Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS

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

Analysis of Abdominal Respiratory Sensor Performance in Sleep Apnea Conditions

Abdominal respiratory sensor is a sensor used to detect sleep apnea that is specifically for neonates, this sensor is specifically for neonates because the use of this sensor does not require a voltage input to activate the sensor. In the absence of voltage input so as not to disturb the heart rhythm in neonates. When the sensor is no longer elastic, the pressure difference generated by the sensor will be unstable so that the sensor cannot work optimally. With these conditions, the period of use of the sensor needs to be known how durable the sensor is when it is used on patients so that the sensor can maximally detect the occurrence of apnea in neonates. How many times have you been in apnea. This study uses an Arduino microcontroller to process the pressure value and RR value generated by the stomach sensor and the MPX5010dp pressure sensor. the research method used is to use a simulator to analyze the combination of abdominal sensors and pressure sensors to monitor apnea. If viewed based on the average error, the error value in the RR 10bpm setting is ±0.185%, the RR 15 setting is ±0.245%, and the setting RR 20bpm is ±0.383%. From the average error value, it can be said that the higher the RR setting value, the higher the average error for each decrease in pressure output. it can be concluded that the performance of the use of the Abdominal Respiratory Sensor and Pressure Sensor on the Apnea Monitoring module functions well in detecting RR according to the settings on the simulator for 3-day monitoring. The development that can be done in this research is to use a more sensitive pressure sensor so that the results obtained are more stable and make the module display more attractive

Breathing Monitoring and Pattern Recognition with Wearable Sensors

This chapter introduces the anatomy and physiology of the respiratory system, and the reasons for measuring breathing events, particularly, using wearable sensors. Respiratory monitoring is vital including detection of sleep apnea and measurement of respiratory rate. The automatic detection of breathing patterns is equally important in other respiratory rehabilitation therapies, for example, magnetic resonance exams for respiratory triggered imaging, and synchronized functional electrical stimulation. In this context, the goal of many research groups is to create wearable devices able to monitor breathing activity continuously, under natural physiological conditions in different environments. Therefore, wearable sensors that have been used recently as well as the main signal processing methods for breathing analysis are discussed. The following sensor technologies are presented: acoustic, resistive, inductive, humidity, acceleration, pressure, electromyography, impedance, and infrared. New technologies open the door to future methods of noninvasive breathing analysis using wearable sensors associated with machine learning techniques for pattern detection

Pemantauan Apnea Berbasis Internet of Things dengan Notifikasi di Mobilephone

Penderita gangguan henti napas pada saat tidur (sleep apnea) semakin meningkat, hampir lebih dari 80% orang menderita gangguan ini tidak terdiagnosis. Gejala dari sleep apnea yaitu terjadinya henti napas selama lebih dari 10 detik. Tujuan dari penelitian ini adalah merancang alat monitor apnea agar dapat mendeteksi gejala sleep apnea. Kontribusi dalam penelitian ini adalah sistem monitoring atau pemantauan jarak jauh sehingga orang lain dapat memantau kondisi pasien meskipun tidak sedang mendampinginya. Agar dapat mempermudah proses monitoring dan pendiagnosaan pasien maka dibuatlah alat apnea monitor berbasis Internet of Things dengan dilengkapi notifikasi pada android sehingga dapat dengan cepat dilakukannya penanganan pada pasien. Perancangan alat ini menggunakan piezoelektrik sebagai sensor pendeteksi pernapasan yang diletakkan pada bagian perut pasien. Output sensor berupa tegangan kemudian dikondisikan pada rangkaian PSA. Menggunakan mikrokontroler ESP32 sebagai pemrosesan sinyal yang dibentuk oleh rangkaian PSA dan diolah menjadi nilai respirasi. Nilai respirasi kemudian dikirimkan ke perangkat android menggunakan jaringan Wi-Fi dan ditampilkan pada aplikasi Blynk. Apabila terdeteksi kejadian henti napas selama lebih dari 10 detik maka alat akan menyalakan indikator dan mengaktifkan notifikasi pada android. Penelitian ini melakukan pengukuran amplitudo sinyal respirasi dan nilai respirasi terhadap responden dan juga melakukan pengujian pengiriman jarak jauh menggunakan jaringan Wi-Fi. Hasil pengujian pada penelitian ini alat dapat mengirimkan data dengan baik dan tanpa loss data dengan jarak 5 meter dalam ruangan dan 10 meter berbeda ruangan. Alat ini dapat diimplementasikan pada proses monitoring pasien sehingga dapat mengurangi penderita gangguan sleep apnea. Patients with breathing problems during sleep (sleep apnea) are increasing, almost more than 80% of people suffering from this disorder are not diagnosed. Symptoms of sleep apnea include breathing for more than 10 seconds. The purpose of this study is to design apnea monitoring devices to detect sleep apnea symptoms. The contribution in this study is a monitoring system or remote monitoring so that others can monitor the condition of the patient even though not accompanying him. In order to simplify the process of monitoring and diagnosing patients, an apnea monitor based on the Internet of Things is made with notifications on android so that treatment can be quickly performed on patients. The design of this device uses piezoelectric as a respiratory detection sensor 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 signal processing which is formed by the PSA circuit and processed into respiration values. Respiration values ​​are then sent to the Android device using a Wi-Fi network and displayed on the Blynk app If a stop breathing event is detected for more than 10 seconds, the device will turn on the indicator and activate the notification on the android. The test results in this study the tool can send data properly and without loss data with a distance of 5 meters in a room and 10 meters in a different room. This tool can be implemented in the patient monitoring process so that it can reduce sufferers of sleep apnea disorders

Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning

Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an overnight sleep study in a specialized sleep clinic. This setup is expensive and the number of beds and staff are limited, leading to a long waiting time. To enable more patients to be tested, and repeated monitoring for diagnosed patients, portable sleep monitoring devices are being developed. These devices automatically detect sleep apnea events in one or more respiration-related signals. There are multiple methods to measure respiration, with varying levels of signal quality and comfort for the patient. In this study, the potential of using the bio-impedance (bioZ) of the chest as a respiratory surrogate is analyzed. A novel portable device is presented, combined with a two-phase Long Short-Term Memory (LSTM) deep learning algorithm for automated event detection. The setup is benchmarked using simultaneous recordings of the device and the traditional polysomnography in 25 patients. The results demonstrate that using only the bioZ, an area under the precision-recall curve of 46.9% can be achieved, which is on par with automatic scoring using a polysomnography respiration channel. The sensitivity, specificity and accuracy are 58.4%, 76.2% and 72.8% respectively. This confirms the potential of using the bioZ device and deep learning algorithm for automatically detecting sleep respiration events during the night, in a portable and comfortable setup

Phenotype-Based and Self-Learning Inter-Individual Sleep Apnea Screening With a Level IV-Like Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes and test it on a Level IV-like monitoring system.Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV-like wearable device measuring the thoracic and abdominal movements and the SpO2.Results: With the leave-one-subject-out cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03.Conclusion: The results confirm the hypothesis and show that the proposed algorithm has potential to screen patients with SAS

Recent development of respiratory rate measurement technologies

Respiratory rate (RR) is an important physiological parameter whose abnormity has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to do, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies

Wearable sensors for respiration monitoring: a review

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.This work was supported by the Spanish Government (MICINN) under Projects TED2021-131209B-I00 and PID2021-124288OB-I00.Peer ReviewedPostprint (published version