Multiple-access versus telemedicine home-based sleep apnea testing for obstructive sleep apnea (OSA) diagnosis: a cost-minimization study

Standardized image-based polysomnography database and deep learning algorithm for sleep-stage classification

STUDY OBJECTIVES

Polysomnography (PSG) scoring is labor-intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for automated sleep scoring have been developed, they are tied to a fixed amount of input channels and resolution. In this study, we constructed a standardized image-based PSG dataset in order to overcome the heterogeneity of raw signal data obtained from various PSG devices and various sleep laboratory environments.

METHODS

All individually exported European data format files containing raw signals were converted into images with an annotation file, which contained the demographics, diagnoses, and sleep statistics. An image-based DL model for automatic sleep staging was developed, compared with a signal-based model, and validated in an external dataset.

RESULTS

We constructed 10253 image-based PSG datasets using a standardized format. Among these, 7745 diagnostic PSG data were used to develop our DL model. The DL model using the image dataset showed similar performance to the signal-based dataset for the same subject. The overall DL accuracy was greater than 80%, even with severe obstructive sleep apnea. Moreover, for the first time, we showed explainable DL in the field of sleep medicine as visualized key inference regions using Eigen-class activation maps. Furthermore, when a DL model for sleep scoring performs external validation, we achieved a relatively good performance.

CONCLUSIONS

Our main contribution demonstrates the availability of a standardized image-based dataset, and highlights that changing the data sampling rate or number of sensors may not require retraining, although performance decreases slightly as the number of sensors decreases.

Feasibility and acceptability of a virtual diagnostic sleep disorders service: A qualitative study

INTRODUCTION

The electronic collection of patient data is used for a large variety of conditions. The boundaries of its use in initial diagnostics and the extent to which it can replace specialist contact, however, remain vague. Our newly developed virtual diagnostic process for common sleep disorders involves digital questionnaire administration, neurophysiological diagnostic studies, and virtual consultation with a sleep specialist. In this study, we evaluated patients' virtual diagnostic process experiences as part of a full evaluation of this process.

METHODS

Patients who completed the virtual diagnostic process participated in online semi-structured interviews based on the following criteria: age ≥ 18 years, no obvious cognitive deficits, and access to the internet. Patients who did not complete the virtual diagnostic process were contacted by telephone and given the opportunity to explain why.

RESULTS

Of the 24 patients included, 14 completed the virtual diagnostic process. Most participants understood the concept of the virtual diagnostic process, could navigate the process, and felt that the combined use of a questionnaire and virtual consultation was meaningful. Although participants could provide information as free text, the simplified closed questions, required for diagnostic classification, triggered feelings of insecurity and the inability to sufficiently describe symptoms, thereby evoking concern. All patients deemed the complementary personal contact important.

DISCUSSION

The findings demonstrate that the virtual diagnostic process is feasible and highly accepted by most patients. However, (virtual) personal (telephone or video) contact is mandatory to address patients' concerns. The virtual diagnostic process application evaluated here likely represents the outer limit of the use of electronic data collection in virtual diagnostic procedures.

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment.