Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19.

The COVID-19 disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become one of the worst global pandemics of the century. Wearable devices are well suited for continuously measuring heart rate. Here we show that the Resting Heart Rate is modified for several weeks following a COVID-19 infection. The Resting Heart Rate shows 3 phases: 1) elevated during symptom onset, with average peak increases relative to the baseline of 1.8% (3.4%) for females (males), 2) decrease thereafter, reaching a minimum on average ≈13 days after symptom onset, and 3) subsequent increase, reaching a second peak on average ≈28 days from symptom onset, before falling back to the baseline ≈112 days from symptom onset. All estimates vary with disease severity.

Measurement of respiratory rate using wearable devices and applications to COVID-19 detection.

We show that heart rate enabled wearable devices can be used to measure respiratory rate. Respiration modulates the heart rate creating excess power in the heart rate variability at a frequency equal to the respiratory rate, a phenomenon known as respiratory sinus arrhythmia. We isolate this component from the power spectral density of the heart beat interval time series, and show that the respiratory rate thus estimated is in good agreement with a validation dataset acquired from sleep studies (root mean squared error = 0.648 min-1, mean absolute error = 0.46 min-1, mean absolute percentage error = 3%). We use this respiratory rate algorithm to illuminate two potential applications (a) understanding the distribution of nocturnal respiratory rate as a function of age and sex, and (b) examining changes in longitudinal nocturnal respiratory rate due to a respiratory infection such as COVID-19. 90% of respiratory rate values for healthy adults fall within the range 11.8-19.2 min-1 with a mean value of 15.4 min-1. Respiratory rate is shown to increase with nocturnal heart rate. It also varies with BMI, reaching a minimum at 25 kg/m2, and increasing for lower and higher BMI. The respiratory rate decreases slightly with age and is higher in females compared to males for age <50 years, with no difference between females and males thereafter. The 90% range for the coefficient of variation in a 14 day period for females (males) varies from 2.3-9.2% (2.3-9.5%) for ages 20-24 yr, to 2.5-16.8% (2.7-21.7%) for ages 65-69 yr. We show that respiratory rate is often elevated in subjects diagnosed with COVID-19. In a 7 day window from D-1 to D+5 (where D0 is the date when symptoms first present, for symptomatic individuals, and the test date for asymptomatic cases), we find that 36.4% (23.7%) of symptomatic (asymptomatic) individuals had at least one measurement of respiratory rate 3 min-1 higher than the regular rate.

Assessment of physiological signs associated with COVID-19 measured using wearable devices.

Respiration rate, heart rate, and heart rate variability (HRV) are some health metrics that are easily measured by consumer devices, which can potentially provide early signs of illness. Furthermore, mobile applications that accompany wearable devices can be used to collect relevant self-reported symptoms and demographic data. This makes consumer devices a valuable tool in the fight against the COVID-19 pandemic. Data on 2745 subjects diagnosed with COVID-19 (active infection, PCR test) were collected from May 21 to September 11, 2020, consisting of PCR positive tests conducted between February 16 and September 9. Considering male (female) participants, 11.9% (11.2%) of the participants were asymptomatic, 48.3% (47.8%) recovered at home by themselves, 29.7% (33.7%) recovered at home with the help of someone else, 9.3% (6.6%) required hospitalization without ventilation, and 0.5% (0.4%) required ventilation. There were a total of 21 symptoms reported, and the prevalence of symptoms varies by sex. Fever was present in 59.4% of male subjects and in 52% of female subjects. Based on self-reported symptoms alone, we obtained an AUC of 0.82 ± 0.017 for the prediction of the need for hospitalization. Based on physiological signs, we obtained an AUC of 0.77 ± 0.018 for the prediction of illness on a specific day. Respiration rate and heart rate are typically elevated by illness, while HRV is decreased. Measuring these metrics, taken in conjunction with molecular-based diagnostics, may lead to better early detection and monitoring of COVID-19.

Development and validation of a novel non-contact monitor of nocturnal respiration for identifying sleep-disordered breathing in patients with heart failure.

