Concept: Heart rate monitor
To assess the validity of RR intervals and short-term heart rate variability (HRV) data obtained from the Polar V800 heart rate monitor, in comparison to an electrocardiograph (ECG).
An important challenge in heart research is to make the relation between the features of external stimuli and heart activity. Olfactory stimulation is an important type of stimulation that affects the heart activity, which is mapped on Electrocardiogram (ECG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the ECG signal. This study investigates the relation between the structures of heart rate and the olfactory stimulus (odorant). We show that the complexity of the heart rate is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal heart rate. Also, odorant having higher entropy causes the heart rate having lower approximate entropy. The method discussed here can be applied and investigated in case of patients with heart diseases as the rehabilitation purpose.
- Computer methods in biomechanics and biomedical engineering
- Published almost 9 years ago
Electrocardiogram (ECG) signals are difficult to interpret, and clinicians must undertake a long training process to learn to diagnose diabetes from subtle abnormalities in these signals. To facilitate these diagnoses, we have developed a technique based on the heart rate variability signal obtained from ECG signals. This technique uses digital signal processing methods and, therefore, automates the detection of diabetes from ECG signals. In this paper, we describe the signal processing techniques that extract features from heart rate (HR) signals and present an analysis procedure that uses these features to diagnose diabetes. Through statistical analysis, we have identified the correlation dimension, Poincaré geometry properties (SD2), and recurrence plot properties (REC, DET, L (mean)) as useful features. These features differentiate the HR data of diabetic patients from those of patients who do not have the illness, and have been validated by using the AdaBoost classifier with the perceptron weak learner (yielding a classification accuracy of 86%). We then developed a novel diabetic integrated index (DII) that is a combination of these nonlinear features. The DII indicates whether a particular HR signal was taken from a person with diabetes. This index aids the automatic detection of diabetes, thereby allowing a more objective assessment and freeing medical professionals for other tasks.
Heart rate variability (HRV) is widely used to assess autonomic nervous system (ANS) function. It is traditionally collected from a dedicated laboratory electrocardiograph (ECG). This presents a barrier to collecting the large samples necessary to maintain the statistical power of between-subject psychophysiological comparisons. An alternative to ECG involves an optical pulse sensor or photoplethysmograph run from a smartphone or similar portable device: smartphone pulse rate variability (SPRV). Experiment 1 determined the simultaneous accuracy between ECG and SPRV systems in n=10 participants at rest. Raw SPRV values showed a consistent positive bias, which was successfully attenuated with correction. Experiment 2 tested an additional n=10 participants at rest, during attentional load, and during mild stress (exercise). Accuracy was maintained, but slightly attenuated during exercise. The best correction method maintained an accuracy of +/-2% for low-frequency spectral power, and +/- 5% for high-frequency spectral power over all points. Thus, the SPRV system records a pulse-to-pulse approximation of an ECG-derived heart rate series that is sufficiently accurately to perform time- and frequency-domain analysis of its variability, as well as accurately reflecting change in autonomic output provided by typical psychophysiological stimuli. This represents a novel method by which an accurate approximation of HRV may be collected for large-sample or naturalistic cardiac psychophysiological research.
Although Photoplethysmographic (PPG) signals can monitor heart rate (HR) quite conveniently in hospital environments, trying to incorporate them during fitness programs poses a great challenge, since in these cases the signals are heavily corrupted by motion artifacts (MA).
We investigated how the audience member’s physiological reactions differ as a function of listening context (i.e., live versus recorded music contexts). Thirty-seven audience members were assigned to one of seven pianists' performances and listened to his/her live performances of six pieces (fast and slow pieces by Bach, Schumann, and Debussy). Approximately 10 weeks after the live performance, each of the audience members returned to the same room and listened to the recorded performances of the same pianists' via speakers. We recorded the audience members' electrocardiograms in listening to the performances in both conditions, and analyzed their heart rates and the spectral features of the heart-rate variability (i.e., HF/TF, LF/HF). Results showed that the audience’s heart rate was higher for the faster than the slower piece only in the live condition. As compared with the recorded condition, the audience’s sympathovagal balance (LF/HF) was less while their vagal nervous system (HF/TF) was activated more in the live condition, which appears to suggest that sharing the ongoing musical moments with the pianist reduces the audience’s physiological stress. The results are discussed in terms of the audience’s superior attention and temporal entrainment to live performance.
Fitness trackers are devices or applications for monitoring and tracking fitness-related metrics such as distance walked or run, calorie consumption, quality of sleep and heart rate. Since accurate heart rate monitoring is essential in fitness training, the objective of this study was to assess the accuracy and precision of the Fitbit Charge 2 for measuring heart rate with respect to a gold standard electrocardiograph. Fifteen healthy participants were asked to ride a stationary bike for 10 minutes and their heart rate was simultaneously recorded from each device. Results showed that the Fitbit Charge 2 underestimates the heart rate. Although the mean bias in measuring heart rate was a modest -5.9 bpm (95% CI: -6.1 to -5.6 bpm), the limits of agreement, which indicate the precision of individual measurements, between the Fitbit Charge 2 and criterion measure were wide (+16.8 to -28.5 bpm) indicating that an individual heart rate measure could plausibly be underestimated by almost 30 bpm.
Many modern smart watches and activity trackers feature an optical sensor that estimates the wearer’s heart rate. Recent studies have evaluated the performance of these consumer devices in the laboratory.
Risk stratification models can be employed at the emergency department (ED) to evaluate patient prognosis and guide choice of treatment. We derived and validated a new cardiovascular risk stratification model comprising vital signs, heart rate variability (HRV) parameters, and demographic and electrocardiogram (ECG) variables.
- International journal of sports physiology and performance
- Published almost 5 years ago
Training load (TL) is monitored with the aim of making evidence-based decisions on appropriate loading schemes to reduce injuries and enhance team performance. However little is known in detail about the variables of load and methods analysis used in high level football. Therefore the aim of this study was to provide information on the practices and perceptions of monitoring in professional clubs. Eighty two high-level football clubs from Europe, the United States and Australia were invited to answer questions relating to (1) how TL is quantified; (2) how players' responses are monitored, and (3) their perceptions of the effectiveness of monitoring. Forty one responses were received. All teams used GPS and heart rate monitors during all training sessions and 28 used RPE. The top 5 ranking TL variables were; acceleration (various thresholds), total distance, distance covered above 5.5 m·s-1, estimated metabolic power, and heart rate exertion. Players' responses to training are monitored using questionnaires (68% of clubs) and submaximal exercise protocols (41%). Differences in expected vs. actual effectiveness of monitoring were 23% and 20% for injury prevention and performance enhancement respectively (P<0.001 d=1.0 to 1.4). Of the perceived barriers to effectiveness, "limited human resources" scored highest, followed by "coach buy-in". The discrepancy between expected and actual effectiveness appears to be due to suboptimal integration with coaches, insufficient human resources and concerns over the reliability of assessment tools. Future approaches should critically evaluate the usefulness of current monitoring tools and explore methods of reducing the identified barriers to effectiveness.