Continuous SpO2 Monitoring Using Reflectance Pulse Oximetry at the Wrist and Upper Arm During Overnight Sleep Apnea Recordings

Sleep apnea (SA) is a chronic sleep-related disorder consisting of repetitive pauses or restrictions in airflow during sleep and is known to be a risk factor for cerebro- and cardiovascular disease. It is generally diagnosed using polysomnography (PSG) recorded overnight in an in-lab setting at the hospital. This includes the measurement of blood oxygen saturation (SpO2), which exhibits fluctuations caused by SA events. In this paper, we investigate the accuracy and utility of reflectance pulse oximetry from a wearable device as a means to continuously monitor SpO2 during sleep. To this end, we analyzed data from a cohort of 134 patients with suspected SA undergoing overnight PSG and wearing the watch-like device at two measurement locations (upper arm and wrist). Our data show that standard requirements for pulse oximetry measurements are met at both measurement locations, with an accuracy (root mean squared error) of 1.9% at the upper arm and 3.2% at the wrist. With a rejection rate of 3.1%, the upper arm yielded better results in terms of data quality when compared to the wrist location which had 30.4% of data rejected.

Generative Pre-Trained Transformer for Cardiac Abnormality Detection

ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. The goal of the Physionet/CinC 2021 challenge was to accurately classify clinical diagnosis based on 12, 6, 4, 3 or 2-lead ECG recordings in order to aid doctors in the diagnoses of different heart conditions. Transformers have had great success in the field of natural language processing in the past years. Our team, CinCSEM, proposes to draw the parallel between text and periodic time series signals by viewing the repeated period as words and the whole signal as a sequence of such words. In this way, the attention mechanisms of the transformers can be applied to periodic time series signals. In our implementation, we follow the Transformer Encoder architecture, which combines several encoder layers followed by a dense layer with linear or sigmoid activation for generative pre-training or classification, respectively. The use case presented here is multi-label classification of heartbeat abnormalities of ECG recordings shared by the challenge. Our best entry, not exceeding the challenge's hardware limitations, achieved a score of 0.12, 0.07, 0.10, 0.10 and 0.07 on 12-lead, 6-lead, 4-lead, 3-lead and 2-lead test set respectively. Unfortunately, our team was unable to be ranked because of a missing pre-print.