Perimenopause and the Use of Fertility Tracking: 3 Case Studies

Perimenopause is a time in a woman's life where fertility may vary depending upon her age and her reproductive stage and has been defined as the transition period prior to menopause that is characterized by irregular menses, hormonal changes, vasomotor symptoms, and declining fertility (Casper, 2020). Fertility tracking during this time in a woman's reproductive stage has not been widely studied. Employing the use of Luteinizing Hormone Urine Assay sticks, an electronic hormonal monitor device or mucus, we propose a set of guidelines to determine the potentially fertile times of a woman's cycle based on staging according to the Stages of Reproductive Aging Workshop (STRAW) criteria and illustrate their application with three case reports.

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.

Vital Sign Monitor Device Equipped with a Telegram Notifications Based on Internet of Thing Platform

Vital Sign Monitor is a tool used to diagnose a patient who needs intensive care to know the condition of the patient. Parameters used in monitoring the patient's condition include body temperature and respiration. The contribution of this research designed a vital sign monitoring tool with IoT-based notifications so that remote monitoring can be done by utilizing web Thinger.io, LCD, RGB LEDs as a display of the results of the study and notify telegrams if it becomes abnormal to the patient's condition. Therefore, in order to produce accurate data in the process of data retrieval, a relaxed position of the patient is required and the stability of the wi-fi network so that monitoring is not hampered. The study used the DS18B20 digital temperature sensor placed on the axilla and the piezoelectric sensor placed on the abdomen of the patient. The results of the study were obtained by taking data on patients. The resulting temperature value will be compared to the thermometer, which produces the highest error value of 0.56%, which is still possible because the tolerance limit is 1oC. and for the collection of respiration values that have been compared to the patient monitor obtained the highest error value of 6.2%, which is still feasible because the tolerance limit is 10%. In this study, there is often a crash library between the temperature sensor and other sensors, so for further research, recommend to replacing the temperature sensor

Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation

Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task, a smart meter must be used for load forecasting, the reduction in consumer bills as well as the reduction in grid distortions. Smart meters can be used to disaggregate the energy consumption at the device level. In this paper, we investigated the potential of identifying the multimedia content played by a TV or monitor device using the central house’s smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture was based on the elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms, which use symmetric distance measures, were used with the best achieved video content identification accuracy of 93.6% using the MVM algorithm.

Computer visualization in breathing curve analysis

The aim. To study the features of the breathing process based on the breathing curve analysis in patients with various forms of bronchial asthma using computer visualization methods.Material and Methods. The experimental data comprised breathing curves registered with the MONITOR device in patients with various bronchial asthma types and a group of apparently healthy people. The original algorithm of spectral-time analysis was used to identify the characteristic features of the breathing curves in each group at the stage of digital processing.Results. Breathing curves were analyzed and typical images were obtained for the group of apparently healthy individuals and four groups of patients with various types of bronchial asthma (following the classification of E.V. Nemerov). Conclusion. A spectral-time analysis allowed us to obtain characteristic “single” graphical images of the breath curve in patients with various forms of bronchial asthma. The resulting images can be used as an additional diagnostic criterion. The algorithm proposed by the authors can also be used in the analysis of any other biosignals.

Using Candlestick Charting and GH-Method: Math-Physical Medicine to Investigate the Differences of Glucose Measurement Results Based on both Finger-Piercing Method and Continuous Monitoring Device

The author developed his GH-Method: math-physical medicine (MPM) by applying mathematics, physics, engineering modeling, and computer science such as big data analytics and artificial intelligence to derive the mathematical metabolism model and three prediction tools for weight, FPG, and PPG with >30 input elements. This research paper describes glucose measurement results based on the finger-piercing method and continuous glucose monitor device using candlestick charting and segmentation analysis.

Improving efficiency of clinical studies by a novel, total digital approach: The Red Heart Study (Preprint)

BACKGROUND In general, most clinical studies in patients and healthy volunteers take a longer time to finalize than original planned because of the long recruitment periods. Especially signing the informed consent is time-consuming OBJECTIVE The objectives were to explore if inclusion of a large number of participants could be done quickly by using a total digital approach both for information and signing of informed consent and if a totally web-based inclusion would result in a geographically more uniform distribution of participants METHODS In the Red Heart Study, women with palpitations were intensively studied during two months by means of a handheld ECG monitor device (Coala Heart Monitor), It connects to a smartphone or tablet so the participants can get an immediate response of the results. Recruitment, study information and signing the informed consent were total digital. The informed consent was signed digitally with the help of Swedish eAuthentication (Bank ID). RESULTS Between March and May 2018, 2 424 persons announced interest to participate in the study. On June 19th, 2018, presumptive participants were invited to log in to the website to answer baseline questionnaires and sign informed consent. The recruitment was completed on the 13th of July-after 25 days with 1 089 women included. About 100°000 thumb ECG and 100°000 chest ECG recordings were performed. The mean age of the women was 56±11 years (range 21-88 years). Thirty-five participants were 75 years or older. No participant had any difficulties with this the full digital approach. The participants were evenly distributed between living in the countryside (n=525) and in the cities (n=438). CONCLUSIONS To the best of our knowledge, this is the first-time researchers have used eAuthentication at online signing of informed consent. Fully digitalized inclusion recruitment of 1 089 participants was done in 45 days. This novel approach also resulted in a more evenly geographically distribution of participants CLINICALTRIAL ISRCTN22495299