AIMS: At least 50% of patients with heart failure (HF) may have sleep-disordered breathing (SDB). Overnight in-hospital polysomnography (PSG) is considered the gold standard for diagnosis, but a lack of access to such testing contributes to under-diagnosis of SDB. Therefore, there is a need for simple and reliable validated methods to aid diagnosis in patients with HF. The aim of this study was to investigate the accuracy of a non-contact type IV screening device, SleepMinderTM (SM), compared with in-hospital PSG for detecting SDB in patients with HF. METHODS AND RESULTS: The study included 75 adult patients with systolic HF and suspected SDB who underwent simultaneous PSG and SM recordings. An algorithm was developed from the SM signals, using digital signal processing and pattern recognition techniques to calculate the SM apnoea-hypopnoea index (AHI). This was then compared with expert-scored PSGAHI . The SM algorithm had 70% sensitivity and 89% specificity for identifying patients with clinically significant SDB (AHI ≥ 15/h). At this threshold, it had a positive likelihood ratio of 6.3 and a negative likelihood ratio of 0.16. The overall accuracy of the SMAHI algorithm was 85.8% as shown by the area under a receiver operator characteristic curve. The mean AHI with SM was 3.8/h (95% confidence interval 0.5-7.1) lower than that with PSG. CONCLUSIONS: The accuracy of the non-contact type IV screening device SM is good for clinically significant SDB in patients with systolic HF and could be considered as a simple first step in the diagnostic pathway.

A comparison of radio-frequency biomotion sensors and actigraphy versus polysomnography for the assessment of sleep in normal subjects.

PURPOSE: This paper aims to compare the absolute performance of three noncontact sleep measurement devices for measuring sleep parameters in normal subjects against polysomnography and to assess their relative performance. METHODS: The devices investigated were two noncontact radio-frequency biomotion sensors (SleepMinder (SM) and SleepDesign (HSL-101)) and an actigraphy-based system (Actiwatch). Overnight polysomnography measurements were carried out in 20 normal subjects, with simultaneous assessment of sleep parameters using the three devices. The parameters measured included total sleep time (TST), sleep efficiency (SE), sleep-onset latency (SOL), and wake-after-sleep onset (WASO). The per-epoch agreement level for sleep/wake distinction was evaluated. RESULTS: The TSTs reported by the three devices were 426 ± 34, 434 ± 22, and 441 ± 16 min, for the SM, HSL-101, and Actiwatch, respectively, against polysomnogram (PSG)-reported TST of 391 ± 49 min. The SOLs were 10 ± 10, 5 ± 6, and 3 ± 2 min for the SM, HSL-101 and Actiwatch, respectively against PSG SOL of 19 ± 13 min. The WASO times were 46 ± 33, 43 ± 22, and 38 ± 17 min, as against PSG-reported 69 ± 46 min. All three devices had a statistically significant bias to overestimate sleep time and underestimate WASO and SOL compared with PSG. The performance of the three devices was basically equivalent, with only minor interdevice differences. The overall per-epoch agreement levels were 86 % for the SM, 86 % for the HSL-101, and 85 % for the Actiwatch. CONCLUSIONS: Noncontact biomotion approaches to sleep measurement provided reasonable estimates of TST, but with a bias to over-estimation of sleep. The radio-frequency biomotion sensors provided similar accuracies for sleep/wake determination in normal subjects as the actigraph used in this study and slightly improved estimates of TST, SOL, and WASO.

Wireless sleep measurement: sensing sleep and breathing patterns using radio-frequency sensors.

Despite the fact that we spend nearly one third of our lives asleep, surprisingly little was known about sleep until the 20th century. Now, sleep medicine is firmly established as a significant branch of medical practice, taking its roots strongly from the work of Nathaniel Kleitman and colleagues at the University of Chicago in the 1950s. They were the first to show the existence of rapid eye movement (REM) sleep?commonly associated with ?dreaming?and began the process of opening our eyes to the complex physiological processes that occur during sleep.

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.

Comparison of a novel non-contact biomotion sensor with wrist actigraphy in estimating sleep quality in patients with obstructive sleep apnoea.

