Redmond Shouldice

An Evaluation of a Non-contact Biomotion Sensor with Actimetry

Actimetry is a widely accepted technology for the diagnosis and monitoring of sleep disorders such as insomnia, circadian sleep/wake disturbance, and periodic leg movement. In this study we investigate a very sensitive non-contact biomotion sensor to measure actimetry and compare its performance to wrist-actimetry. A data corpus consisting of twenty subjects (ten normals, ten with sleep disorders) was collected in the unconstrained home environment with simultaneous non-contact sensor and ActiWatch actimetry recordings. The aggregated length of the data is 151 hours. The non-contact sensor signal was mapped to actimetry using 30 second epochs and the level of agreement with the ActiWatch actimetry determined. Across all twenty subjects, the sensitivity and specificity was 79% and 75% respectively. In addition, it was shown that the non-contact sensor can also measure breathing and breathing modulations. The results of this study indicate that the non-contact sensor may be a highly convenient alternative to wrist-actimetry as a diagnosis and screening tool for sleep studies. Furthermore, as the non- contact sensor measures breathing modulations, it can additionally be used to screen for respiratory disturbances in sleep caused by sleep apnea and COPD.

Modulating effect of respiration on atrioventricular conduction time assessed using PR interval variation.

Respiratory sinus arrhythmia (RSA) is the variation of heart rate (or RR interval) in phase with respiration and has been extensively studied. However, the effect of respiration on atrioventricular conduction delay (and hence PR interval length) has not yet received much attention. This work reports on measurements of respiration and associated RR and PR variability, in 11 subjects, assessed through surface electrocardiogram measurements, for both paced and spontaneous respiration in the supine position. A wavelet-based approach was used to extract RR and PR intervals. The accuracy of RR and PR interval measurement was consistent with previously published work. Respiratory atrioventricular conduction delay variability (RCV) was assessed using three techniques: spectral, peak-to-trough and cosinor methods. All measures showed statistically significant variations in PR interval due to respiration during paced respiration at 6 min−1. Of the three measures, cosinor analysis was most reliable in highlighting RCV. Using this measure, statistically significant RCV was seen in ten out of the 11 subjects during paced respiration. The magnitude of the variability was estimated as ±5.9% of the mean PR interval. In spontaneous respiration, statistically significant RCV was seen in approximately half of the subjects, with an estimated variability of ±1.5%. As a control, statistically significant values for RSA were also obtained from the same data, which agreed with previously published measurements. It was concluded that respiration does indeed modulate atrioventricular conduction delay, deep breathing in the supine position accentuates this effect, and cosinor analysis provides a reliable means for quantifying this effect.

PR and PP ECG intervals as indicators of autonomic nervous innervation of the cardiac sinoatrial and atrioventricular nodes

Two different methods to elucidate the dependent or independent nature of the autonomic nervous system influence on the sinoatrial and atrioventricular nodes are considered. ECG PP and PR intervals are extracted from 20 normal, young healthy subjects using a wavelet based technique. The first analysis technique focuses upon a supine to stand transition. This transient event serves to highlight the uncoupled nature of PP and PR interval duration for about five beats, suggesting an independent ANS drive to each node. Secondly, sections of quiet supine and standing data are analyzed. Parametric spectral analysis shows variable LF and HF component behavior across different subjects. The relative distribution of recovery RP intervals versus PR for supine and standing are clearly separate for 10 different subjects and borderline for 5. Large intersubject variation is evident.

Real time breathing rate estimation from a non contact biosensor

An automated real time method for detecting human breathing rate from a non contact biosensor is considered in this paper. The method has low computational and RAM requirements making it well-suited to real-time, low power implementation on a microcontroller. Time and frequency domain methods are used to separate a 15s block of data into movement, breathing or absent states; a breathing rate estimate is then calculated. On a 1s basis, 96% of breaths were scored within 1 breath per minute of expert scored respiratory inductance plethysmography, while 99% of breaths were scored within 2 breaths per minute. When averaged over 30s, as is used in this respiration monitoring system, over 99% of breaths are within 1 breath per minute of the expert score.

Automated Detection of Paroxysmal Atrial Fibrillation from Inter-Heartbeat Intervals

An automated method for detecting episodes of probable paroxysmal atrial fibrillation based on processing blocks of inter-heartbeat intervals is considered. The method has very low computational requirements making it well-suited to near real-time, low power applications. A supervised linear discriminant classifier is used to estimate the likelihood of a block of inter-heartbeat intervals containing paroxysmal atrial fibrillation (PAF). Per block accuracies in separating normal from PAF were 92%, 94%, 100% and 100% when the method was used to process the Physionet MITDB, AFDB, NSRDB and NSR2DB databases respectively.

A pilot study of the nocturnal respiration rates in COPD patients in the home environment using a non-contact biomotion sensor.

