An Automated Heart Rate-based Algorithm for Sleep Stage Classification: Validation Using Conventional PSG and Innovative Wearable ECG Device

Study Objectives: Validate a HR-based deep-learning algorithm for sleep staging named Neurobit-HRV (Neurobit Inc., New York, USA). Methods: The algorithm can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-second epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n=994 participants) and a proprietary dataset (Z3Pulse, n=52 participants), composed of HR recordings collected with a chest-worn, wireless sensor. A simultaneous PSG was collected using SOMNOtouch. We evaluated the performance of the models in both datasets using Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP). Results: CinC - The highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect sleep scoring, while a significant decrease of performance by age was reported across the models. Z3Pulse - The highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment. Conclusions: Results demonstrate the feasibility of accurate HR-based sleep staging. The combination of the illustrated sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution easily deployable in the home.

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GI-SleepNet: A Highly Versatile Image-based Sleep Classification using a Deep Learning Algorithm

10.21203/rs.3.rs-586116/v1 ◽

2021 ◽

Author(s):

Tian Xiang Gao ◽

Jia Yi Li ◽

Yuji Watanabe ◽

Chi Jung Hung ◽

Akihiro Yamanaka ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Sleep Stage ◽

Image Data ◽

Sleep Research ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Deep Learning Algorithm ◽

Technical Requirements ◽

Sleep Stage Classification

Abstract Sleep-stage classification is essential for sleep research. Various automatic judgment programs including deep learning algorithms using artificial intelligence (AI) have been developed, but with limitations in data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase the accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as few as one mouse data yielded significant accuracy. Because of its image-based nature, it is easy to apply to data of different formats, of different species of animals, and even outside of sleep research. Image data can be easily understood by humans, thus especially confirmation by experts is easy when there are some anomalies of prediction. Because deep learning of images is one of the leading fields in AI, numerous algorithms are also available.

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GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm

Clocks & Sleep ◽

10.3390/clockssleep3040041 ◽

2021 ◽

Vol 3 (4) ◽

pp. 581-597

Author(s):

Tianxiang Gao ◽

Jiayi Li ◽

Yuji Watanabe ◽

Chijung Hung ◽

Akihiro Yamanaka ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Sleep Stage ◽

Image Data ◽

Sleep Research ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Deep Learning Algorithm ◽

Technical Requirements ◽

Sleep Stage Classification

Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images, and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase its accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as little as one mouse’s data yielded significant accuracy. Due to its image-based nature, the program is easy to apply to data of different formats, different species of animals, and even outside sleep research. Image data can be easily understood; thus, confirmation by experts is easily obtained, even when there are prediction anomalies. As deep learning in image processing is one of the leading fields in AI, numerous algorithms are also available.

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Review for "Smart grid security enhancement by detection and classification of non‐technical losses employing deep learning algorithm"

10.1002/2050-7038.12521/v1/review4 ◽

2020 ◽

Keyword(s):

Deep Learning ◽

Smart Grid ◽

Learning Algorithm ◽

Grid Security ◽

Security Enhancement ◽

Smart Grid Security ◽

Deep Learning Algorithm

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Review for "Smart grid security enhancement by detection and classification of non‐technical losses employing deep learning algorithm"

10.1002/2050-7038.12521/v1/review2 ◽

2020 ◽

Keyword(s):

Deep Learning ◽

Smart Grid ◽

Learning Algorithm ◽

Grid Security ◽

Security Enhancement ◽

Smart Grid Security ◽

Deep Learning Algorithm

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Faculty Opinions recommendation of Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.732648119.793554280 ◽

2018 ◽

Author(s):

Christine Ko

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Clinical Images ◽

Deep Learning Algorithm

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Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

Applied Sciences ◽

10.3390/app10051797 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1797 ◽

Cited By ~ 2

Author(s):

Mera Kartika Delimayanti ◽

Bedy Purnama ◽

Ngoc Giang Nguyen ◽

Mohammad Reza Faisal ◽

Kunti Robiatul Mahmudah ◽

...

Keyword(s):

Machine Learning ◽

Sleep Stage ◽

Machine Learning Algorithms ◽

High Dimensional ◽

Sleep Stages ◽

Eeg Signals ◽

Stage Classification ◽

Sleep Stage Classification ◽

Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

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Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning

Cancers ◽

10.3390/cancers13071615 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1615

Author(s):

Ines P. Nearchou ◽

Hideki Ueno ◽

Yoshiki Kajiwara ◽

Kate Lillard ◽

Satsuki Mochizuki ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Prognostic Significance ◽

Stage Ii ◽

Full Cohort ◽

Test Set ◽

Colorectal Tumour ◽

Desmoplastic Reaction ◽

Deep Learning Algorithm

The categorisation of desmoplastic reaction (DR) present at the colorectal cancer (CRC) invasive front into mature, intermediate or immature type has been previously shown to have high prognostic significance. However, the lack of an objective and reproducible assessment methodology for the assessment of DR has been a major hurdle to its clinical translation. In this study, a deep learning algorithm was trained to automatically classify immature DR on haematoxylin and eosin digitised slides of stage II and III CRC cases (n = 41). When assessing the classifier’s performance on a test set of patient samples (n = 40), a Dice score of 0.87 for the segmentation of myxoid stroma was reported. The classifier was then applied to the full cohort of 528 stage II and III CRC cases, which was then divided into a training (n = 396) and a test set (n = 132). Automatically classed DR was shown to have superior prognostic significance over the manually classed DR in both the training and test cohorts. The findings demonstrated that deep learning algorithms could be applied to assist pathologists in the detection and classification of DR in CRC in an objective, standardised and reproducible manner.

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