Machine Learning Based Sleep Phase Monitoring using Pulse Oximeter and Accelerometer

2019 ◽

Vol 9 (2) ◽

pp. 3011-3017

Keyword(s):

Machine Learning ◽

Energy Consumption ◽

Duty Cycle ◽

Mac Protocol ◽

Selection Function ◽

Medium Access ◽

Sleep Phase ◽

Agricultural Applications ◽

Time Division ◽

Wireless Node

Duty cycle of a Medium Access Control (MAC) protocol is made up of sleep phase, wake-up phase and listen phase. MAC protocols usually proposes to optimize the duration of the wake-up and listen phases, in order to increase the duration of the sleep phase, thereby reducing the unwanted energy consumption of the wireless node. In this paper, we propose an Artificial Intelligence (AI) and machine learning (ML) based approach, which uses a hybrid combination of Time Division Multiple Access (TDMA), Bitmap Assisted MAC (BMA) and Sensor MAC (SMAC). The machine learning layer utilizes the duty cycle in the MAC layer, and generates multiple solutions for a given wireless communication. The AI layer then selects the best solution from the generated solutions by incorporating a duty cycle factor in the selection function, thereby optimizing the duty cycle of the protocol. The proposed system shows a 15% improvement in communication speed, and a 10% reduction in energy consumption across multiple communications. We plan to further extend this work for rural India, and apply it to real time agricultural applications.

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Using self-supervised feature learning to improve the use of pulse oximeter signals to predict paediatric hospitalization

Wellcome Open Research ◽

10.12688/wellcomeopenres.17148.1 ◽

2021 ◽

Vol 6 ◽

pp. 248

Author(s):

Paul Mwaniki ◽

Timothy Kamanu ◽

Samuel Akech ◽

Dustin Dunsmuir ◽

J. Mark Ansermino ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Deep Learning ◽

Pulse Oximeter ◽

Regression Models ◽

Data Sets ◽

Learning Models ◽

Data Set ◽

Logistic Regression Models ◽

End To End

Background: The success of many machine learning applications depends on knowledge about the relationship between the input data and the task of interest (output), hindering the application of machine learning to novel tasks. End-to-end deep learning, which does not require intermediate feature engineering, has been recommended to overcome this challenge but end-to-end deep learning models require large labelled training data sets often unavailable in many medical applications. In this study, we trained machine learning models to predict paediatric hospitalization given raw photoplethysmography (PPG) signals obtained from a pulse oximeter. We trained self-supervised learning (SSL) for automatic feature extraction from PPG signals and assessed the utility of SSL in initializing end-to-end deep learning models trained on a small labelled data set with the aim of predicting paediatric hospitalization.Methods: We compared logistic regression models fitted using features extracted using SSL with end-to-end deep learning models initialized either randomly or using weights from the SSL model. We also compared the performance of SSL models trained on labelled data alone (n=1,031) with SSL trained using both labelled and unlabelled signals (n=7,578). Results: The SSL model trained on both labelled and unlabelled PPG signals produced features that were more predictive of hospitalization compared to the SSL model trained on labelled PPG only (AUC of logistic regression model: 0.78 vs 0.74). The end-to-end deep learning model had an AUC of 0.80 when initialized using the SSL model trained on all PPG signals, 0.77 when initialized using SSL trained on labelled data only, and 0.73 when initialized randomly. Conclusions: This study shows that SSL can improve the classification of PPG signals by either extracting features required by logistic regression models or initializing end-to-end deep learning models. Furthermore, SSL can leverage larger unlabelled data sets to improve performance of models fitted using small labelled data sets.

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Machine Learning Sleep Phase Monitoring using ECG and EMG

10.1109/icset53708.2021.9612546 ◽

2021 ◽

Author(s):

Leila G. Ablao ◽

Zmantha Ysabel B. Tupaz ◽

Jennifer C. Dela Cruz ◽

Jonathan Ibera

Keyword(s):

Machine Learning ◽

Sleep Phase

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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