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عنوان انگلیسی مقاله:
A different sleep apnea classification system with neural network based on the acceleration signals
ترجمه فارسی عنوان مقاله:
یک سیستم طبقه بندی sleep apnea متفاوت با شبکه عصبی مبتنی بر سیگنال های شتاب
Sciencedirect - Elsevier - Applied Acoustics, 163 (2020) 107225. doi:10.1016/j.apacoust.2020.107225
Ahmet Hayrettin Yüzer a, Harun Sümbül b, Majid Nour c, Kemal Polat d,⇑
Background and objective: The apnea syndrome is characterized by an abnormal breath pause or reduction
in the airflow during sleep. It is reported in the literature that it affects 2% of middle-aged women
and 4% of middle-aged men, approximately. This study has vital importance, especially for the elderly,
the disabled, and pediatric sleep apnea patients.
Methods: In this study, a new diagnostic method is developed to detect the apnea event by using a microelectromechanical
system (MEMS) based acceleration sensor. It records the value of acceleration by measuring
the movements of the diaphragm in three axes during the respiratory. The measurements are
carried out simultaneously, a medical spirometer (Fukuda Sangyo), to test the validity of measurement
results. An artificial neural network model was designed to determine the apnea event. For the number
of neurons in the hidden layer, 1-3-5-10-18-20-25 values were tried, and the network with three hidden
neurons giving the most suitable result was selected. In the designed ANN, three layers were formed that
three neurons in the hidden layer, the two neurons at the input, and two neurons at the output layer.
Results: A study group was formed of 5 patients (having different characteristics (age, height, and body
weight)). The patients in the study group have sleep apnea (SA) in different grades. Several 12.723 acceleration
data (ACC) in the XYZ-axis from 5 different patients are recorded for apnea event training and
detection. The measured accelerometer (ACC) data from one of the patients (called H1) are used to train
an ANN. During the training phase, MSE is used to calculate the fitness value of the apnea event. Then
Apnea event is detected successfully for the other patients by using ANN trained only with H1’s ACC data.
Conclusions: The sleep apnea event detection system is presented by using ANN from directly acceleration
values. Measurements are performed by the MEMS-based accelerometer and Industrial
Spirometer simultaneously. A total of 12723 acceleration data is measured from 5 different patients.
The best result in 7000 iterations was reached (the number of iterations was tried up to 10.000 with
1000 steps). 605 data of only H1 measurements are used to train ANN, and then all data used to check
the performance of the ANN as well as H2, H3, H4, and H5 measurement results. MSE performance benchmark
shows us that trained ANN successfully detects apnea events. One of the contributions of this study
to literature is that only ACC data are used in the ANN training step. After training for one patient, the
ANN system can monitor the apnea event situation on-line for others.
Keywords: Sleep apnea | Acceleration sensor | Acceleration data | Artificial neural network | Medical decision making