Ambulatory monitoring is of major clinical interest in the diagnosis of obstructive sleep apnoea syndrome. We compared a novel non-contact biomotion sensor, which provides an estimate of both sleep time and sleep-disordered breathing, with wrist actigraphy in the assessment of total sleep time in adult humans suspected of obstructive sleep apnoea syndrome. Both systems were simultaneously evaluated against polysomnography in 103 patients undergoing assessment for obstructive sleep apnoea syndrome in a hospital-based sleep laboratory (84 male, aged 55 ± 14 years and apnoea-hypopnoea index 21 ± 23). The biomotion sensor demonstrated similar accuracy to wrist actigraphy for sleep/wake determination (77.3%: biomotion; 76.5%: actigraphy), and the biomotion sensor demonstrated higher specificity (52%: biomotion; 34%: actigraphy) and lower sensitivity (86%: biomotion; 94%: actigraphy). Notably, total sleep time estimation by the biomotion sensor was superior to actigraphy (average overestimate of 10 versus 57 min), especially at a higher apnoea-hypopnoea index. In post hoc analyses, we assessed the improved apnoea-hypopnoea index accuracy gained by combining respiratory measurements from polysomnography for total recording time (equivalent to respiratory polygraphy) with total sleep time derived from actigraphy or the biomotion sensor. Here, the number of misclassifications of obstructive sleep apnoea severity compared with full polysomnography was reduced from 10/103 (for total respiratory recording time alone) to 7/103 and 4/103 (for actigraphy and biomotion sensor total sleep time estimate, respectively). We conclude that the biomotion sensor provides a viable alternative to actigraphy for sleep estimation in the assessment of obstructive sleep apnoea syndrome. As a non-contact device, it is suited to longitudinal assessment of sleep, which could also be combined with polygraphy in ambulatory studies.

Assessment of sleep-disordered breathing using a non-contact bio-motion sensor.

Obstructive sleep apnoea is a highly prevalent but under-diagnosed disorder. The gold standard for diagnosis of obstructive sleep apnoea is inpatient polysomnography. This is resource intensive and inconvenient for the patient, and the development of ambulatory diagnostic modalities has been identified as a key research priority. SleepMinder (BiancaMed, NovaUCD, Ireland) is a novel, non-contact, bedside sensor, which uses radio-waves to measure respiration and movement. Previous studies have shown it to be effective in measuring sleep and respiration. We sought to assess its utility in the diagnosis of obstructive sleep apnoea. SleepMinder and polysomnographic assessment of sleep-disordered breathing were performed simultaneously on consecutive subjects recruited prospectively from our sleep clinic. We assessed the diagnostic accuracy of SleepMinder in identifying obstructive sleep apnoea, and how SleepMinder assessment of the apnoea-hypopnoea index correlated with polysomnography. Seventy-four subjects were recruited. The apnoea-hypopnoea index as measured by SleepMinder correlated strongly with polysomnographic measurement (r = 0.90; P ≤ 0.0001). When a diagnostic threshold of moderate-severe (apnoea-hypopnoea index ≥15 events h(-1) ) obstructive sleep apnoea was used, SleepMinder displayed a sensitivity of 90%, a specificity of 92% and an accuracy of 91% in the diagnosis of sleep-disordered breathing. The area under the curve for the receiver operator characteristic was 0.97. SleepMinder correctly classified obstructive sleep apnoea severity in the majority of cases, with only one case different from equivalent polysomnography by more than one diagnostic class. We conclude that in an unselected clinical population undergoing investigation for suspected obstructive sleep apnoea, SleepMinder measurement of sleep-disordered breathing correlates significantly with polysomnography.

Sleep/wake measurement using a non-contact biomotion sensor.

We studied a novel non-contact biomotion sensor, which has been developed for identifying sleep/wake patterns in adult humans. The biomotion sensor uses ultra low-power reflected radiofrequency waves to determine the movement of a subject during sleep. An automated classification algorithm has been developed to recognize sleep/wake states on a 30-s epoch basis based on the measured movement signal. The sensor and software were evaluated against gold-standard polysomnography on a database of 113 subjects [94 male, 19 female, age 53±13years, apnoea-hypopnea index (AHI) 22±24] being assessed for sleep-disordered breathing at a hospital-based sleep laboratory. The overall per-subject accuracy was 78%, with a Cohen's kappa of 0.38. Lower accuracy was seen in a high AHI group (AHI >15, 63 subjects) than in a low AHI group (74.8% versus 81.3%); however, most of the change in accuracy can be explained by the lower sleep efficiency of the high AHI group. Averaged across subjects, the overall sleep sensitivity was 87.3% and the wake sensitivity was 50.1%. The automated algorithm slightly overestimated sleep efficiency (bias of +4.8%) and total sleep time (TST; bias of +19min on an average TST of 288min). We conclude that the non-contact biomotion sensor can provide a valid means of measuring sleep-wake patterns in this patient population, and also allows direct visualization of respiratory movement signals.

