Computerized detection of cyclic alternating patterns of sleep: A new paradigm, future scope and challenges

Sharma, Manish; Lodhi, Harsh; Yadav, Rishita; Elphick, Heather; Acharya, U. Rajendra

doi:10.1016/j.cmpb.2023.107471

Cited by 13 publications

(5 citation statements)

References 61 publications

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“…The current absence of standardization in automated insomnia detection methods contributes to result disparities, posing challenges for cross-study comparisons. These techniques often depend on wearable devices and the collection of personal data, raising concerns about security and privacy implications (Kye et al 2017, Sharma et al 2023b, 2023c. Automated techniques have the potential to significantly enhance insomnia diagnosis, treatment, and comprehension while also increasing accessibility and effectiveness.…”

Section: Discussionmentioning

confidence: 99%

A systematic review on automatic identification of insomnia

Ingle,

Sharma,

Kumar

et al. 2024

Physiol. Meas.

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Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual’s sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals. This has led to the development of technologies for more accurate detection of various sleep disorders, including insomnia. This paper explores the algorithms and techniques for automatic insomnia detection.Methods: We followed the recommendations given in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) during our process of content discovery. Our review encompasses research papers published between 2015 and 2023, with a specific emphasis on automating the identification of insomnia. From a se- lection of well-regarded journals, we included more than 30 publications dedicated to insomnia detection. In our analysis, we assessed the performance of various meth- ods for detecting insomnia, considering different datasets and physiological signals. A common thread across all the papers we reviewed was the utilization of artificial intel- ligence (AI) models, trained and tested using annotated physiological signals. Upon closer examination, we identified the utilization of 15 distinct algorithms for this de- tection task.Results: Result: The major goal of this research is to conduct a thorough study to categorize, compare, and assess the key traits of automated systems for identifying insomnia. Our analysis offers complete and in-depth information. The essential com- ponents under investigation in the automated technique include the data input source, objective, machine learning (ML) and deep learning (DL) network, training framework, and references to databases. We classified pertinent research studies based on ML and DL model perspectives, considering factors like learning structure and input data types.Conclusion: Based on our review of the studies featured in this paper, we have identi- fied a notable research gap in the current methods for identifying insomnia and oppor- tunities for future advancements in the automation of insomnia detection. While the current techniques have shown promising results, there is still room for improvement in terms of accuracy and reliability. Future developments in technology and machine learning algorithms could help address these limitations and enable more effective and efficient identification of insomnia.

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Section: Discussionmentioning

confidence: 99%

A systematic review on automatic identification of insomnia

Ingle,

Sharma,

Kumar

et al. 2024

Physiol. Meas.

Self Cite

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“…The baselines for the performance analysis of the best-performing models are considered from the review studies conducted by Mendonca et al [36], and Sharma et al [37]. Table 4 presents the performance metrics provided by the baselines from the reviews.…”

Section: Discussionmentioning

confidence: 99%

Visual Explanations of Deep Learning Architectures in Predicting Cyclic Alternating Patterns Using Wavelet Transforms

et al. 2023

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Cyclic Alternating Pattern (CAP) is a sleep instability marker defined based on the amplitude and frequency of the electroencephalogram signal. Because of the time and intensive process of labeling the data, different machine learning and automatic approaches are proposed. However, due to the low accuracy of the traditional approach and the black box approach of the machine learning approach, the proposed systems remain untrusted by the physician. This study contributes to accurately estimating CAP in a Frequency-Time domain by A-phase and its subtypes prediction by transforming the monopolar deviated electroencephalogram signals into corresponding scalograms. Subsequently, various computer vision classifiers were tested for the A-phase using scalogram images. It was found that MobileNetV2 outperformed all other tested classifiers by achieving the average accuracy, sensitivity, and specificity values of 0.80, 0.75, and 0.81, respectively. The MobileNetV2 trained model was further fine-tuned for A-phase subtypes prediction. To further verify the visual ability of the trained models, Gradcam++ was employed to identify the targeted regions by the trained network. It was verified that the areas identified by the model match the regions focused on by the sleep experts for A-phase predictions, thereby proving its clinical viability and robustness. This motivates the development of novel deep learning based methods for CAP patterns predictions.

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“…This increase is often attributed to modern lifestyle factors such as increased screen time, irregular work hours, and higher stress levels [25]. The state of the art in sleep disorder diagnosis involves an integration of machine learning models that can process vast amounts of data to detect subtle patterns indicative of sleep abnormalities [26]. These advanced models not only offer greater accuracy but also enhance the efficiency of diagnosis, thus potentially reducing the healthcare burden associated with untreated sleep disorders [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Machine Learning Models for Predicting Sleep Disorders in a Lifestyle and Health Data Context

Airlangga

2024

jiko

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Sleep disorders significantly impact public health, but their detection is often complicated by the multifaceted nature of causative factors. This study investigates the efficacy of various machine learning (ML) models in identifying sleep disorders based on comprehensive lifestyle and health data. We employed a dataset comprising 400 individual records with features including demographic information, sleep metrics, lifestyle factors, and health parameters. The dataset distinguished between individuals with no sleep disorder, insomnia, and sleep apnea. We evaluated a broad spectrum of ML models including logistic regression, decision trees, ensemble methods like RandomForest and GradientBoosting, support vector machines, and neural networks. The models' performances were assessed using accuracy, precision, recall, and F1 score metrics. Results indicated that ensemble methods, particularly RandomForest and XGBClassifier, outperformed other models in terms of accuracy, precision, and F1 scores, achieving values as high as 0.93. These methods proved effective in managing the complexity and variability of the dataset, thereby suggesting their robustness in clinical predictive analytics. The study's findings advocate for the use of advanced ensemble techniques in developing diagnostic tools for sleep disorders, highlighting their potential to enhance predictive accuracy and reliability in real-world healthcare settings. Further research is recommended to optimize these models and explore their integration into clinical practice.

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Computerized detection of cyclic alternating patterns of sleep: A new paradigm, future scope and challenges

Cited by 13 publications

References 61 publications

A systematic review on automatic identification of insomnia

A systematic review on automatic identification of insomnia

Visual Explanations of Deep Learning Architectures in Predicting Cyclic Alternating Patterns Using Wavelet Transforms

Evaluating Machine Learning Models for Predicting Sleep Disorders in a Lifestyle and Health Data Context

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