Heart rate variability, the change in the time intervals between adjacent heartbeats, is an emergent property of interdependent regulatory systems that operates on different time scales to adapt to environmental and psychological challenges. This article briefly reviews neural regulation of the heart and offers some new perspectives on mechanisms underlying the very low frequency rhythm of heart rate variability. Interpretation of heart rate variability rhythms in the context of health risk and physiological and psychological self-regulatory capacity assessment is discussed. The cardiovascular regulatory centers in the spinal cord and medulla integrate inputs from higher brain centers with afferent cardiovascular system inputs to adjust heart rate and blood pressure via sympathetic and parasympathetic efferent pathways. We also discuss the intrinsic cardiac nervous system and the heart-brain connection pathways, through which afferent information can influence activity in the subcortical, frontocortical, and motor cortex areas. In addition, the use of real-time HRV feedback to increase self-regulatory capacity is reviewed. We conclude that the heart's rhythms are characterized by both complexity and stability over longer time scales that reflect both physiological and psychological functional status of these internal self-regulatory systems.

Heart Rate Variability: New Perspectives on Physiological Mechanisms, Assessment of Self-regulatory Capacity, and Health risk.

The invention provides a body-worn vital sign monitor that measures a patient's vital signs (e.g. blood pressure, SpO2, heart rate, respiratory rate, and temperature) while simultaneously characterizing their activity state (e.g. resting, walking, convulsing, falling) and posture (upright, supine). The monitor processes this information to minimize corruption of the vital signs and associated alarms/alerts by motion-related artifacts. It also features a graphical user interface (GUI) rendered on a touchpanel display that facilitates a number of features to simplify and improve patient monitoring and safety in both the hospital and home.

Body-worn vital sign monitor

A survey on ECG analysis

Heart rate variability (HRV) refers to various methods of assessing the beat-to-beat variation in the heart over time, in order to draw inference on the outflow of the autonomic nervous system. Easy access to measuring HRV has led to a plethora of studies within emotion science and psychology assessing autonomic regulation, but significant caveats exist due to the complicated nature of HRV. Firstly, both breathing and blood pressure regulation have their own relationship to social, emotional and cognitive experiments – if this is the case are we observing heart rate changes as a consequence of breathing changes? Secondly, experiments often have poor internal and external controls. In this review we highlight the interrelationships between heart rate and respiration, as well as presenting recommendations for researchers to use when collecting data for HRV assessment. Namely, we highlight the superior utility of within-subjects designs along with the importance of establishing an appropriate baseline and monitoring respiration.

/pdf/considerations-in-the-assessment-of-heart-rate-variability-4owo86p92x.pdf

Considerations in the assessment of heart rate variability in biobehavioral research

The invention provides a system for measuring respiratory rate (RR) from a patient. The system includes an impedance pneumography (IP) sensor, connected to at least two electrodes, and a processing system that receives and processes signals from the electrodes to measure an IP signal. A motion sensor (e.g. an accelerometer) measures at least one motion signal (e.g. an ACC waveform) describing movement of a portion of the patient's body to which it is attached. The processing system receives the IP and motion signals, and processes them to determine, respectfully, frequency-domain IP and motion spectra. Both spectra are then collectively processed to remove motion components from the IP spectrum and determine RR. For example, during the processing, an algorithm determines motion frequency components from the frequency-domain motion spectrum, and then using a digital filter removes these, or parameters calculated therefrom, from the IP spectrum.

Body-worn monitor for measuring respiration rate

An algorithm for analyzing changes in ECG morphology based on principal component analysis (PCA) is presented and applied to the derivation of surrogate respiratory signals from single-lead ECGs. The respiratory-induced variability of ECG features, P waves, QRS complexes, and T waves are described by the PCA. We assessed which ECG features and which principal components yielded the best surrogate for the respiratory signal. Twenty subjects performed controlled breathing for 180 s at 4, 6, 8, 10, 12, and 14 breaths per minute and normal breathing. ECG and breathing signals were recorded. Respiration was derived from the ECG by three algorithms: the PCA-based algorithm and two established algorithms, based on RR intervals and QRS amplitudes. ECG-derived respiration was compared to the recorded breathing signal by magnitude squared coherence and cross-correlation. The top ranking algorithm for both coherence and correlation was the PCA algorithm applied to QRS complexes. Coherence and correlation were significantly larger for this algorithm than the RR algorithm(p < 0.05 and p < 0.0001, respectively) but were not significantly different from the amplitude algorithm. PCA provides a novel algorithm for analysis of both respiratory and nonrespiratory related beat-to-beat changes in different ECG features.

