A wireless wearable sleep monitoring system based on EEG signals is developed.The collected EEG signals are wirelessly sent to the PC or mobile phone Bluetooth APP for real-time display.The system is small in size,low in power consumption,and light in weight.It can be worn on the patient's forehead and is comfortable.It can be applied to home sleep monitoring scenarios and has good application value.The key performance indicators of the system are compared with the industry-related medical device measurement standards,and the measurement results are better than the special standards.

The concentration of end-tidal carbon dioxide is one of the important indicators for evaluating whether the human respiratory system is normal.Accurately detecting of end-tidal carbon dioxide is of great significance in clinical practice.With the continuous promotion of the localization of end-tidal carbon dioxide monitoring technology,its application in clinical practice in China has become increasingly widespread in recent years.The study is based on the non-dispersive infrared method and comprehensively elaborates on the detection principle,gas sampling methods,key technologies,and technological progress of end-tidal carbon dioxide detection technology.It comprehensively introduces the current development status of this technology and provides reference for application promotion and further improvement.

Pulse rate and blood oxygen levels are crucial physiological parameters that reflect physiological and pathological information within the human body.The system designs a wireless pulse wave monitoring system utilizing a flexible reflective probe and the AFE4490,which is capable of monitoring pulse wave and blood oxygen levels on the human forehead.The system is predominantly based on a reflective flexible probe,the AFE4490,a power supply module,a control microcontroller unit(MCU),and a Wi-Fi module.Post-processing by a slave computer,the collected pulse wave data is wirelessly transmitted to a smartphone.The real-time pulse waveform,pulse rate,and blood oxygen levels are displayed on an application.Following relevant tests and verifications,the system can accurately detect pulse wave signals,meet the requirements for wearable technology,and possesses significant market application potential.

ECG signals and sleep monitoring parameters complement each other and can be used for qualitative diagnosis of sleep apnea syndrome and cardio-related diseases.However,due to the limitations of the instrument volume and the detection environment,it is often challenging to integrate these two functions in practical applications.In this paper,a 12-lead dynamic electrocardiograph integrated with sleep monitoring is designed.The system's volume is reduced by combining the integrated ECG simulation front end with a miniature sensor.The system achieves the extraction,conditioning,and calculation of 12-lead ECG signals and sleep-related parameters and writes the data to a memory card in real time,which offers convenience for users and doctors in the diagnostic process.

Atrial fibrillation is a common arrhythmia, and its diagnosis is interfered by many factors. In order to achieve applicability in diagnosis and improve the level of automatic analysis of atrial fibrillation to the level of experts, the automatic detection of atrial fibrillation is very important. This study proposes an automatic detection algorithm for atrial fibrillation based on BP neural network (back propagation network) and support vector machine (SVM). The electrocardiogram (ECG) segments in the MIT-BIH atrial fibrillation database are divided into 10, 32, 64, and 128 heartbeats, respectively, and the Lorentz value, Shannon entropy, K-S test value and exponential moving average value are calculated. These four characteristic parameters are used as the input of SVM and BP neural network for classification and testing, and the label given by experts in the MIT-BIH atrial fibrillation database is used as the reference output. Among them, the use of atrial fibrillation in the MIT-BIH database, the first 18 cases of data are used as the training set, and the last 7 cases of data are used as the test set. The results show that the accuracy rate of 92% is obtained in the classification of 10 heartbeats, and the accuracy rate of 98% is obtained in the latter three categories. The sensitivity and specificity are both above 97.7%, which has certain applicability. Further validation and improvement in clinical ECG data will be done in next study.
Humans ; Atrial Fibrillation/diagnosis* ; Support Vector Machine ; Heart Rate ; Algorithms ; Neural Networks, Computer ; Electrocardiography

To comprehensively evaluate the human body's respiratory, circular metabolism and other functions, and to diagnose lung disease, an accurate and reliable pulmonary function test (PFT) is developed. The system is divided into two parts:hardware and software. It realizes the collection of respiratory, pulse oxygen, carbon dioxide, oxygen and other signals, and draws flow-volume curve (FV curve), volume-time curve (VT curve), respiratory waveform, pulse wave, carbon dioxide and oxygen waveform in real time on the upper computer of the PFT system, and conducts signal processing and parameter calculation for each signal. The experimental results prove that the system is safe and reliable, it can accurately measure the basic functions of human body, and provide reliable parameters, and has good application prospects.
Humans ; Carbon Dioxide ; Respiratory Function Tests ; Oxygen ; Heart Rate

Ventricular fibrillation is the most common pathophysiological mechanism leading to cardiac arrest. If cardiac arrest can be rescued in time, the survival rate of patients can be greatly improved. Therefore, rapid and accurate identification of ventricular fibrillation is extremely important. This paper proposes an automatic detection algorithm for ventricular fibrillation based on random forest and BP (back propagation) neural network. Pass the ECG signal through a 6 s moving window, calculate 6 kinds of characteristic parameters according to the time-frequency domain information of the signal, use these 6 kinds of characteristic parameters as the input of the classifier, carry out classification and test, and give the authoritative experts in the database. A total of 44 cases of related data were used to evaluate the method. The results show that using the ten-fold cross-validation method, the accuracy of classification of ventricular fibrillation in the CU database (Creighton University Ventricular Tachyarrhythmia Database) and the AHA database (the American Heart Association Database) has reached 96.38% and 99.45%, which has certain applicability.

Fetal ECG monitoring is a routine clinical detection method that can reflect the changes of fetal heart in utero in real time. At present, most of the clinical fetal heart rate detection adopts the ultrasonic Doppler method, which is technically difficult and highly specialized in operation and expensive. This study introduces a fetal ECG detection system based on the maternal abdominal electrode method. The weak fetal ECG changes are sensed through the maternal abdominal electrode, and the mixed ECG signal is obtained through the corresponding amplification and filtering circuit. Finally, the obtained signal is passed through WiFi, transmitted to the host computer. The host computer uses the adaptive filtering algorithm to estimate the fetal ECG signal. The system has strong feasibility, low operation expertise, low cost, and is more convenient.

Body temperature is an essential physiological parameter. Conducting non-contact, fast and accurate measurement of temperature is increasing important under the background of COVID-19. The study introduces an infrared temperature measurement system based on the thermopile infrared temperature sensor ZTP-135SR. Extracting original temperature date of sensor, post-amplification and filter processing have been performed to ensure accuracy of the system. In addition, the temperature data of environmental compensation which obtained by polynomial fitting is added to the system to further improve measurement accuracy.
Algorithms ; Body Temperature ; COVID-19 ; Humans ; Temperature ; Thermometers

Breathing is of great significance in the monitoring of patients with obstructive sleep apnea hypopnea syndrome, perioperative monitoring and intensive care. In this study, a respiratory monitoring and verification system based on optical capacitance product pulse wave (PPG) is designed, which can synchronously collect human PPG signals. Through algorithm processing, the characteristic parameters of PPG signal are calculated, and the respiratory signal and respiratory frequency can be extracted in real time. In order to verify the accuracy of extracting respiratory signal and respiratory rate by the algorithm, the system adds the nasal airflow respiratory signal acquisition module to synchronously collect the nasal airflow respiratory signal as the standard signal for comparison and verification. Finally, the root mean square error between the respiratory rate extracted by the algorithm from the pulse wave and the standard respiratory rate is only 1.05 times/min.
Algorithms ; Electrocardiography ; Heart Rate ; Humans ; Photoplethysmography ; Respiration ; Respiratory Rate ; Signal Processing, Computer-Assisted ; Sleep Apnea, Obstructive