Effects of Touchscreen Media Use on Toddlers' Sleep: Insights from Longtime ECG Monitoring

Wearable biomedical sensor technology enables reliable monitoring of physiological data, even in very young children. The purpose of the present study was to develop algorithms for gaining valid physiological indicators of sleep quality in toddlers, using data from an undisturbing and easy-to-use wearable device. The study further reports the application of this technique to the investigation of potential impacts of early touchscreen media use. Toddlers' touchscreen media use is of strong interest for parents, educators, and researchers. Mostly, negative effects of media use are assumed, among them, disturbances of sleep and impairments of learning and development. In 55 toddlers (32 girls, 23 boys; 27.4 ± 4.9 months; range: 16-37 months), ECG monitoring was conducted for a period of 30 (±3) h. Parents were asked about their children's touchscreen media use and they rated their children's sleep quality. The use of touchscreen media predicted the physiologically determined quality of sleep but not parent-reported sleep quality (such as sleep onset latency). Greater heart rate differences between restless sleep phases and restful sleep indicated poorer nighttime recovery in children with more frequent use of touchscreen media. The study demonstrates that the expert analysis of the ECG during sleep is a potent tool for the estimation of sleep quality in toddlers.

How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG

Recent developments in noninvasive electrocardiogram (ECG) monitoring with small, wearable sensors open the opportunity to record high-quality ECG over many hours in an easy and non-burdening way. However, while their recording has been tremendously simplified, the interpretation of heart rate variability (HRV) data is a more delicate matter. The aim of this paper is to supply detailed methodological discussion and new data material in order to provide a helpful notice of HRV monitoring issues depending on recording conditions and study populations. Special consideration is given to the monitoring over long periods, across periods with different levels of activity, and in adults versus children. Specifically, the paper aims at making users aware of neglected methodological limitations and at providing substantiated recommendations for the selection of appropriate HRV variables and their interpretation. To this end, 30-h HRV data of 48 healthy adults (18–40 years) and 47 healthy toddlers (16–37 months) were analyzed in detail. Time-domain, frequency-domain, and nonlinear HRV variables were calculated after strict signal preprocessing, using six different high-frequency band definitions including frequency bands dynamically adjusted for the individual respiration rate. The major conclusion of the in-depth analyses is that for most applications that implicate long-term monitoring across varying circumstances and activity levels in healthy individuals, the time-domain variables are adequate to gain an impression of an individual’s HRV and, thus, the dynamic adaptation of an organism’s behavior in response to the ever-changing demands of daily life. The sound selection and interpretation of frequency-domain variables requires considerably more consideration of physiological and mathematical principles. For those who prefer using frequency-domain variables, the paper provides detailed guidance and recommendations for the definition of appropriate frequency bands in compliance with their specific recording conditions and study populations.

A novel approach towards non-obstructive detection and classification of COPD using ECG derived respiration

The alarming rate of mortality and disability due to Chronic Obstructive Pulmonary Disease (COPD) has become a serious health concern worldwide. The progressive nature of this disease makes it inevitable to detect this disease in its early stages, leads to a greater demand for developing non-obstructive and reliable technology for COPD detection. The use of highly patient-effort dependent, time-consuming, and expensive methods are some major inherent limitations of previous techniques. Lack of knowledge about the disease and inadequacy of proper diagnostic tool for early detection of COPD is another reason behind the 3rd leading cause of death worldwide. For this reason, this study aims to explore the utility of ECG Derived Respiration (EDR) for classification between COPD patients and normal healthy subjects as EDR can be easily extracted from ECG. ECG and respiration signals collected from 30 normal and 30 COPD subjects were analysed. Error calculation and statistical analysis were performed to observe the similarity between original respiration and EDR signal. The morphological pattern changes of respiration and EDR signals were analysed and three different features were extracted from those. Classification was performed by different classifiers employing Decision Tree, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Apart from obtaining comparable classification performance it was seen that EDR has better potential than the original respiration signal for classification of COPD from normal population.

