reliable and an accurate means for the classification of sleep apnea and can serve as a basis for the future development of its screening.

3

Possibilities of diagnosing breathing disorders during sleep in home conditions

Górnicka Dorota, Kuryjański Ryszard, Michalik Michał

Diagnostyka

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2021

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Vol. 22, No. 4

115--121

EN

Disorders of breathing during sleep not only adversely affect the condition of the body during the daytime, but, above all, can be dangerous to health and life. Clinical methods of diagnosing these disorders are highly developed and, as a result, allow to effectively eliminate the problem, but still the problem is early diagnosis at home, which will be the basis for reporting to the doctor for extended examinations. This paper presents a proposed algorithm for inferring sleep-disordered breathing supported by conclusions from work on investigating the associations of discriminants with selected fragments of acoustic signals. The effectiveness of the developed algorithm was verified on a test sample of acoustic signals from selected patients treated by the MML clinic. The results of the study are the basis for the development of a numerical application for preclinical diagnosis of sleep apnea and sleep-disordered breathing. The verification of the algorithm carried out on real examples confirms the correctness of the assumptions made, demonstrates its effectiveness and suitability for use in a mobile application.

PL

Zaburzenia oddychania podczas snu nie tylko niekorzystnie wpływają na kondycję organizmu w porze dziennej, ale przede wszystkim mogą być niebezpieczne dla zdrowia i życia. Kliniczne metody diagnozowania tych zaburzeń są wysoko rozwinięte i w efekcie pozwalają na skuteczne wyeliminowanie problemu, jednak nadal problemem jest wczesna diagnostyka w warunkach domowych, która będzie podstawą do zgłoszenia się do lekarza w celu przeprowadzenia rozszerzonych badań. W artykule przedstawiono propozycję algorytmu wnioskowania o zaburzeniach oddychania w trakcie snu popartego wnioskami z prac nad badaniem związków wyróżników z wybranymi fragmentami sygnałów akustycznych. Skuteczność opracowanego algorytmu zweryfikowano na próbce testowej sygnałów akustycznych pochodzących od wybranych pacjentów leczonych przez klinikę MML. Rezultaty przeprowadzonych badań są podstawą opracowania aplikacji numerycznej służącej przedklinicznemu diagnozowaniu bezdechów śródsennych i zaburzeń oddychania podczas snu. Przeprowadzona na rzeczywistych przykładach weryfikacja algorytmu potwierdza poprawność przyjętych założeń, wykazuje jego skuteczność i przydatność do zastosowania w aplikacji mobilnej.

4

Application of intrinsic band function technique for automated detection of sleep apnea using HRV and EDR signals

Tripathy R. K.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 1

136--144

EN

Sleep apnea is the most common sleep disorder that causes respiratory, cardiac and brain diseases. The heart rate variability (HRV) and the electrocardiogram-derived respiration (EDR) signals to capture the cardio-respiratory information and the features extracted from these two signals have been used for the detection of sleep apnea. Detection of sleep apnea using the combination of HRV and EDR signals may provide more information. This paper proposes a novel method for the automated detection of sleep apnea based on the features extracted from HRV and EDR signals. The method involves the extraction of features from the intrinsic band functions (IBFs) of both EDR and HRV signals, and the classification using kernel extreme learning machine (KELM). The IBFs of HRV and EDR signals are evaluated using the Fourier decomposition method (FDM). The energy and the fuzzy entropy (FE) features are extracted from these IBFs. The kernel extreme learning machine (KELM) classifier with four kernel functions such as 'linear', 'polynomial', 'radial basis function (RBF)' and 'cosine wavelet kernel' is used for the automated detection of sleep apnea. The proposed technique yielded a sensitivity and a specificity of 78.02% and 74.64%, respectively using the public database. The method outperformed some of the reported works using HRV and EDR signals.

5

Urządzenia nasobne do monitorowania układu sercowo-naczyniowego

Przystup P., Bujnowski A.

