Semi-Automated Nasal PAP Mask Sizing using Facial Photographs

We present a semi-automated system for sizing nasal Positive Airway Pressure (PAP) masks based upon a neural network model that was trained with facial photographs of both PAP mask users and non-users. It demonstrated an accuracy of 72% in correctly sizing a mask and 96% accuracy sizing to within 1 mask size group. The semi-automated system performed comparably to sizing from manual measurements taken from the same images which produced 89% and 100% accuracy respectively.Comment: 4 pages, 3 figures, 4 tables, IEEE Engineering Medicine and Biology Conference 201

A Switching Feature Extraction System for ECG Heartbeat Classification

Abstract This study compared Introduction The electrocardiogram (ECG) is a non-invasive test that can be used to detect arrhythmias. To successfully capture some arrhythmias up to a month of ECG activity may need to be recorded. Detection of non-lifethreatening arrhythmias is an important area of study as many of these arrhythmias may require therapy to prevent further problems. A characteristic of many arrhythmias is that they appear as sequences of heartbeats with unusual timing or ECG waveshape. The rhythm of the ECG signal can be determined by knowing the classification of consecutive heartbeats in the signal Automated processing of the annotation of beat types is helpful to the clinician as it may save many hours of tedious work manually annotating the beat types of multiday ECG recordings. There are numerous publications on ECG beat classification e.g. Methods Data Data from the 48 recordings of the MIT-BIH arrhythmia database The data is bandpass filtered at 0.1-100Hz and sampled at 360Hz. There are 109,492 labeled ventricular beats from 15 different heartbeat types which were remapped to the five AAMI heartbeat classes We note the error of mapping the atrial escape beats and nodal (junctional) escape beats to the normal class in After remapping, there were five heartbeat classes. Class N contained beats originating in the sinus node (normal and bundle branch block beat types), class S contained supraventricular ectopic beats (SVEB), class V contained ventricular ectopic beats (VEB), class F contained beats that result from fusing normal and VEBs, and class Q contained unknown beats including paced beats. ISSN 2325-8861 Computing in Cardiology 2013; 40:955-958. 95

Heart rate variability during wakefulness as a marker of obstructive sleep apnea severity.

Study objectives: Patients with obstructive sleep apnea (OSA) exhibit heterogeneous heart rate variability (HRV) during wakefulness and sleep. We investigated the influence of OSA severity on HRV parameters during wakefulness in a large international clinical sample. Methods: 1247 subjects (426 without OSA and 821 patients with OSA) were enrolled from the Sleep Apnea Global Interdisciplinary Consortium. HRV parameters were calculated during a 5-minute wakefulness period with spontaneous breathing prior to the sleep study, using time-domain, frequency-domain and nonlinear methods. Differences in HRV were evaluated among groups using analysis of covariance, controlling for relevant covariates. Results: Patients with OSA showed significantly lower time-domain variations and less complexity of heartbeats compared to individuals without OSA. Those with severe OSA had remarkably reduced HRV compared to all other groups. Compared to non-OSA patients, those with severe OSA had lower HRV based on SDNN (adjusted mean: 37.4 vs. 46.2 ms; p < 0.0001), RMSSD (21.5 vs. 27.9 ms; p < 0.0001), ShanEn (1.83 vs. 2.01; p < 0.0001), and Forbword (36.7 vs. 33.0; p = 0.0001). While no differences were found in frequency-domain measures overall, among obese patients there was a shift to sympathetic dominance in severe OSA, with a higher LF/HF ratio compared to obese non-OSA patients (4.2 vs. 2.7; p = 0.009). Conclusions: Time-domain and nonlinear HRV measures during wakefulness are associated with OSA severity, with severe patients having remarkably reduced and less complex HRV. Frequency-domain measures show a shift to sympathetic dominance only in obese OSA patients. Thus, HRV during wakefulness could provide additional information about cardiovascular physiology in OSA patients. Clinical trial information: A Prospective Observational Cohort to Study the Genetics of Obstructive Sleep Apnea and Associated Co-Morbidities (German Clinical Trials Register - DKRS, DRKS00003966) https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00003966. Keywords: autonomic nervous activity; frequency domain analysis; heart rate variability; nonlinear dynamic analysis; obstructive sleep apnea; time domain analysis; wakefulness.Peer reviewe

Heartbeat classification system using adaptive learning from selected beats

An adaptive system for the automatic processing of the electrocardiogram for the classification of heartbeats into beat classes that learns from selected beats is presented. A first set of beat labels is produced by the system by processing an incoming recording with an unadapted classifier. The beat labels are then ranked by a confidence measure calculated from the posterior probabilities estimates associated with each beat classification. An expert then validates and if necessary corrects a fraction of the least confident beats of the recording. The system adapts by first training a classifier using the newly annotated beats, and then combining the outputs with the unadapted classifier to produce an adapted classification system. The adapted system then updates the remaining beat labels of the recording. Data was obtained from the heartbeats obtained from the 44 non-pacemaker recordings of the MIT-BIH arrhythmia database classified into one of eleven classes. With no adaptation a classification accuracy of 63% was achieved. By adapting the classifier, classification accuracy could be increased to over 91%. Our results show that a significant boost in classification performance of the system is achieved even when a small number of selected beats are used for adaptation