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Open AccessJournal ArticleDOI

Classifying obstructive sleep apnea using smartphones

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TLDR
A reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests and demonstrates the effectiveness of the developed system when compared to the gold standard.
About
This article is published in Journal of Biomedical Informatics.The article was published on 2014-12-01 and is currently open access. It has received 81 citations till now. The article focuses on the topics: Polysomnography & Obstructive sleep apnea.

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Journal ArticleDOI

A Review of Obstructive Sleep Apnea Detection Approaches

TL;DR: The objective of this review is to analyze already existing algorithms that have not been implemented on hardware but have had their performance verified by at least one experiment that aims to detect obstructive sleep apnea to predict trends.
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Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography

TL;DR: The study shows that the absolute parameters and sleep staging reported by the Sleep Time app (Azumio, Inc.) for iPhones correlate poorly with PSG, and further studies comparing app sleep-wake detection to actigraphy may help elucidate its potential clinical utility.
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New technology to assess sleep apnea: wearables, smartphones, and accessories

TL;DR: Home recording and non-obtrusive recording over extended periods of time with telemedicine methods support this research and require new technologies to investigate underlying mechanisms in the regulation of sleep in order to better understand the pathophysiology of sleep disorders.
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Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

TL;DR: Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection and some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals.
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Design and Evaluation of an Intelligent Remote Tidal Volume Variability Monitoring System in E-Health Applications

TL;DR: An intelligent system for patient home care, capable of measuring respiration rate and tidal volume variability via a wearable sensing technology, designed particularly for the goal of diagnosis and treatment in patients with pathological breathing, e.g., respiratory complications after surgery or sleep disorders is proposed.
References
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Journal ArticleDOI

Clinical tests: sensitivity and specificity

TL;DR: Many clinical tests are used to confirm or refute the presence of a disease or further the diagnostic process, but most clinical tests fall short of the ideal of correctly identifying all patients with the disease and all patients who are disease free.
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Physiological Parameter Monitoring from Optical Recordings With a Mobile Phone

TL;DR: It is shown that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor.
Journal ArticleDOI

The effectiveness of M-health technologies for improving health and health services: a systematic review protocol

TL;DR: This systematic review will summarise the evidence for the effectiveness of mobile technology interventions for improving health and health service outcomes (M-health) around the world and guide future work on intervention development and primary research in this field.
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A Wearable Smartphone-Based Platform for Real-Time Cardiovascular Disease Detection Via Electrocardiogram Processing

TL;DR: Two smartphone-based wearable CVD-detection platforms capable of performing real-time ECG acquisition and display, feature extraction, and beat classification are developed and the same statistical summaries available on resting ECG machines are provided.
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