/pdf/principal-component-analysis-as-a-tool-for-analyzing-beat-to-4nm95ms8tu.pdf

Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG-Derived Respiration

We used principal component analysis to derive the respiratory rate from single lead ECGs. In this algorithm the respiratory induced beat-to-beat variability of the ECG is described by the coefficients of the principal components. Subjects were asked to breathe at different rates and naturally while respiration and ECG were recorded. Breathing rate, determined by Fourier analysis, was compared for the ECG derived respiration obtained by principal component analysis and the recorded respiratory signal. Across the different breathing patterns the mean absolute differences between reference respiratory rate and ECG respiratory rate were 0.2 breaths per minute (bpm) or less. In all cases the respiratory rate was accurately determined from respiratory surrogates derived from principal component analysis of the single lead ECGs.

http://cinc.mit.edu/archives/2008/pdf/0437.pdf

Respiratory rate derived from principal component analysis of single lead electrocardiogram

Changes in the ECG ST segment are often observed in patients with myocardial ischaemia. However, non-ischaemic changes in ST level are also common thereby limiting ischaemia detection accuracy. The aim of this study was to devise an algorithm and determine its accuracy in distinguishing between ischaemic and non-ischaemic changes in the ECG ST-segment, using expertly annotated ECG data sets as a gold standard reference. The algorithm considered only the change in ST relative to a baseline ST level (/spl Delta/ST) provided by the PhysioNet database, and based on simple level thresholding within specified time windows. An initial score of 82.3% (accuracy 91.1%, with sensitivity 99.0% and specificity 88.8%) was achieved for the learning set. By making slight modifications to the algorithm and introducing principal components of ST it was not possible to improve the original algorithm. The original algorithm was therefore left as our challenge entry achieving an accuracy of 90.7% for the test data set (score of 81.4%, entry 1, 1 May 2003).

/pdf/an-algorithm-to-distinguish-ischaemic-and-non-ischaemic-st-4j7bvfanpd.pdf

An algorithm to distinguish ischaemic and non-ischaemic ST changes in the Holter ECG

Atrial fibrillation is an ECG rhythm with a significant mortality due to stroke. The objective of this study was to detect those patients most likely to develop atrial fibrillation, and to identify ECGs closest to the onset of fibrillation. Our hypothesis was that patients with atrial fibrillation would have atrial ectopy, and the frequency of this activity would increase prior to onset of fibrillation. From a learning set of 100 30-minute ECGs from 50 patients, 25 without atrial fibrillation (normal) and 25 who subsequently developed atrial fibrillation, an algorithm was developed to detect the presence of ectopic beats using R-R interval data. In the learning set, 37/50 abnormal and 34/50 normal patients were identified, giving a potential screening accuracy of 71%. As a prediction test to detect the ECGs closest to atrial fibrillation, 19/25 were correctly identified. For the test set, a total of 29/50 were correctly assigned to the normal and fibrillation groups, and a 39/50 score obtained in predicting the onset of atrial fibrillation.

Can paroxysmal atrial fibrillation be predicted

An algorithm for analysing changes in ECG morphology based on principal component analysis (PCA) is presented and applied to the derivation of surrogate respiratory signals from single lead ECGs. The respiratory induced variability of ECG features, P waves, QRS complexes and T waves are described by the PCA. We assessed which ECG features and which principal components yielded the best surrogate for the respiratory signal. 20 subjects performed controlled breathing for 180 s at 4, 6, 8, 10, 12 and 14 breaths per minute and normal breathing. ECG and breathing signals were recorded. Respiration was derived from the ECG by three algorithms; the PCA based algorithm and two established algorithms, based on RR intervals and QRS amplitudes. ECG derived respiration was compared to the recorded breathing signal by magnitude squared coherence and cross-correlation. The top ranking algorithm for both coherence and correlation was the PCA algorithm applied to QRS complexes. Coherence and correlation were significantly larger for this algorithm than the RR algorithm (p<0.05 and p<0.0001 respectively) but were not significantly different from the amplitude algorithm. PCA provides a novel algorithm for analysis of both respiratory and non-respiratory related beat-to-beat changes in different ECG features.

Principal Component Analysis as a Tool for Analysing Beat-to-Beat Changes in Electrocardiogram Features: Application to Electrocardiogram Derived Respiration

E.J. Bowers

Papers

Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG-Derived Respiration

Respiratory rate derived from principal component analysis of single lead electrocardiogram

An algorithm to distinguish ischaemic and non-ischaemic ST changes in the Holter ECG

Can paroxysmal atrial fibrillation be predicted

Principal Component Analysis as a Tool for Analysing Beat-to-Beat Changes in Electrocardiogram Features: Application to Electrocardiogram Derived Respiration