Journal ArticleDOI
Automated Scoring of Obstructive Sleep Apnea and Hypopnea Events Using Short-Term Electrocardiogram Recordings
Ahsan H. Khandoker,Jayavardhana Gubbi,Marimuthu Palaniswami +2 more
- Vol. 13, Iss: 6, pp 1057-1067
TLDR
Results indicate the possibility of recognizing apnea/hypopnea events based on shorter segments of ECG signals and estimate the surrogate apnea index (AI) / hypopneaindex (HI) (AHI) and wavelet-based features of 5-s ECGs.Abstract:
Obstructive sleep apnea or hypopnea causes a pause or reduction in airflow with continuous breathing effort. The aim of this study is to identify individual apnea and hypopnea events from normal breathing events using wavelet-based features of 5-s ECG signals (sampling rate = 250 Hz) and estimate the surrogate apnea index (AI)/hypopnea index (HI) (AHI). Total 82 535 ECG epochs (each of 5-s duration) from normal breathing during sleep, 1638 ECG epochs from 689 hypopnea events, and 3151 ECG epochs from 1862 apnea events were collected from 17 patients in the training set. Two-staged feedforward neural network model was trained using features from ECG signals with leave-one-patient-out cross-validation technique. At the first stage of classification, events (apnea and hypopnea) were classified from normal breathing events, and at the second stage, hypopneas were identified from apnea. Independent test was performed on 16 subjects' ECGs containing 483 hypopnea and 1352 apnea events. The cross-validation and independent test accuracies of apnea and hypopnea detection were found to be 94.84% and 76.82%, respectively, for training set, and 94.72% and 79.77%, respectively, for test set. The Bland-Altman plots showed unbiased estimations with standard deviations of plusmn 2.19, plusmn 2.16, and plusmn 3.64 events/h for AI, HI, and AHI, respectively. Results indicate the possibility of recognizing apnea/hypopnea events based on shorter segments of ECG signals.read more
Citations
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Journal ArticleDOI
Montreal Archive of Sleep Studies: an open-access resource for instrument benchmarking and exploratory research.
TL;DR: An open‐access database of polysomnographic biosignals is proposed, expected to facilitate the development and cross‐validation of sleep analysis automation systems and be a catalyst for cross‐centre collaborations on difficult topics such as improving inter‐rater agreement on sleep stage scoring.
Journal ArticleDOI
Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier
Gang Li,Wan-Young Chung +1 more
TL;DR: The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT)-based features.
Journal ArticleDOI
A Review of Obstructive Sleep Apnea Detection Approaches
Fábio Mendonça,Sheikh Shanawaz Mostafa,Antonio G. Ravelo-García,Fernando Morgado-Dias,Thomas Penzel +4 more
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.
Journal ArticleDOI
Pulse Rate Variability Analysis for Discrimination of Sleep-Apnea-Related Decreases in the Amplitude Fluctuations of Pulse Photoplethysmographic Signal in Children
TL;DR: The results suggest that PRV can be used in apnea detectors based on DAP events, to discriminate apneic from nonapneic events avoiding the need for ECG recordings.
Journal ArticleDOI
An Automatic Screening Approach for Obstructive Sleep Apnea Diagnosis Based on Single-Lead Electrocardiogram
Lili Chen,Xi Zhang,Changyue Song +2 more
TL;DR: An automatic-segmentation-based screening approach with the single channel of Electrocardiogram (ECG) signal for OSA subject diagnosis and the main work of the proposed approach lies in three aspects: an automatic signal segmentation algorithm is adopted for signal segmentations instead of the equal-length segmentation rule.
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