Evaluation of a Novel Ear Pulse Oximeter: Towards Automated Oxygen Titration in Eyeglass Frames.

Current oxygen delivery modes lack monitoring and can be cumbersome for patients with chronic respiratory diseases. Integrating a pulse oximeter and nasal oxygen cannulas into eyeglasses would reduce the burden of current solutions. An ear pulse oximeter (OxyFrame) was evaluated on 16 healthy volunteers and 20 hypoxemic patients with chronic respiratory diseases undergoing a prespecified protocol simulating daily activities. Correlation, error, and accuracy root mean square error (ARMS) were calculated to compare SpO2 measured by OxyFrame, a standard pulse oximeter (MASIMO), and arterial blood gas analysis (aBGA). SpO2 measured by OxyFrame and MASIMO correlated strongly in volunteers, with low error and high accuracy (r = 0.85, error = 0.2 ± 2.9%, ARMS = 2.88%). Performances were similar in patients (r = 0.87, error 0 ± 2.5%, ARMS = 2.49% compared with MASIMO; and r = 0.93, error = 0.4 ± 1.9%, ARMS = 1.94% compared with aBGA). However, the percentage of rejected measurements was high (volunteers 77.2%, patients 46.9%). The OxyFrame cavum conchae pulse oximeter was successfully evaluated, and demonstrated accurate SpO2 measurements, compliant with ISO 80601-2-61:2017. Several reasons for the high rejection rate were identified, and potential solutions were proposed, which might be valuable for optimization of the sensor hardware.

Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding-An Interdisciplinary Project.

Artificial intelligence (AI) is gaining increasing interest in the field of medicine because of its capacity to process big data and pattern recognition. Cardiotocography (CTG) is widely used for the assessment of foetal well-being and uterine contractions during pregnancy and labour. It is characterised by inter- and intraobserver variability in interpretation, which depends on the observers' experience. Artificial intelligence (AI)-assisted interpretation could improve its quality and, thus, intrapartal care. Cardiotocography (CTG) raw signals from labouring women were extracted from the database at the University Hospital of Bern between 2006 and 2019. Later, they were matched with the corresponding foetal outcomes, namely arterial umbilical cord pH and 5-min APGAR score. Excluded were deliveries where data were incomplete, as well as multiple births. Clinical data were grouped regarding foetal pH and APGAR score at 5 min after delivery. Physiological foetal pH was defined as 7.15 and above, and a 5-min APGAR score was considered physiologic when reaching ≥7. With these groups, the algorithm was trained to predict foetal hypoxia. Raw data from 19,399 CTG recordings could be exported. This was accomplished by manually searching the patient's identification numbers (PIDs) and extracting the corresponding raw data from each episode. For some patients, only one episode per pregnancy could be found, whereas for others, up to ten episodes were available. Initially, 3400 corresponding clinical outcomes were found for the 19,399 CTGs (17.52%). Due to the small size, this dataset was rejected, and a new search strategy was elaborated. After further matching and curation, 6141 (31.65%) paired data samples could be extracted (cardiotocography raw data and corresponding maternal and foetal outcomes). Of these, half will be used to train artificial intelligence (AI) algorithms, whereas the other half will be used for analysis of efficacy. Complete data could only be found for one-third of the available population. Yet, to our knowledge, this is the most exhaustive and second-largest cardiotocography database worldwide, which can be used for computer analysis and programming. A further enrichment of the database is planned.

Measuring Pressure: Introducing oBPM, the Optical Revolution for Blood Pressure Monitoring.

According to the World Health Organization, every third adult suffers from hypertension-which amounts to 1 billion adults worldwide. Hypertension can lead to severe complications, such as stroke and heart failure. Each year, this illness results in 7.5 million premature deaths. The paradox of hypertension is that most people suffering from this condition are unaware of it. As such, hypertension is known as the "silent killer."

Getting rid of the wires and connectors in physiological monitoring.

One of the main limitations towards an easy-touse, comfortable, and reliable product for physiological monitoring comes from wires and associated connectors. Wireless solutions for data transmission are more and more common in every domain, but for biopotential and impedance measurements, at least one galvanic line will always be needed. This paper describes a new technology that can make possible the measurement of biopotentials and body impedances with high quality standard using only one wire. As this wire requires neither shield nor isolation, one can imagine a conductive garment that simply connects several sensors distributed over the body. From the user point of view, the product would be 'cableless'.