an intelligent algorithm
Tran Trong Huu - Post-graduate Student, Department of Biotechnical Systems, Saint Petersburg Electrotechnical University «LETI»
E.V. Sadykova - Ph.D. (Eng.), Associate Professor, Department of Biotechnical Systems, Saint Petersburg Electrotechnical University «LETI»
Despite the relative diversity of existing units, which are used for the diagnosis and treatment of sleep apnea syndrome, the im-provement of the methods and software and hardware as a portable (used at home), and stationary, there is still an urgent task.
This can be explained by the fact that, for example, the portable device does not evaluate the most informative physiological pa-rameter of the patient, which leads to inaccuracies in the diagnostic tests. Stationary therapeutic devices which, in turn, analyze a large number of parameters that are uncomfortable for the patient and interferes during sleep, which reduces the accuracy of di-agnosis. In addition, studies conducted on stationary devices are highly labor intensive and require the presence of personnel during diagnostic procedures. Based on the foregoing, we can conclude that the development of hardware and software, combining in itself the positive qualities of both portable and fixed devices, whose tasks will include conducting qualitative removal and treatment of physiological parameters that are most informative in the diagnosis of sleep apnea syndrome (ECG signal 2 signal breathing efforts photoplethysmogram signal, blood oxygen saturation signal (the SpO2)), and which at the same time it will be convenient for both the patient and the doctor, it is an urgent task. In addition, a device will be capable of sounding the alarm in emergency - stop breathing during sleep. In this paper we describe the features and operation principle of the developed hardware and software system, the use of which will more accurately compared to peers to analyze in real time the presence of sleep apnea syndrome due to the use of a new algorithm for ECG signal analysis and reduce the risk of death during sleep in patients with this syndrome. Sensors remove the ECG signal, the signal 2 respiratory effort, photoplethysmogram signal, blood oxygen saturation signal (the SpO2) with a patient who is in a state of sleep. These signals are then transmitted to a signal processing device which filters and amplifies the received signals. Information captured signals using a bluetooth module that is integrated into the device, is transmitted to the smart phone, which, thanks to the built-in software (mobile app) is estimated the presence of sleep apnea syndrome in a patient. In addition, if an attack is detected apnea smartphone delivers a special sound to wake the patient (feedback). Then, the signaling information via the Internet on the smartphone is transmitted to Web-application with which the doctor operates remotely. The doctor analyzes the ECG signal parameters, calculated by the smartphone, and additional patient parameters obtained in the analysis of the other four signals, and then use the decision support tool finds the severity of sleep apnea syndrome in a patient. It is important to note that the Web-based application allows the doctor to process the data of several patients who use this device. The doctor will be able to monitor patient data and analyze them in real time, and to study the diagnostic information collected over a period of time, such as overnight.
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