Detection of obstructive sleep apnea in ECG recordings using time-frequency distributions and dynamic features

Quiceno-Manrique, A. F.; Alonso-Hernández, Jesús B.; González, Carmelo Javier León; Ferrer, Miguel A.; Castellanos-Domínguez, G.

doi:10.1109/iembs.2009.5333736

Cited by 47 publications

(26 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the sake of brevity we cannot describe them. Basically, they all rely on the evaluation of different (sets of) vital parameters: oximetry [5], [6], ElectroEncephalography (EEG) [7], ElectroCardioGraphy (ECG) [4], [8], [9], [10], thoracic and abdominal signals [11], or combinations of them [12], [13]. Through the use of such parameters an event can be classified as either apnea or nonapnea.…”

Section: Introductionmentioning

confidence: 99%

On Finding Explicit Rules for Personalized Forecasting of Obstructive Sleep Apnea Episodes

Falco

Pietro

Sannino

2015

2015 IEEE International Conference on Information Reuse and Integration

View full text Add to dashboard Cite

Obstructive Sleep Apnea (OSA) is a breathing disorder that takes place during sleep, and has both short-as well as long-term consequences on patient's health. Real-time monitoring for a patient can be carried out by making use of ElectroCardioGraphy (ECG) recordings. This paper introduces a methodology to forecast OSA events in the minutes following the current time instant. This is accomplished by using a tool based on Differential Evolution that is able to automatically extract offline knowledge about the monitored patient as a form of a set of IF-THEN rules. These rules connect the values of some ECGrelated parameters recorded in the last minutes the occurrence of an apnea episode in the following minute. This approach has been tested on a literature database with 35 OSA patients. A comparison against six well-known classifiers has been performed.

show abstract

Section: Introductionmentioning

confidence: 99%

On Finding Explicit Rules for Personalized Forecasting of Obstructive Sleep Apnea Episodes

Falco

Pietro

Sannino

2015

2015 IEEE International Conference on Information Reuse and Integration

View full text Add to dashboard Cite

show abstract

“…Quiceno-Manrique et al [5] proposed a simple diagnostic tool for OSA with a high accuracy (up to 92.67%) using timefrequency distributions and dynamic features in ECG signal.…”

Section: Related Workmentioning

confidence: 99%

A Neural Network System for Detection of Obstructive Sleep Apnea Through SpO2 Signal Features

Almazaydeh¹,

Faezipour²,

Elleithy³

2012

IJACSA

View full text Add to dashboard Cite

Abstract-Obstructive sleep apnea (OSA) is a common disorder in which individuals stop breathing during their sleep. These episodes last 10 seconds or more and cause oxygen levels in the blood to drop. Most of sleep apnea cases are currently undiagnosed because of expenses and practicality limitations of overnight polysomnography (PSG) at sleep labs, where an expert human observer is required. New techniques for sleep apnea classification are being developed by bioengineers for most comfortable and timely detection. In this study, we develop and validate a neural network (NN) using SpO2 measurements obtained from pulse oximetry to predict OSA. The results show that the NN is useful as a predictive tool for OSA with a high performance and improved accuracy, approximately 93.3%, which is better than reported techniques in the literature.

show abstract

“…The k-NN classifier has been recently utilized to predict a variety of heart diseases [6]. Numerical approximation techniques based on splines are extensively applied in engineering areas such as signal processing [7] Among the different spline techniques, we selected (m, s)-splines [8] for their favorable characteristics to our research: they allow to face multivariable problems, the problem domain is not required to be a mesh grid (that is, data used to compute interpolation can be at any point in space), and the computational load is low.…”

Section: Single-step Decision Enginesmentioning

confidence: 99%

Decision support in AmI sport environments

Vales‐Alonso

López-Matencio

Alcaraz

et al. 2011

2011 IEEE SENSORS Proceedings

View full text Add to dashboard Cite

In this paper, we comparatively analyze a set of decision making methodologies that have been successfully applied to improve athletes' training in real-time. The decisionmaking processes are based on the environmental data as well as on the athletes' biometrics in a scenario with runners in cross country circuits. Data acquisition is performed by means of a WSN which measures temperature and circuits slopes, whereas heart rate is monitored for each athlete. In all cases, the goal was to select the best tracks in the circuit to control the heart rate of the athlete within a given interval. Two broad types of decision-making procedures have been studied: (i) optimal classifiers, and (ii) dynamic programming. Results show a notable performance increase of these methods over heuristics, as well as the importance of environmental sensor data.

show abstract

Detection of obstructive sleep apnea in ECG recordings using time-frequency distributions and dynamic features

Cited by 47 publications

References 10 publications

On Finding Explicit Rules for Personalized Forecasting of Obstructive Sleep Apnea Episodes

On Finding Explicit Rules for Personalized Forecasting of Obstructive Sleep Apnea Episodes

A Neural Network System for Detection of Obstructive Sleep Apnea Through SpO2 Signal Features

Decision support in AmI sport environments

Contact Info

Product

Resources

About