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.

Physiotherapy for sleep disturbance in chronic low back pain: a feasibility randomised controlled trial.

BACKGROUND: Sleep disturbance is becoming increasingly recognised as a clinically important symptom in people with chronic low back pain (CLBP, low back pain >12 weeks), associated with physical inactivity and depression. Current research and international clinical guidelines recommend people with CLBP assume a physically active role in their recovery to prevent chronicity, but the high prevalence of sleep disturbance in this population may be unknowingly limiting their ability to participate in exercise-based rehabilitation programmes and contributing to poor outcomes. There is currently no knowledge concerning the effectiveness of physiotherapy on sleep disturbance in people with chronic low back pain and no evidence of the feasibility of conducting randomized controlled trials that comprehensively evaluate sleep as an outcome measure in this population. METHODS/DESIGN: This study will evaluate the feasibility of a randomised controlled trial (RCT), exploring the effects of three forms of physiotherapy (supervised general exercise programme, individualized walking programme and usual physiotherapy, which will serve as the control group) on sleep quality in people with chronic low back pain. A presenting sample of 60 consenting patients will be recruited in the physiotherapy department of Beaumont Hospital, Dublin, Ireland, and randomly allocated to one of the three groups in a concealed manner. The main outcomes will be sleep quality (self-report and objective measurement), and self-reported functional disability, pain, quality of life, fear avoidance, anxiety and depression, physical activity, and patient satisfaction. Outcome will be evaluated at baseline, 3 months and 6 months. Qualitative telephone interviews will be embedded in the research design to obtain feedback from a sample of participants' about their experiences of sleep monitoring, trial participation and interventions, and to inform the design of a fully powered future RCT. Planned analysis will explore trends in the data, effect sizes and clinically important effects (quantitative data), and thematic analysis (qualitative data). DISCUSSION: This study will evaluate the feasibility of a randomised controlled trial exploring the effects of three forms of physiotherapy (supervised general exercise programme, individualized walking programme and usual physiotherapy, which will serve as the control group) on sleep quality in people with chronic low back pain. TRIAL REGISTRATION: Current controlled trial ISRCTN54009836.

Towards using photo-plethysmogram amplitude to measure blood pressure during sleep.

There is considerable interest in non-intrusive and reliable continuous ambulatory blood pressure measurement systems. Pulse amplitude is the peak to trough amplitude of the photo-plethysmogram signal. We compared pulse amplitude with a currently popular parameter, the pulse arrival time (PAT), for estimating continuous systolic blood pressure (SBP). Overnight sleep data from 18 young, healthy subjects (14 M 4 F, age 24+/-5 years, BMI 23.8+/-4.0 kg/m2) was analyzed. We found that pulse amplitude was more effective than PAT for estimating SBP during sleep. Mean coherence between pulse amplitude and SBP was significantly stronger than that for PAT [p<0.001, 95% CI: 0.21-0.25 (finger), 0.11-0.14 (wrist)]. Correlation between pulse amplitude and SBP was significantly stronger than that for PAT [p<0.001, 95% CI: 0.46-0.53 (finger), 0.13-0.20 (wrist)]. SBP estimation errors were significantly lower using pulse amplitude [p<0.001, 95% CI: -1.55 to -1.29 mmHg (finger), -0.53 to -0.36 mmHg (wrist)]. We also found that while pulse amplitude was closely related to SBP, the relationship weakened during and around REM sleep (ANOVA of REM, transitional Wake-REM and transitional REM-Sleep versus other sleep states: F=24.7, p<0.001). These results suggest that pulse amplitude is potentially a more suitable measure than pulse arrival time for estimating continuous blood pressure.

