Electrocardiogram derived respiration from QRS slopes

Single-lead ECG based autonomic nervous system assessment for meditation monitoring

We propose a single-lead ECG-based heart rate variability (HRV) analysis algorithm to quantify autonomic nervous system activity during meditation. Respiratory sinus arrhythmia (RSA) induced by breathing is a dominant component of HRV, but its frequency depends on an individual's breathing speed. To address this RSA issue, we designed a novel HRV tachogram decomposition algorithm and new HRV indices. The proposed method was validated by using a simulation, and applied to our experimental (mindfulness meditation) data and the WESAD open-source data. During meditation, our proposed HRV indices related to vagal and sympathetic tones were significantly increased (p < 0.000005) and decreased (p < 0.000005), respectively. These results were consistent with self-reports and experimental protocols, and identified parasympathetic activation and sympathetic inhibition during meditation. In conclusion, the proposed method successfully assessed autonomic nervous system activity during meditation when respiration influences disrupted classical HRV. The proposed method can be considered a reliable approach to quantify autonomic nervous system activity.

Assessing heart disease using a novel magnetocardiography device

The aim of this paper is to present the use of a portable, unshielded magnetocardiograph (MCG) and identify key characteristics of MCG scans that could be used in future studies to identify parameters that are sensitive to cardiac pathology. We recruited 50 patients with confirmed myocardial infarction (MI) within the past 12 weeks and 46 volunteers with no history of cardiac disease. A set of 38 parameters were extracted from MCG features including both signals from the sensor array and from magnetic images obtained from the device and principal component analysis was used to concentrate the information contained in these parameters into uncorrelated predictors. Linear fits of these parameters were then used to examine the ability of MCG to distinguish between sub-groups of patients. In the fist instance, the primary aim of this study was to ensure that MCG has a basic ability to separate a highly polarised patient group (young controls from post infarction patients) and to identify parameters that could be used in future studies to build a formal diagnostic tool kit. Parameters that parameterised left ventricular ejection fraction (LVEF) were identified and an example is presented to show differential low and high ejection fractions.

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.

Deriving respiration from high resolution 12-channel-ECG during cycling exercise

Monitoring of cardiac and respiratory activity, is essential in several clinical interventions like bicycle ergometries. The respiration signal can be derived from the ECG if it is not recorded itself (ECG derived respiration, EDR). In this study, we tried to reconstruct breathing rates (BR) from stress test high resolution 12-channel-ECGs in nine healthy subjects using higher order central moments. A mean absolute error per subjects of 2.9/min and relatively high correlation (rp = 0.85) and concordance coefficient (rc = 0.79) indicated a quite accurate reproduction of respiratory activity. The analysis of the different test stages revealed an increase of BR errors while subjects were effortful cycling compared to rest. During incremental cycling exercise test the mean absolute error per subjects was 3.4/min. Compared to the results reported in other studies at rest in supine position, this seems adequately accurate. In conclusion, our results indicate that EDR using higher order central moments is suited for monitoring BR during physical activity.

Estimation of a respiratory signal from a single-lead ECG using the 4th order central moments

For a variety of clinical applications like magnetic resonance imaging (MRI) the monitoring of vital signs is a common standard in clinical daily routine. Besides the electrocardiogram (ECG), the respiratory activity is an important vital parameter and might reveal pathological changes. Thoracic movement and the resulting impedance change between ECG electrodes enable the estimation of the respiratory signal from the ECG. This ECG-derived respiration (EDR) can be used to calculate the breathing rate without the need for additional devices or monitoring modules. In this paper a new method is presented to estimate the respiratory signal from a single-lead ECG. The 4th order central moments was used to estimate the EDR signal exploiting the change of the R-wave slopes induced by respiration. This method was compared with two approaches by analyzing the Fantasia database from www.physionet.org. Furthermore, the ECG signals of 24 healthy subjects placed in an 3 T MR-scanner were acquired.