Recent Trends of Artificial Intelligence and Machine Learning for Insomnia Research
Article information
Abstract
Recently, the research using artificial intelligence (AI) have been actively conducted in various fields. In the sleep medicine, the research using an AI such as machine learning and deep learning have begun to increase explosively to keep pace with this trend of the times. The field where the most research is being done is the automation of diagnosis of obstructive sleep apnea syndrome and scoring of polysomnography. Studies using AI on insomnia also have shown a remarkable increase in recent years. In this paper, we look at the recent trends of insomnia research using AI and provide a milestone to researchers.
INTRODUCTION
Artificial intelligence (AI) refers to machines or algorithms that do what human intelligence can do and machine learning (ML) is a technology and technique that aims to implement functions such as human learning ability in a computer [1]. Mainly, a supervised learning is used in ML and it is a method that let the algorithm learns a pattern from big data and performs classification for the pre-labeled outcome variable [1]. Deep learning (DL), which is in the spotlight these days, is one of ML, a nonlinear regression technique for learning data and pattern recognition using artificial neural networks (ANNs) [2]. According to a report by the U.S. Patent and Trademark Office in 2020, AI patent applications doubled in 2018 (60000/year) compared to 2002 (30000/year) and the proportion in all patent applications is also showing an explosive increase from 9% to 16% [3]. This is a proof that the use of AI has begun to take root in our daily lives.
In the medical research field, using AI is showing an explosive increase [2]. It is actively introduced in various fields such as neurology, radiology, ophthalmology, anesthesia, intensive care medicine and oncology [4-8]. Research using AI is also increasing in sleep medicine and the American Academy of Sleep Medicine published a position statement in 2020 [9]. The leading field in sleep research using AI is the automated scoring of polysomnography (PSG) and the screening of obstructive sleep apnea syndrome (OSAS) [4,10]. PSG and continuous positive airway pressure data was used mainly and is expected to be used more in the future [4]. In the field of sleep medicine, insomnia is probably the next most studied disease after OSAS. Therefore, the author has conducted this study to examine the recent trends in the use of AI in the insomnia research, and to present a milestone to researchers planning future research.
METHODS
In PubMed (https://pubmed.ncbi.nlm.nih.gov/), the author searched for papers satisfying the MeSH term including “Sleep Initiation and Maintenance Disorders” and “Artificial Intelligence” regardless of the publication year. In addition, related papers identified in the reference and hand searching were also included in the analysis.
The inclusion criterion was a paper using AI for the research of the diagnosis, treatment, and pathophysiology of insomnia.
Exclusion criteria are as follows: 1) even if insomnia patients were the subjects of the study, the aim of study was only an automated scoring of PSG, 2) AI methods were not used for research methods and interventions, 3) papers that did not go through peer-review, 4) meta-analysis, systematic review, and conference material.
This review was conducted partially using guidelines of the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) [11].
RESULTS
Applying the search strategy in PubMed, 17 results were initially found. In addition, handsearching of relevant studies yield 9 studies. After checking duplicates, the title and abstract of 26 studies were screened and the full text of 21 studies were reviewed. Finally 17 studies satisfied the inclusion criteria (Figure 1).
The most common subject and method was diagnosis and screening of insomnia using classification. The most used algorithm was support vector machine (SVM), followed by decision tree and advanced tree (XGBoost, random forest) and ANN.
In addition, the most common modality was PSG. Particularly, there were many studies using signals of the electroencephalography (EEG) channel, and recently there was one study using the electrocardiography (ECG) channel. Next, there were many studies using MRI, followed by studies using questionnaire and using wearable devices.
Table 1 shows the details of the included studies.
DISCUSSION
As a result of the analysis, it was found that Insomnia’s research using AI has exploded in the last three years. Most of the studies were studies to make a predictive model of insomnia using features extracted from the EEG signal of PSG in the extension of automated scoring of PSG [12-16]. Recently there was an interesting study for the prediction of insomnia using ECG signal, and it seems to be in sync with the sleep stage and sleep respiratory event prediction studies using ECG signal of PSG [17].
There were also unsupervised learning studies that reflected recent trends in ML and DL. There was a s phenotyping study using clustering of time series data extracted from wearable device, and a symptomatology study using natural language processing of electrical medical records (EMR) [18,19].
In addition, there was also research on intervention using smartphone application reflecting the recent COVID-19 confinement era [20].
There were still more studies derived from PSG and fMRI studies than insomnia’s unique research.
In terms of methodology, there are still more ML studies than DL studies, and many studies using relatively small dataset at individual hospitals or institutions due to the absence of a large database used in OSAS or PSG studies. Due to this tendency, there was no external clinical validation using external data unseen in training, and the internal validation study using cross-validation holds a majority stakes. There was no study even separating the training set from the test set. To overcome this, it is necessary to create a public big dataset that can be used for training and external validation.
ML does not aim to find a model that best describes a given data, but to find a model that gives more accurate predictions when given new explanatory data [1]. In other words, the goal is to find an optimal model that consider the trade-off between model variance and bias [21]. The variance of the model is the degree to which the model changes when the training data is changed, and the bias is the difference between the hypothesized model and the unknown true model. To do this, it is necessary to solve the problem of overfitting, and for this, recent ML and DL studies tend to separate the training dataset used for model training and the test dataset used for evaluation [21]. So it is worthwhile to use dataset unseen during training to evaluate the performance of the final model [1]. For hyperparameter tuning to select the best model, it is also recommended to use the validation dataset separated from a training and a test dataset [21].
In classification analysis, which is mainly aimed at making predictive models of insomnia, ML algorithms such as SVM and advanced trees show strengths over DL, which is a phenomenon often seen in studies in other fields. The DL algorithm has no choice but to use a sigmoid or a softmax function in the output layer, and that functions are similar with logistic regression [1]. Therefore, DL does not show strength in the classification task, but shows strength in the feature extraction from primary data such as EEG, ECG, MRI, and EMR [2]. Researchers should keep this in mind, and depending on the task, it is recommended to plan tests on varous algorithms besides DL.
Acknowledgements
None
Notes
The author has no potential conflicts of interest to disclose.