SleepMinder: an innovative contact-free device for the estimation of the apnoea-hypopnoea index.

We describe an innovative sensor technology (SleepMinder) for contact-less and convenient measurement of sleep and breathing in the home. The system is based on a novel non-contact biomotion sensor and proprietary automated analysis software. The biomotion sensor uses an ultra low-power radio-frequency transceiver to sense the movement and respiration of a subject. Proprietary software performs a variety of signal analysis tasks including respiration analysis, sleep quality measurement and sleep apnea assessment. This paper measures the performance of SleepMinder as a device for the monitoring of sleep-disordered breathing (SDB) and the provision of an estimate of the apnoea-hypopnoea index (AHI). The SleepMinder was tested against expert manually scored PSG data of patients gathered in an accredited sleep laboratory. The comparison of SleepMinder to this gold standard was performed across overnight recordings of 129 subjects with suspected SDB. The dataset had a wide demographic profile with the age ranging between 20 and 81 years. Body weight included subjects with normal weight through to the very obese (Body Mass Index: 21-44 kg/m(2)). SDB severity ranged from subjects free of SDB to those with severe SDB (AHI: 0.8-96 events/hours). SleepMinder's AHI estimation has a correlation of 91% and can detect clinically significant SDB (AHI>15) with a sensitivity of 89% and a specificity of 92%.

Objective and subjective assessment of sleep in chronic low back pain patients compared with healthy age and gender matched controls: a pilot study.

BACKGROUND: While approximately 70% of chronic low back pain (CLBP) sufferers complain of sleep disturbance, current literature is based on self report measures which can be prone to bias and no objective data of sleep quality, based exclusively on CLBP are available. In accordance with the recommendations of The American Sleep Academy, when measuring sleep, both subjective and objective assessments should be considered as the two are only modestly correlated, suggesting that each modality assesses different aspects of an individual's sleep experience. Therefore, the purpose of this study was to expand previous research into sleep disturbance in CLBP by comparing objective and subjective sleep quality in participants with CLBP and healthy age and gender matched controls, to identify correlates of poor sleep and to test logistics and gather information prior to a larger study. METHODS: 15 CLBP participants (mean age = 43.8 years (SD = 11.5), 53% female) and 15 healthy controls (mean age = 41.5 years (SD = 10.6), 53% female) consented. All participants completed the Pittsburgh Sleep Quality Index, Insomnia Severity Index, Pittsburgh Sleep Diary and the SF36v2. CLBP participants also completed the Oswestry Disability Index. Sleep patterns were assessed over three consecutive nights using actigraphy. Total sleep time (TST), sleep efficiency (SE), sleep latency onset (SL) and number of awakenings after sleep onset (WASO) were derived. Statistical analysis was conducted using unrelated t-tests and Pearson's product moment correlation co-efficients. RESULTS: CLBP participants demonstrated significantly poorer overall sleep both objectively and subjectively. They demonstrated lower actigraphic SE (p = .002) and increased WASO (p = .027) but no significant differences were found in TST (p = .43) or SL (p = .97). Subjectively, they reported increased insomnia (p =< .001), lower SE (p =< .001) and increased SL (p =< .001) but no difference between TST (p = .827) and WASO (p = .055). Statistically significant associations were found between low back pain (p = .021, r = -.589), physical health (p = .003, r = -.713), disability levels (p = .025, r = .576), and subjective sleep quality in the CLBP participants but not with actigraphy. CONCLUSION: CLBP participants demonstrated poorer overall sleep, increased insomnia symptoms and less efficient sleep. Further investigation using a larger sample size and a longer period of sleep monitoring is ongoing.

Multimodal detection of sleep apnoea using electrocardiogram and oximetry signals.

A method for the detection of sleep apnoea, suitable for use in the home environment, is presented. The method automatically analyses night-time electrocardiogram (ECG) and oximetry recordings and identifies periods of normal and sleep-disordered breathing (SDB). The SDB is classified into one of six classes: obstructive, mixed and central apnoeas, and obstructive, mixed and central hypopnoeas. It also provides an estimated apnoea, hypopnoea and apnoea-hypopnoea index. The basis of the method is a pattern recognition system that identifies episodes of apnoea by analysing the heart variability, an ECG-derived respiration signal and blood oximetry values. The method has been tested on 183 subjects with a range of apnoea severities who have undergone a full overnight polysomnogram study. The results show that the method separates control subjects from subjects with clinically significant sleep apnoea with a specificity of 83 per cent and sensitivity of 95 per cent. These results demonstrate that home-based screening for sleep apnoea is a viable alternative to hospital-based tests with the added benefit of low cost and minimal waiting times.

