Variations of HRV analysis in different approaches

Chang, Fang-Kai; Chang, Che-Cheng; Chiu, Chuang-Chien; Hsu, Sheng-Yi; Lin, Yu-De

doi:10.1109/cic.2007.4745410

Cited by 18 publications

(12 citation statements)

References 10 publications

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“…Different measurements of the heart activity are more promising [5,8,25]. Heart rate, or pulse, is both an easily measured feature, strongly related to arousal and stress, and easy to conceptualize for end-users.…”

Section: Methods For Sensing Arousalmentioning

confidence: 99%

“…First, a measurement fault of missing one heart beat would give an extremely large variation, thus being very error prone [5,8]. Second, the physiological research community has failed to reach consensus regarding the interpretation of HRV data [5,19,30], preventing widespread clinical application. Therefore, the study of HRV has to mature before it can be used to provide unassisted biofeedback to non-expert users.…”

Section: Can We Sense Adaptability?mentioning

confidence: 99%

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Mind the body!

Sanches

Höök

Vaara

et al. 2010

Proceedings of the 8th ACM Conference on Designing Interactive Systems

101

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We have designed a stress management biofeedback mobile service for everyday use, aiding users to reflect on both positive and negative patterns in their behavior. To do so, we embarked on a complex multidisciplinary design journey, learning that: detrimental stress results from complex processes related to e.g. the subjective experience of being able to cope (or not) and can therefore not be measured and diagnosed solely as a bodily state. We learnt that it is difficult, sometimes impossible, to make a robust analysis of stress symptoms based on biosensors worn outside the laboratory environment they were designed for. We learnt that rather than trying to diagnose stress, it is better to mirror short-term stress reactions back to them, inviting their own interpretations and reflections. Finally, we identified several experiential qualities that such an interface should entail: ambiguity and openness to interpretation, interactive history of prior states, fluency and aliveness.

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Section: Methods For Sensing Arousalmentioning

confidence: 99%

Section: Can We Sense Adaptability?mentioning

confidence: 99%

Mind the body!

Sanches

Höök

Vaara

et al. 2010

Proceedings of the 8th ACM Conference on Designing Interactive Systems

101

View full text Add to dashboard Cite

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“…For ECG, the extraction method incorporated a peak detection algorithm that found the time of occurrence of each QRS complex in the filtered ECG signal (So and Chan 1997), and then the durations between successive peak locations were calculated to produce a time series of R-R intervals (RRIs) as HRV signals. For PPG, a neighboring peak searching method to derive the peak events from the amplitude of the filtered PPG signals (Chang et al 2007), and then the intervals between the successive detected peaks (PPIs) were calculated and defined as PPV signals. All of the RRI and PPI time series were firstly edited automatically, after which a careful manual editing was performed by visual inspection.…”

Section: Extraction Of Hrv and Ppv Signalsmentioning

confidence: 99%

“…Furthermore, PPG has been successfully used as a surrogate for ECG in calculation of measures of HRV in both healthy subjects (Chang et al 2007;Lu et al 2008;Nitzan et al 1998) and a number of disease conditions (Gil et al 2005(Gil et al , 2006, including paraplegia (Wecht et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Limitations of Oximetry to Measure Heart Rate Variability Measures

Lü

Yang

2009

Cardiovasc Eng

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Measuring heart rate variability (HRV) is widely used to assess autonomic nervous system function. It requires accurate measurement of the interval between successive heartbeats. This can be achieved from recording the electrocardiogram (ECG), which is non-invasive and widely available. However, methodological problems inherent in recording and analyzing ECG traces have motivated a search for alternative means of measuring the interval between successive heartbeats. Recording blood oxygenation pulsations (photoplethysmography-PPG) is also convenient, non-invasive and widely available, and has been suggested as an effective alternative to ECG to derive HRV. Moreover, it has been claimed that the pulse waveforms produced by oximetry may be more practicable than R-R intervals measured from the by ECG, especially for ambulatory recordings. We have therefore compared PPG with ECG recordings to measure HRV applying the same signal analysis techniques to PPG and ECG recordings made simultaneously. Comparison of 5 min recording epochs demonstrated a very high degree of correlation, in temporal, frequency domains and non-linear analysis, between HRV measures derived from the PPG and ECG. However, we found that the PPG signal is especially vulnerable to motion artifacts when compared to the ECG, preventing any HRV analysis at all in a significant minority of PPG recordings. Our results demonstrate that even though PPG provides accurate interpulse intervals to measure heart rate variability under ideal conditions, it is less reliable due to its vulnerability to motion artifacts. Therefore it is unlikely to prove a practical alternative to the ECG in ambulatory recordings or recordings made during other activities.

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“…However, measuring the ECG of the patient is not easy for untrained personnel, not mentioning long-term monitoring without significant interference in the daily life. Although it is less prominent than that from ECG [4], HRV from PPG can be still an acceptable compromise for non-professional and daily monitoring. PPG would be more acceptable for general public because of single detection location (usually at fingertip or ear lobe) and no gel electrode needed.…”

Section: Introductionmentioning

confidence: 99%

Portable heart rate detector based on photoplethysmography with android programmable devices

Kin

Lao

Pun

et al. 2012

2012 35th International Conference on Telecommunications and Signal Processing (TSP)

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Abstract-In this paper, a miniature portable heart rate detector system is implemented by modern hardware ICs and simple sensor circuit with software executable on both PC and Android platform. The biosignal is first extracted via photoplethysmography (PPG) principle into electric signal. Then a microprocessor is used to covert biosignal from analog to digital format, suitably for feeding into an RF module (nRF24L01 for RF transmission). On the receiver end, the computer and/or smart phone can analyze the data using a robust algorithm that can detect peaks of the PPG waveform, hence to calculate the heart rate. Some application software running on Windows and Android phone have been developed to display heart rate information and time domain waveform to users for health care monitoring. In the future, pure Bluetooth technology will be used for wireless personal communications instead of RF modules. At the same time, the data can be sent to computer console using existing available networks (3G, 4G, WiFi, etc.) for health database logging purpose.

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Variations of HRV analysis in different approaches

Abstract: The analysis of normal-to-normal (NN) intervals acquired from a continuous electrocardiogram (ECG)record

Cited by 18 publications

References 10 publications

Mind the body!

Mind the body!

Limitations of Oximetry to Measure Heart Rate Variability Measures

Portable heart rate detector based on photoplethysmography with android programmable devices

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