Elektronika : konstrukcje, technologie, zastosowania

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2016

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Vol. 57, nr 9

85--88

PL

Choroby układu sercowo-naczyniowego są najczęstszą przyczyną zgonów na świecie. Rozszerzenie tradycyjnych metod diagnostyki i monitorowania pacjentów (EKG, Holter EKG) o dodatkowe biosygnały, oraz zastosowanie zaawansowanych metod analizy zarejestrowanych danych pozwoli na wczesną reakcję na wystąpienie epizodów zagrażających zdrowiu lub życiu pacjenta. W poniższej pracy zaprezentowano dwa urządzenia nasobne do monitorowania systemu sercowo-naczyniowego: SleAp oraz Pathmon. Umożliwiają one długotrwałą rejestrację szeregu biosygnałów, pozwalających na detekcję bezdechów, ocenę elektrycznej i mechanicznej aktywności serca, oraz zarejestrowanie czynności oddechowej.

EN

Cardiovascular system diseases are the most common cause of death. The extension of traditional diagnostic and patient monitoring methods (ECG, Holter ECG) by additional biosignals, and use of advanced analysis methods of recorded data will allow for early response to the occurrence of episodes endanger the health or life of the patient. The following paper presents two wearable devices to monitor the cardiovascular system SleAp and Pathmon. They allow long-term recording of the number of biosignals, enabling the detection of apnea and assessment of the electrical and mechanical activity of the heart, as well as respiration signal recording.

6

Computer-aided obstructive sleep apnea screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating

Hassan A. R., Haque M. A.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

256--266

EN

Automatic sleep apnea screening is important to alleviate the onus of the physicians of analyzing a large volume of data visually. Again, the push towards low-power, portable and wearable sleep quality monitoring systems necessitates the use of minimum number of recording channels to enhance battery life. So, there is a dire need of an automated apnea detection scheme based on single-lead electrocardiogram (ECG). Most of the existing works are based on multiple channels of physiological signals or yield poor performance. The effect of various classification models on algorithmic performance is also poorly explored. In the present work, we propose a statistical and spectral feature based sleep apnea identification scheme that utilizes single-lead ECG signals. Bootstrap aggregating is employed to perform classification. The efficacy of the selected features is demonstrated by intuitive, statistical and graphical analyses. Optimal choices of classifier parameters are also expounded. The performance of the proposed algorithm is evaluated for various classifiers. The performance of our method is also compared to that of the state-of-the-art ones. The proposed method yields accuracy, sensitivity and specificity of 85.97%, 84.14% and 86.83% respectively on a widely used benchmark data-set. Experimental findings backed by statistical and graphical analyses suggest that the proposed method performs better than the existing ones in terms of accuracy, sensitivity, specificity and computational cost.

7

A Detector of Sleep Disorders for Using at Home

Przystup P., Bujnowski A., Rumiński J., Wtorek J.

Journal of Telecommunications and Information Technology

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2014

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nr 2

70--78

EN

Obstructive sleep apnea usually requires all-night examination in a specialized clinic, under the supervision of a medical staff. Because of those requirements it is an expensive and a non-widely utilized test. Moving the examination procedure to patients’ home with automatic analysis algorithms involved will decrease the costs and make it available for larger group of patients. The developed device allows all-night recordings of the following biosignals: three channels ECG, thoracic impedance (respiration), snoring sounds and larynx vibrations. Additional information, like patient’s body position changes and electrodes’ attachment quality are estimated as well. The reproducible and high quality signals are obtained using the developed and unobtrusive device.

8

Rozproszone przenośne systemy diagnostyki medycznej

Kornacki W.

Elektronika : konstrukcje, technologie, zastosowania

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2004

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Vol. 45, nr 6

13-16

PL

Przedstawiono konstrukcje i zastosowania dwóch przenośnych rejestratorów medycznych. Jeden z nich służy do diagnostyki bezdechów sennych, drugi zaś to tremorometr do diagnostyki choroby Parkinsona.

EN

This paper presents the design and applications of two portable systems for medical diagnostics. One of them is used for the diagnosis of sleep apnea syndrome and the other one is used as a tremorometer for Parkinsons disease diagnostics.