Nocturnal respiration rate parameters were collected from 20 COPD subjects over an 8 week period, to determine if changes in respiration rate were associated with exacerbations of COPD. These subjects were primarily GOLD Class 2 to 4, and had been recently discharged from hospital following a recent exacerbation. The respiration rates were collected using a non-contact radio-frequency biomotion sensor which senses respiratory effort and body movement using a short-range radio-frequency sensor. An adaptive notch filter was applied to the measured signal to determine respiratory rate over rolling 15 s segments. The accuracy of the algorithm was initially verified using ten manually-scored 15 min segments of respiration extracted from overnight polysomnograms. The calculated respiration rates were within 1 breath min(-1) for >98% of the estimates. For the 20 subjects monitored, 11 experienced one or more subsequent exacerbation of COPD (ECOPD) events during the 8 week monitoring period (19 events total). Analysis of the data revealed a significant increase in nocturnal respiration rate (e.g. >2 breath min(-1)) prior to many ECOPD events. Using a simple classifier of a change of 1 breath min(-1) in the mode of the nocturnal respiration rate, a predictive rule showed a sensitivity of 63% and specificity of 85% for predicting an exacerbation within a 5 d window. We conclude that it is possible to collect respiration rates reliably in the home environment, and that the respiration rate may be a potential indicator of change in clinical status.

Automatic Detection of Paroxysmal Atrial Fibrillation

The purpose of this chapter is to provide a tutorial level introduction to (a) the physiology and clinical background of paroxysmal (intermittent) atrial fibrillation (PAF), and (b) methods for detection of patterns consistent with AF using electrocardiogram (ECG) processing. The document assumes that the reader is familiar with basic signal processing concepts, but assumes no prior knowledge of AF or pattern classification. A practical implementation of an automatic AF detector is presented; a supervised linear discriminant classifier is used to estimate the likelihood of a block of inter-heartbeat intervals being PAF, with accuracies of 92%, 94%, 100% and 100% when the method was used to process the publically available Physionet (Goldberger et al., 2000) signal databases MITDB, AFDB, NSRDB and NSR2DB respectively.

Reliability of Holter Oximetry for Home Sleep Apnea Testing

Objective To test the reliability of the Holter Oximeter for home testing of obstructive sleep apnea. Previous reports have shown a 96% correlation with simultaneous polysomnography and Holter Oximetry in the sleep laboratory. This study was designed to measure reliability of data obtained at home, as well as to obtain information from patients regarding comfort of the device. Methods A prospective study of 120 consecutive patients (ages 5 to 85) presenting to an otolaryngology practice during a 4-month period with complaints of snoring or sleep apnea symptoms. Device: The Holter Oximeter produces an apnea hypopnea index (AHI) based on an automated processing method of a continuous electrocardiogram and pulse oximeter. The reliability of the test was determined by the number of tests completed without interruption due to patient discomfort, electrode or device failure. Results There was 97% data recovery from the home testing device. Data failure was due to faulty memory cards in the device or surface electrode failure. All patients tolerated wearing the device at home, and there were no voluntary interruptions of the tests by patients. On a discomfort scale of 0 to 10 (0: no discomfort and 10: maximal discomfort), the average discomfort score was 2. Conclusions Holter Oximetry represents a new, easy to use, and reliable device for the home diagnosis of obstructive sleep apnea. It can also be used to measure outcomes for the surgical and non-surgical treatment of obstructive sleep apnea in adults and children.

Methods of quantifying respiratory modulation in human PR electrocardiographic intervals

Respiratory sinus arrhythmia (RSA) is a clinically useful measure of heart rate variability which can be estimated by analyzing RR intervals. Respiratory related variation in atrioventricular nodal conduction (which we term RCV) may also be physiologically and clinically significant. RCV can be calculated through its influence on PR intervals. Two methods of estimating RCV in spontaneous and paced breathing are presented; these are cross spectral coherence and cosinor fitting measures. It was found that both techniques show statistically significant respiratory modulation of RR and PR interval duration, although the magnitude of RSA was found to be greater than RCV as expected. Changing posture from supine to standing caused a statistically significant drop in RSA for spontaneous breathing, but not paced breathing. The postural change also reduced RCV for both spontaneous and paced breathing, but this change was not statistically significant.

Independent autonomic modulation of the sinoatrial and atrioventricular nodes assessed through RR and PR interval variation

The degree of coupling between autonomic nervous system influence on the sinoatrial (SA) and atrioventricular (AV) nodes is considered, using RR and PR intervals as indicators of ANS activity. Seventy minute recordings of electrocardiogram and respiratory measurements were made with data recorded in supine, standing, and sitting positions. Indices of respiratory sinus arrhythmia (RSA) for RR and PR intervals were calculated for 2-minute sections of deep breathing in both supine and standing positions. RSA indices for RR intervals were consistently higher in the supine position; no consistent pattern was seen for RSA in PR intervals. In addition, multiple regression analysis was carried out relating RR, PP, RP, and PR intervals in the three positions under the assumption of closely coupled ANS influence. The measured data was inconsistent with this assumption. We conclude that some independent ANS modulation occurs at the SA and AV nodes.