/pdf/where-is-the-artificial-intelligence-applied-in-dentistry-39eekn76.pdf

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

Obstructive sleep apnea is a common sleep-disordered breathing disorder caused by repeated obstruction of the upper airway. Existing sleep apnea automatic detection models usually use the time-frequency domain and nonlinear features of physiological signals, and some are based on deep learning. The use of respiratory status parameters during sleep is mostly discussed for medical purposes, yet they are the most direct evidence of respiratory and pulmonary dysfunction. This paper proposed an automatic OSA detection method using respiratory parameters calculated from nasal airflow signals including respiratory cycle, respiratory rate (RR), tidal volume (TV), fractional inspiration (FIT), and minute ventilation (MV). Features chosen by statistical analysis were available to fed into machine learning models, and the results were compared. The selected features were further applied to ECG-derived respiration (EDR) signals to classify OSA and non-OSA patients. Among the models based on nasal airflow signal, extreme gradient boosting (XGBoost) had the best performance, with accuracy, sensitivity, precision, and F1 score of 86.42%, 83.82%, 87.55%, and 0.857, respectively. On the application of selected features in EDR signals, the results showed that the XGBoost model can achieve 82.76% accuracy and 85.97% precision, respectively. Our research can be used for screening and prognosis monitoring of sleep apneahypopnea syndrome patients with single-lead ECG sensors and provides a new path for the out-of-hospital management of sleep apnea patients. 

Automatic Obstructive Sleep Apnea Detection Based on Respiratory Parameters in Physiological Signals

Obstructive sleep apnea is a common sleep-disordered breathing disorder caused by repeated obstruction of the upper airway. Existing sleep apnea automatic detection models usually use the time-frequency domain and nonlinear features of physiological signals, and some are based on deep learning. The use of respiratory status parameters during sleep is mostly discussed for medical purposes, yet they are the most direct evidence of respiratory and pulmonary dysfunction. This paper proposed an automatic OSA detection method using respiratory parameters calculated from nasal airflow signals including respiratory cycle, respiratory rate (RR), tidal volume (TV), fractional inspiration (FIT), and minute ventilation (MV). Features chosen by statistical analysis were available to fed into machine learning models, and the results were compared. The selected features were further applied to ECG-derived respiration (EDR) signals to classify OSA and non-OSA patients. Among the models based on nasal airflow signal, extreme gradient boosting (XGBoost) had the best performance, with accuracy, sensitivity, precision, and F1 score of 86.42%, 83.82%, 87.55%, and 0.857, respectively. On the application of selected features in EDR signals, the results showed that the XGBoost model can achieve 82.76% accuracy and 85.97% precision, respectively. Our research can be used for screening and prognosis monitoring of sleep apneahypopnea syndrome patients with single-lead ECG sensors and provides a new path for the out-of-hospital management of sleep apnea patients.

Sleep Apnea is a condition in which a person has pauses in breathing or very low breathing episodes during sleep. It is a condition that could prove life-threatening if not monitored and treated. A medical diagnosis of Sleep Apnea involves overnight recording of body signals, monitoring by a medical professional, use of hospital based equipment and data analysis for detection of anomalies. During the past decade, the measurement and analysis of human body signals using machine learning techniques on embedded devices have started to transform healthcare applications. The use of cost effective micro-controllers can ensure that health monitoring is available and accessible to all. In this paper, we show that machine learning models deployed on microcontrollers can successfully analyze ECG signals in real-time for Sleep Apnea detection. We have created TinyML models using TensorFlow Lite which we have deployed on cost effective and resource constrained devices like the Raspberry Pi Pico and ESP32. Our setup has given results comparable to more advanced and expensive devices for the detection of Sleep Apnea using ECG signals. 

Sleep Apnea Detection System Using Machine Learning on Resource-Constrained Devices

The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2021) took place from September 21–26 virtually, around the world; co-located with the International Symposium on Wearable Computers (ISWC). This article discusses some of the interesting research published in the Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies, as well as those accepted at ISWC this year—presented virtually with Live Q&A over Whova, Zoom, and Gather.Town. Furthermore, it covers some of the interesting plenary events, such as the Keynotes by Cynthia Breazeal (MIT Media Labs) and John LePore (Perception), as well as the panel discussion on the 30th anniversary of Mark Weiser’s paper on the Vision of a Computer for the 21st Century. Finally, some of the interesting highlights from the ISWC gadget-show and design-exhibition, alongside multiple virtual workshops as part of the adjunct program have been presented. 

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Stepping Into the Next Decade of Ubiquitous and Pervasive Computing: UbiComp and ISWC 2021

Sleep apnea is a sleep disorder in which breathing is briefly and repeatedly interrupted. Polysomnography (PSG) is the standard clinical test for diagnosing sleep apnea. However, it is expensive and time-consuming which requires hospital visits, specialized wearable sensors, professional installations, and long waiting lists. To address this problem, we design a smartwatch-based system called ApneaDetector, which exploits the built-in sensors in smartwatches to detect sleep apnea. Through a clinical study, we identify features of sleep apnea captured by smartwatch, which can be leveraged by machine learning techniques for sleep apnea detection. However, there are many technical challenges such as how to extract various special patterns from the noisy and multi-axis sensing data. To address these challenges, we propose signal denoising and data calibration techniques to process the noisy data while preserving the peaks and troughs which reflect the possible apnea events. We identify the characteristics of sleep apnea such as signal spikes which can be captured by smartwatch, and propose methods to extract proper features to train machine learning models for apnea detection. Through extensive experimental evaluations, we demonstrate that our system can detect apnea events with high precision (0.9674), recall (0.9625), and F1-score (0.9649).

Yifei Xiao

Papers

ApneaDetector: Detecting Sleep Apnea with Smartwatches

Effects of fluoxetine on the restoration of functional independence in patients after acute cerebral infarction and prognostic factors