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Query: UMLS:C0037315 (sleep apnea)
 8,000 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Use of extended electrocardiography (ECG) for detection of sleep disordered breathing SDB when obstructive sleep apneas and Cheyne-Stokes breathing are simultaneously present is explored. A multi-tier algorithm is designed that uses quantitative changes in the morphology of the QRS complex of Lead 1 and V4 due of SDB events and combines those changes with variations in heart rate to detect each type of SDB. For this purpose, ECG signals are divided into 15 minute epochs. These epochs are then subjected to baseline wander removal and R peak detection. An envelope of R peaks is computed to derive R Wave Attenuation (RWA). Concurrently, the heart rate variability (HRV) is also computed. Various biomarkers derived from these trends are combined to develop an algorithm to classify Normal, OSA and CSR epochs. One hundred and five (105) data clips from 15 subjects were used to test the proposed algorithm. It produced detection rates of 93.75%, 100% and 83.3% for Normal, OSA and CSR epochs respectively in case of training set (66 clips). Detection rates of 75%, 85.71% and 70.5% for Normal, OSA and CSR epochs respectively were obtained in case of test set (39 clips).
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PMID:ECG biomarkers for simultaneous detection of obstructive sleep apnea and Cheyne-Stokes breathing. 1800 40

We report that combining the interbeat heart rate as measured by the RR interval (RR) and R-peak envelope (RPE) derived from R-peak of ECG waveform may significantly improve the detection of sleep disordered breathing (SDB) from single lead ECG recording. The method uses textural features extracted from normalized gray-level cooccurrence matrices of the time frequency plots of HRV or RPE sequences. An optimum subset of textural features is selected for classification of the records. A multi-layer perceptron (MLP) serves as a classifier. To evaluate the performance of the proposed method, single Lead ECG recordings from 7 normal subjects and 7 obstructive sleep apnea patients were used. With 500 randomized Monte-Carlo simulations, the average training sensitivity, specificity and accuracy were 100.0%, 99.9%, and 99.9%, respectively. The mean testing sensitivity, specificity and accuracy were 99.0%, 96.7%, and 97.8%, respectively.
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PMID:Sleep disordered breathing detection using heart rate variability and R-peak envelope spectrogram. 1996 46

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.
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PMID:Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVETand Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram. 3226 Apr 36