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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

End-to end decision support system for sleep apnea detection and Apnea-Hypopnea Index calculation using hybrid feature vector and Machine learning

Arslan Recep Sinan, Ulutas Hasan, Köksal Ahmet Sertol, Bakir Mehmet, Çiftçi Bülent

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 4

684--699

EN

Sleep apnea is a disease that occurs due to the decrease in oxygen saturation in the blood and directly affects people’s lives. Detection of sleep apnea is crucial for assessing sleep quality. It is also an important parameter in the diagnosis of various other diseases (diabetes, chronic kidney disease, depression, and cardiological diseases). Recent studies show that detection of sleep apnea can be done via signal processing, especially EEG and ECG signals. However, the detection accuracy needs to be improved. In this paper, a ML model is used for the detection of sleep apnea using 19 static sensor data and 2 dynamic data (Sleep score and Arousal). The sensor data is recorded as a discrete signal and the sleep process is divided into 4.8 M segments. In this work, 19 different sensor data sets were recorded with polysomnography (PSG). These data sets have been used to perform sleep scoring. Then, arousal status marking is done. Model training was carried out with the feature vector consisting of 21 data obtained. Tests were performed with eight different machine learning techniques on a unique dataset consisting of 113 patients. After all, it was automatically determined whether people were diseased (a kind of apnea) or healthy. The proposed model had an average accuracy of 97.27%, while the recall, precision, and f-score values were 99.18%, 95.32%, and 97.20%, respectively. After all, the model that less feature engineering, less complex classification model, higher dataset usage, and higher classification performance has been revealed.

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

Prosta metoda diagnozowania bezdechu sennego

Krawczyk M., Graffstein J.

Zeszyty Naukowe Politechniki Rzeszowskiej. Mechanika

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2007

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z. 71 [238]

431-348

PL

Patologia polegająca na okresowym bezdechu podczas snu, jest ciężką chorobą, powodującą przewlekłe niedotlenienie organizmu. Z przeprowadzonych badań wynika, że zjawisko ma charakter masowy - występuje u 6% populacji. Jej odległym, nie zawsze prawidłowo kojarzonym skutkiem, jest znaczny wzrost prawdopodobieństwa wystąpienia wielu chorób, np. układu sercowo-naczyniowego i nerwowego. Koncepcja prezentowanej metody diagnostycznej, weryfikowana w ramach projektu rozwojowego, zakłada wykonanie pośredniej oceny procesu oddychania na podstawie analizy stowarzyszonych ruchów klatki piersiowej i brzucha. Pomiar wykonywany jest za pomocą czujnika piezoelektrycznego. Kolejno zarejestrowany sygnał poddawany jest procesowi zaawansowanej analizy numerycznej, w celu wykrycia zespołu bezdechu sennego. W artykule przedstawiono i omówiono wstępne wyniki analizy testowych pomiarów klinicznych.

EN

Pathology consisting in occurance of apnea periods during a sleep is a serious illness resulting in protracted hypoxemia of the whole organism. Results of research prove the mass character of this phenomenon - about 6% of population suffers from this affections. Its far and sometimes not correctly diagnosed results are many serious diseases of heart & vascular or nervous systems. The diagnostic method presented here and developed within the grant project, is based on indirect estimation of breathing process depending on analysis of abdomen and chest movements. The measurement is realized by piezoelectric sensor. The recorded signal is then subjected to advanced numerical analysis aimed at apnea detection. Some preliminary results of test measurements carried on in hospital are presented and discussed in the article.

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.