Temat: apnea detection - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Comparing methods of ECG respiration signals derivation based on measuring the amplitude of QRS complexes
Autorzy:: Kikta, A.
Augustyniak, P.
Powiązania:: https://bibliotekanauki.pl/articles/333821.pdf
Data publikacji:: 2007
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: EKG
sygnał oddechowy
wykrywanie bezdechu
ECG
EDR
respiratory signal
apnea detection
Opis:: This paper presents the study of algorithms for derivation of respiration waveform from the electrocardiogram. The problem has considerable clinical impact, because the heart rate and respiration are both driven by the central nervous system, and commonly used low-cost Holter recording may be used for efficient detection of breath disturbances (e.g. apnea). Three methods based on: heart rate, heart position and lung resistance influencing the ECG amplitude were compared in our research. Among 18 volunteers breathing at a controlled frequency all implemented algorithms show acceptable sensitivity of order of 97% in slow breathing phases detection. In fast breathing the sensitivity is reduced to 90%, since the heart beats are too sparse with regard of respiration waveform.
Źródło:: Journal of Medical Informatics & Technologies; 2007, 11; 155-163
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Single-channel EEG processing for sleep apnea detection and differentiation
Autorzy:: Prucnal, Monika A.
Polak, Adam G.
Powiązania:: https://bibliotekanauki.pl/articles/27311744.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:: single-channel EEG
sleep apnea detection
optimization of signal processing
medical decision support
Opis:: Sleep apnea syndrome is a common sleep disorder. Detection of apnea and differentiation of its type: obstructive (OSA), central (CSA) or mixed is important in the context of treatment methods, however, it typically requires a great deal of technical and human resources. The aim of this research was to propose a quasi-optimal procedure for processing single-channel electroencephalograms (EEG) from overnight recordings, maximizing the accuracy of automatic apnea or hypopnea detection, as well as distinguishing between the OSA and CSA types. The proposed methodology consisted in processing the EEG signals divided into epochs, with the selection of the best methods at the stages of preprocessing, extraction and selection of features, and classification. Normal breathing was unmistakably distinguished from apnea by the k-nearest neighbors (kNN) and an artificial neural network (ANN), and with 99.98% accuracy by the support vector machine (SVM). The average accuracy of multinomial classification was: 82.29%, 83.26%, and 82.25% for the kNN, SVM and ANN, respectively. The sensitivity and precision of OSA and CSA detection ranged from 55 to 66%, and the misclassification cases concerned only the apnea type.
Źródło:: Metrology and Measurement Systems; 2023, 30, 2; 323--336
0860-8229
Pojawia się w:: Metrology and Measurement Systems
Dostawca treści:: Biblioteka Nauki

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