Spectral Data Fusion for Robust ECG-derived Respiration with Experiments in Different Physical Activity Levels

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.

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“…• Extract the morphological scale variation of part of the signal (e.g., QRS complexes) by comparing the current beat to a template beat [45].…”

Section: Table II Feature-based Techniques For Extraction Of Respiratory Signalsmentioning

confidence: 99%

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Charlton

Birrenkott

Bonnici

et al. 2018

IEEE Rev. Biomed. Eng.

268

213

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“…We detect the ECG R-waves using Pan-Tompkins method [23]. The IBI signal is constructed and the ectopic beats are detected and edited by the method described in [2]. Then we use the time-varying integral pulse frequency modulation (TVIPFM) model proposed in [4], which presumes the instantaneous heart rate as a time-variant DC component, and a zero-mean modulating signal, expressed as dHRM (t) and m(t), respectively.…”

Section: Ecg Processingmentioning

confidence: 99%

“…One major source of HRV contamination is known to be cadence [5,3]. Recently, the study of HRV during stress test has been of interest [2,3,13,14,17,18]. In 2009, Blain et al in [5] revealed the existence of cardiolocomotion coupling (CLC) components in HRV during cycling.…”

Section: Introductionmentioning

confidence: 99%

Characterization and reduction of exercise-based motion influence on heart rate variability using accelerator signals and channel decoding in the time–frequency domain

Alikhani

Noponen²,

Hautala³

et al. 2018

Physiol. Meas.

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In our proposed method, we utilized the motion trajectory (which is known to exist partially in HRV) measured by a three-channel accelerator (ACC). We then estimated their shares in HRV using a wearable electrocardiogram (ECG) and an error-correcting problem formulation. In this method, we characterized the motion components of three orthogonal directions induced into the HRV signal, and then we suppressed the estimated motion artefact to construct a motion-attenuated spectrogram. Main results and Significance: Our analysis showed that HRV in the exercise context is susceptible to motion artefacts. Furthermore, the interpretation of autonomic nervous system (ANS) activity and HRV indices throughout exercise has a high margin of error depending on the intensity level, type of exercise, and motion trajectory. Our experiment on 84 healthy subjects throughout mid-intensity cycling and walking tests showed 39% and 32% influence on average, respectively. In addition, our proposed method revealed through a maximal running test with 11 runners that motion can describe on average 20%-40% of the HRV high-frequency (HF) energy at different workloads of running.

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“…Beat occurrence time series is taken for the construction of instantaneous HR signal (hr(k)). Outliers and ectopic beats are identified using the method explained in [6], and interpolated using spline interpolation of neighboring controlled beats. Then, hr(k) is sampled at eight Hertz using linear interpolation and is indicated as hr(n).…”

Section: B Signal Preprocessingmentioning

confidence: 99%

Contribution of body movements on the heart rate variability during high intensity running

Alikhani

Noponen

Seppänen

2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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We studied the association between the heart rate variability (HRV) and the subject's movement during high intensity running. HRV is affected by movement, and this phenomena is known as cardiolocomotor coupling (CLC). Characterization of movement related components on the HRV spectrogram is a principal step toward meaningful interpretation of autonomic nervous system (ANS) activity. According to the literature, the aliases of the first and second harmonics of the cadence frequency are the main contributors affecting HRV. Instead, we found out that there is another aliasing component containing significant power in the HRV spectrogram. The source of this component might be the arm swings, torso movement or any other mechanical movement along the horizontal axis, orthogonal to the cadence direction. Our results show that in 13 out of 22 subjects the spectral HRV component arising from the alias of the second harmonic of cadence frequency (vertical acceleration) accommodates significantly less energy than the component related to the alias of the first harmonic of horizontal acceleration. Therefore, neglecting this component and/or considering the second harmonic of the cadence frequency as more dominant one is not always a valid assumption.

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Spectral Data Fusion for Robust ECG-derived Respiration with Experiments in Different Physical Activity Levels

Cited by 8 publications

References 0 publications

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Characterization and reduction of exercise-based motion influence on heart rate variability using accelerator signals and channel decoding in the time–frequency domain

Contribution of body movements on the heart rate variability during high intensity running

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