Smartphone Based Respiratory Signal monitoring and Apnea detection Via Bluetooth Comunication

Patients with sleep apnea (sleep apnea) are increasing, almost more than 80% of people with this disorder are undiagnosed. Symptoms of sleep apnea are stopping breathing for more than 10 seconds. The purpose of this study was to design an apnea monitor device in order to detect symptoms of sleep apnea. The contribution in this study is a monitoring system or remote monitoring so that other people can monitor the patient's condition even though they are not accompanying him. In order to facilitate the process of monitoring and diagnosing patients, a Apnea Monitor Based on Bluetooth with Signal Display in Android with a delivery system via a bluetooth network that displays respiratory signals on Android so that patients can be treated quickly when breathing stops (apnea) . The design of this device uses a piezoelectric sensor to detect breathing which is placed on the patient's abdomen. The sensor output in the form of voltage is then conditioned on the PSA circuit. Using the ESP32 microcontroller as a signal processing which is formed by the PSA circuit and processed into a signal and respiration value. The respiration signal and value are then sent to the android device using the Bluetooth network. When a respiratory arrest is detected for more than 10 seconds, the device will turn on the indicator and buzzeer on the device and also send a warning to the Android or Roboremo application in the form of a notification "Apnea!" and a beep sound as a reminder when there is apnea in the patient so that the user can immediately take action on the patient. The test in this study there are 5 respondents who have been tested on this module by comparing the respiration rate per minute with the Patient Monitor, and the test results in this study obtained the measurement and calculation results, the lowest error value was 1.58% and the highest error value was 2.9%, the module can also transmit data well and without data loss with a distance of 10 meters in the room and 5 meters in different rooms. This module can be implemented in the patient monitoring process so that it can reduce sufferers of sleep apnea disorders. the module can also transmit data well and without data loss with a distance of 10 meters in the room and 5 meters in different rooms. This module can be implemented in the patient monitoring process so that it can reduce sufferers of sleep apnea disorders. the module can also transmit data well and without data loss with a distance of 10 meters in the room and 5 meters in different rooms. This module can be implemented in the patient monitoring process so that it can reduce sufferers of sleep apnea disorders.

Evaluating differences in respiratory motion estimates during radiotherapy: a single planning 4DMRI versus daily 4DMRI

Background In radiotherapy, respiratory-induced tumor motion is typically measured using a single four-dimensional computed tomography acquisition (4DCT). Irregular breathing leads to inaccurate motion estimates, potentially resulting in undertreatment of the tumor and unnecessary dose to healthy tissue. The aim of the research was to determine if a daily pre-treatment 4DMRI-strategy led to a significantly improved motion estimate compared to single planning 4DMRI (with or without outlier rejection). Methods 4DMRI data sets from 10 healthy volunteers were acquired. The first acquisition simulated a planning MRI, the respiratory motion estimate (constructed from the respiratory signal, i.e. the 1D navigator) was compared to the respiratory signal in the subsequent scans (simulating 5–29 treatment fractions). The same procedure was performed using the first acquisition of each day as an estimate for the subsequent acquisitions that day (2 per day, 4–20 per volunteer), simulating a daily MRI strategy. This was done for three outlier strategies: no outlier rejection (NoOR); excluding 5% of the respiratory signal whilst minimizing the range (Min95) and excluding the datapoints outside the mean end-inhalation and end-exhalation positions (MeanIE). Results The planning MRI median motion estimates were 27 mm for NoOR, 18 mm for Min95, and 13 mm for MeanIE. The daily MRI median motion estimates were 29 mm for NoOR, 19 mm for Min95 and 15 mm for MeanIE. The percentage of time outside the motion estimate were for the planning MRI: 2%, 10% and 32% for NoOR, Min95 and MeanIE respectively. These values were reduced with the daily MRI strategy: 0%, 6% and 17%. Applying Min95 accounted for a 30% decrease in motion estimate compared to NoOR. Conclusion A daily MRI improved the estimation of respiratory motion as compared to a single 4D (planning) MRI significantly. Combining the Min95 technique with a daily 4DMRI resulted in a decrease of inclusion time of 6% with a 30% decrease of motion. Outlier rejection alone on a planning MRI often led to underestimation of the movement and could potentially lead to an underdosage. Trial registration: protocol W15_373#16.007

Unconstrained Measurement of Heart Rate Considering Harmonics of Respiratory Signal Using Flexible Tactile Sensor Sheet

Measurement of the sleeping state is useful for monitoring the health of a person being nursed. The sleeping state can be estimated from biological information such as respiration rate, heart rate, body motion, and lying posture. A heart rate measurement method that considers the harmonics of a respiratory signal is described herein. The harmonics of respiratory signals for heart rate measurement has not been considered hitherto. An unconstrained method is proposed for measuring respiration, heart rate, and lying posture using a Smart Rubber sensor, which is a rubber-based flexible planar tactile sensor developed for this study. Respiration and heart rates are measured by applying frequency analysis to time-series data of body pressure. The harmonics of a respiratory signal serves as noise in heart rate measurement. Therefore, the heart rate measurement is improved by eliminating the effects of harmonics. The average frequency error of the heart rate measurement by our proposed method is 0.144 Hz. Experimental results show that our proposed method enhances the precision of heart rate measurement. Hence, this method enables the accurate measurement of the sleeping state.

Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.