ECG-derived Cheyne-Stokes respiration and periodic breathing in healthy and hospitalized populations

BACKGROUND

Cheyne-Stokes respiration (CSR) has been investigated primarily in outpatients with heart failure. In this study we compare CSR and periodic breathing (PB) between healthy and cardiac groups.

METHODS

We compared CSR and PB, measured during 24 hr of continuous 12-lead electrocardiographic (ECG) Holter recording, in a group of 90 hospitalized patients presenting to the emergency department with symptoms suggestive of acute coronary syndrome (ACS) to a group of 100 healthy ambulatory participants. We also examined CSR and PB in the 90 patients presenting with ACS symptoms, divided into a group of 39 (43%) with confirmed ACS, and 51 (57%) with a cardiac diagnosis but non-ACS. SuperECG software was used to derive respiration and then calculate CSR and PB episodes from the ECG Holter data. Regression analyses were used to analyze the data. We hypothesized SuperECG software would differentiate between the groups by detecting less CSR and PB in the healthy group than the group of patients presenting to the emergency department with ACS symptoms.

RESULTS

Hospitalized patients with suspected ACS had 7.3 times more CSR episodes and 1.6 times more PB episodes than healthy ambulatory participants. Patients with confirmed ACS had 6.0 times more CSR episodes and 1.3 times more PB episodes than cardiac non-ACS patients.

CONCLUSION

Continuous 12-lead ECG derived CSR and PB appear to differentiate between healthy participants and hospitalized patients.

Development of three methods for extracting respiration from the surface ECG: a review

BACKGROUND

Respiration rate (RR) is a critical vital sign that can be monitored to detect acute changes in patient condition (e.g., apnea) and potentially provide an early warning of impending life-threatening deterioration. Monitoring respiration signals is also critical for detecting sleep disordered breathing such as sleep apnea. Additionally, analyzing a respiration signal can enhance the quality of medical images by gating image acquisition based on the same phase of the patient's respiratory cycle. Although many methods exist for measuring respiration, in this review we focus on three ECG-derived respiration techniques we developed to obtain respiration from an ECG signal.

METHODS

The first step in all three techniques is to analyze the ECG to detect beat locations and classify them. 1) The EDR method is based on analyzing the heart axis shift due to respiration. In our method, one respiration waveform value is calculated for each normal QRS complex by measuring the peak to QRS trough amplitude. Compared to other similar EDR techniques, this method does not need removal of baseline wander from the ECG signal. 2) The RSA method uses instantaneous heart rate variability to derive a respiratory signal. It is based on the observed respiratory sinus arrhythmia governed by baroreflex sensitivity. 3) Our EMGDR method for computing a respiratory waveform uses measurement of electromyogram (EMG) activity created by respiratory effort of the intercostal muscles and diaphragm. The ECG signal is high-pass filtered and processed to reduce ECG components and accentuate the EMG signal before applying RMS and smoothing.

RESULTS

Over the last five years, we have performed six studies using the above methods: 1) In 1907 sleep lab patients with >1.5M 30-second epochs, EDR achieved an apnea detection accuracy of 79%. 2) In 24 adult polysomnograms, use of EDR and chest belts for RR computation was compared to airflow RR; mean RR error was EDR: 1.8±2.7 and belts: 0.8±2.1. 3) During cardiac MRI, a comparison of EMGDR breath locations to the reference abdominal belt signal yielded sensitivity/PPV of 94/95%. 4) Another comparison study for breath detection during MRI yielded sensitivity/PPV pairs of EDR: 99/97, RSA: 79/78, and EMGDR: 89/86%. 5) We tested EMGDR performance in the presence of simulated respiratory disease using CPAP to produce PEEP. For 10 patients, no false breath waveforms were generated with mild PEEP, but they appeared in 2 subjects at high PEEP. 6) A patient monitoring study compared RR computation from EDR to impedance-derived RR, and showed that EDR provides a near equivalent RR measurement with reduced hardware circuitry requirements.