A portable automated assessment tool for sleep apnea using a combined Holter-oximeter.

STUDY OBJECTIVES: Resource limitations have raised interest in portable monitoring systems that can be used by specialist sleep physicians as part of an overall strategy to improve access to the diagnosis of sleep apnea. This study validates a combined electrocardiogram and oximetry recorder (Holter-oximeter) against simultaneous polysomnography for detection of sleep apnea. DESIGN: Prospective study. SETTING: A dedicated sleep disorders unit. PARTICIPANTS: 59 adults presenting for evaluation of suspected sleep apnea. INTERVENTIONS: NA. MEASUREMENTS AND RESULTS: An automated algorithm previously developed for sleep apnea detection was applied to the electrocardiogram and oximetry measurements. The algorithm provides (a) epoch-by-epoch estimates of apnea occurrence and (b) estimates of overall per-subject AHI. Using separate thresholds of AHI > or =15 and AHI <5 for defining clinically significant and insignificant sleep apnea, sensitivity, specificity, and likelihood ratios, conditional on positive or negative (but not indeterminate) test results were used to assess agreement between the proposed system and polysomnography. Sensitivity of 95.8% and specificity of 100% was achieved. Positive and negative likelihood ratios were >20 and 0.04 respectively, with 16.7% of subjects having intermediate test results (AHI 5-14/h). Regardless ofAHI, 85.3% of respiratory events were correctly annotated on an epoch-by-epoch basis. AHI underestimation bias was 0.9/h, and the antilogs of log-transformed limits of agreement were 0.3 and 2.7. Correlation between estimated and reference AHI was 0.95 (P <0.001). CONCLUSION: Combined Holter-oximeter monitoring compares well with polysomnography for identifying sleep apnea in an attended setting and is potentially suitable for home-based automated assessment of sleep apnea in a population suspected of having sleep apnea.

Combined electrocardiogram and photoplethysmogram measurements as an indicator of objective sleepiness.

There is considerable interest in unobtrusive and portable methods of monitoring sleepiness outside the laboratory setting. This study evaluates the usefulness of combined electrocardiogram (ECG) and photoplethysmogram (PPG) measurements for estimating psychomotor vigilance. The psychomotor vigilance test (PVT) was performed at various points over the course of a day, and one channel each of ECG and PPG was recorded simultaneously. Features derived from ECG and PPG were entered into multiple linear regression models to estimate PVT values. A double-loop, subject-independent validation scheme was used to develop and validate the models. We show that features obtained from the RR interval were reasonably useful for estimating absolute PVT levels, but were somewhat inadequate for estimating within-subject PVT changes. Combined ECG and PPG measurements appear to be useful for predicting PVT values, and deserve further investigation for portable sleepiness monitoring.

Electrocardiogram recording as a screening tool for sleep disordered breathing.

STUDY OBJECTIVES: Transient changes in heart rate associated with obstructive apneas have been suggested for screening of sleep disordered breathing (SDB). This study prospectively compares the outcomes of an automated ECG-based SDB screening tool with simultaneous polysomnography. METHODS: The previously-developed automated algorithm was applied to a single channel ECG obtained during standard overnight polysomnography (92 subjects) to obtain an apnea-hypopnea index (AHI) estimate. Using AHI thresholds of < 5 and > or =15 to define absence and presence of SDB, respectively, we determined the likelihood ratios of the proposed technique. RESULTS: The automated algorithm achieved positive and negative likelihood ratios of 2.16 and 0.08. Estimated and reference AHI were highly correlated (r = 0.88). Pathologically insignificant arrhythmia in some subjects had no discernible impact on the algorithm. CONCLUSIONS: ECG-based assessment provides a simple but limited means of recognizing subjects with obstructive sleep apnea.