The Effectiveness Obstructive Sleep Apnea Monitoring Using Telemedicine Smartphone System (TmSS)

Data at the world health organization shows that around more than 100 million people worldwide suffer from Obstructive Sleep Apnea (OSA) and most sufferers go undetected. The purpose of this study is to develop an apnea monitor that can detect the symptoms of Obstructive Sleep Apnea using the Telemedicine Smartphone System (TmSS). The contribution of this study is the generation of telemedicine systems in sleep apnea monitoring devices. So that the monitoring tools can be effective and efficient when used, then the Obstructive Sleep Apnea monitoring tool is made with the TmSS system that can transmit data values wirelessly and in real-time. This tool uses a flex sensor to detect patient breathing. The output generated by the sensor is then conditioned on an analog signal conditioner (PSA) circuit. The signal output from the PSA is processed on the ATMega 328 microcontroller to get the respiration value which is then sent via Bluetooth HC-05 and displayed on an android smartphone device which will also display a warning notification in the event of stopping breathing (apnea). Based on the results of the measurement of respiration values compared with standard equipment produces the highest error value of 6.98% and the lowest of 0.00%. The tool can send data respiration values using the TmSS system properly. This tool can be implemented to detect the symptoms of Obstructive Sleep Apnea.

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.

Severe sleep apnea is associated with atrial fibrillation burden in pacemaker recipients

Background New generation pacemaker allow the assessment day by day of sleep disordered breathing (SDB) based on impedance measurement. A recent study demonstrated that incidence of AF is higher in case of severe SA monitored by pacemaker Purpose The aim was to compare the atrial fibrillation (AF) burden between patients with severe and non-severe sleep apnea (SA) detected with pacemakers monitoring (SDB). Methods This retrospective study was carried out at our University Hospital. We included all patients with Microport CRM pacemaker implanted from 2013 to 2016 at our university hospital. Exclusion criteria were inactivation of sleep apnea monitoring (SAM), history of sleep apnea, missing data or invalid data. AF burden was assessed according to Fallback mode switch (FMS) duration. Respiratory disturbance index (RDI) was calculated as the average number of events (ventilation pause and reductions) per number of hours of monitoring. Patients with RDI<20/h were compared with patients with RDI≥20/h (considered as severe SA group). Results 404 patients (mean age = 79.7±10 years; 52.0% men) were included. The most prevalent indication for cardiac pacing was atrioventricular block in 57%. Mean RDI was 18.9 events per hour. 234 (58%) of them had a mean RDI <20 and 170 (42%) had a mean RDI ≥20. Compared to patients with mean RDI<20, those with mean RDI ≥20 were youngers (78.6±10 years Vs 81.8±8 years; p=0.02), were more likely to be male (58.2% Vs 47.5%: p=0.035) and had more heart failure history (28.8% Vs 19.2%: p=0.03). BMI was not different between groups (26.3±5. vs 26.3±4; P=0.33). Mean follow-up was 27 months. Patients with RDI ≥20 had a mean Atrial fibrillation duration longer than patients with RDI <20 (631 min Vs 291 min respectively; p=0.014). RDI was correlated with FMS (r=0.26; p=0.0004). The stroke rate tended to be higher in the RDI ≥20 group (2.1% vs 5.4%) (p=0.12). Conclusion Severe SA detected by pacemaker was associated with longer AF duration. We did not find higher occurrence of stroke in the severe SA group. Funding Acknowledgement Type of funding source: None