Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual’s quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.

Scoring polysomnography for obstructive sleep apnea diagnosis is a laborious, long, and costly process. Machine learning approaches, such as deep neural networks, can reduce scoring time and costs. However, most methods require prior filtering and preprocessing of the raw signal. Our work presents a novel method for diagnosing obstructive sleep apnea using a transformer neural network with learnable positional encoding, which outperforms existing state-of-the-art solutions. This approach has the potential to improve the diagnostic performance of oximetry for obstructive sleep apnea and reduce the time and costs associated with traditional polysomnography. Contrary to existing approaches, our approach performs annotations at one-second granularity. Allowing physicians to interpret the model’s outcome. In addition, we tested different positional encoding designs as the first layer of the model, and the best results were achieved using a learnable positional encoding based on an autoencoder with structural novelty. In addition, we tried different temporal resolutions with various granularity levels from 1 to 360 s. All experiments were carried out on an independent test set from the public OSASUD dataset and showed that our approach outperforms current state-of-the-art solutions with a satisfactory AUC of 0.89, accuracy of 0.80, and F1-score of 0.79.

Obstructive sleep apnea (OSA) results from repeated collapses of the upper airway during sleep, which can lead to serious health complications. Although polysomnography (PSG) is the diagnostic gold standard, it is costly, labor-intensive, and associated with long waiting times. With the rapid evolution of automated scoring solutions and the emergence of machine learning (ML) and deep learning (DL) in many disciplines, there is a need for tools that use fewer signals and can provide accurate diagnoses. DL models can an process large amounts of data and often generalize effectively to new instances. This makes them a suitable choice for classifying continuous time series data. This study introduces a transformer-based deep learning approach using a single-lead electrocardiogram (ECG) for OSA detection. The proposed architecture, designed to handle raw signals with high sampling rates, preserves temporal continuity over unlimited durations. Without any preprocessing, the model tolerates high-noise raw data. The model is tested with different positional embedding techniques. Additionally, a novel positional encoding technique using an autoencoder is introduced. The proposed approach achieves a high F1 score, outperforming other published work by an average margin of more than 13%. In addition, the model classifies apnea episodes at one-second intervals, providing clinicians with nuanced insights.

Beyond Algorithms: The Case for Standardized Reporting in AI Sleep Scoring