Hosna Ghandeharioun | Searcharxiv

arXiv2024

Development of a pulse oximeter robust to measurement errors, with the ability to estimate heartrate and transmit data to smartphones

Accurate and real-time monitoring of saturated oxygen level of the blood is an important clinical issue gaining great attention in recent years and during COVID 19 pandemic. Monitoring the patients` ventilation and respiration dynamic is widespread and has been adopted as a standard for anesthesia, neonatal care, and post-operative recovery as well. In this paper the fundamentals of current pulse oximeter devices are reviewed and development of a prototype is explained. Our system has two red and infrared light sources radiating to the tissue. The amount of absorbed and transmitted energy is measured by the photodetector, and finally the amount of oxygen content of the blood is estimated based on these values. We used MAX30100 module. Our system has two advantages relating to similar devices; robustness to errors due to power supply variations, ambient light and motion artifacts. Our system has also the ability to estimate heart rate and transmit the data to a smart phone. This makes our proposed system a potentially good hardware for home monitoring of blood oxygen and respiration efficiency if collaborated with a good mobile application for detecting blood de-saturations.

eess.SP↗

arXiv2021

Automatic Home-based Screening of Obstructive Sleep Apnea Using Single Channel Electrocardiogram and SPO2 Signals

Obstructive sleep apnea (OSA) is one of the most widespread respiratory diseases today. Complete or relative breathing cessations due to upper airway subsidence during sleep is OSA. It has confirmed potential influence on Covid-19 hospitalization and mortality, and is strongly associated with major comorbidities of severe Covid-19 infection. Un-diagnosed OSA may also lead to a variety of severe physical and mental side-effects. To score OSA severity, nocturnal sleep monitoring is performed under defined protocols and standards called polysomnography (PSG). This method is time-consuming, expensive, and requiring professional sleep technicians. Automatic home-based detection of OSA is welcome and in great demand. It is a fast and effective way for referring OSA suspects to sleep clinics for further monitoring. On-line OSA detection also can be a part of a closed-loop automatic control of the OSA therapeutic/assistive devices. In this paper, several solutions for online OSA detection are introduced and tested on 155 subjects of three different databases. The best combinational solution uses mutual information (MI) analysis for selecting out of ECG and SpO2-based features. Several methods of supervised and unsupervised machine learning are employed to detect apnoeic episodes. To achieve the best performance, the most successful classifiers in four different ternary combination methods are used. The proposed configurations exploit limited use of biological signals, have online working scheme, and exhibit uniform and acceptable performance (over 85%) in all the employed databases. The benefits have not been gathered all together in the previous published methods.

cs.LG↗