Data fusion for estimating respiratory rate from a single-lead ECG

Orphanidou, Christina; Fleming, Susannah; Shah, Syed Ahmar; Tarassenko, Lionel

doi:10.1016/j.bspc.2012.06.001

Cited by 84 publications

(72 citation statements)

References 23 publications

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“…In the same way, during the respiratory cycle, chest movements and changes in the thorax impedance distribution due to filling and emptying of the lungs cause a rotation of the electrical axis of the heart which has an effect over beat morphology [5,6]. Several studies have developed signal processing techniques to extract respiratory information from the modulation of the ECG morphology and heart rate, using both time and frequency based methods [3,5,[7][8][9]. However, the R-peak amplitudes (RPA) based Rr estimates depend on the given ECG lead, as beat respiratory related morphological changes differ lead to lead and most influenced lead is subject dependent.…”

Section: Introductionmentioning

confidence: 99%

Respiratory rate estimation from multilead directions, based on ECG delineation

Noriega¹,

Marañón²,

Romero³

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Estimating the instantaneous respiratory rate (Rr) from the electrocardiogram (ECG) is of interest as respiration direct measurement in clinical situations is often cumbersome. In this study, the Rr was estimated from the same Final Directions of maximum projection (FD) used for multi lead ECG automatic delineation. Power spectral analysis over the directions based on QRS complex main peak and T wave on-set, peak and end spatial loops was used for Rr estimation. On a subset of the Physionet MGH/MF dataset, the proposed method yielded more accurate Rr estimates (minimum mean absolute error (MAE), 2.82 bpm) than the frequency tracking algorithm (minimum MAE, 4.53 bpm) and Fourier-based frequency estimation (minimum MAE, 4.94 bpm) using each lead alone, outperforming also the weighted multi-signal oscillator-based algorithm estimates for two or three lead (minimum MAE, 3.04 bpm). It was also shown that the FD of the three orthogonalized leads from Principal Component algorithm, improve the performance of Rr estimation.

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Section: Introductionmentioning

confidence: 99%

Respiratory rate estimation from multilead directions, based on ECG delineation

Noriega¹,

Marañón²,

Romero³

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

show abstract

“…To guarantee the accurate signal of a single modality system, the fusion of two or more signals is significant to improve the performance of the system [39,[41][42][43][44][45][46][47][48]. To achieve robust and discriminative performance for gender recognition, a fusion of EMG and HRV [40,41,49] is proposed before feeding into the interface system in order to control the level of MR valve stiffness of the stepper before the rehabilitation exercise can be emulated [40,41].…”

Section: Workmentioning

confidence: 99%

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

et al. 2017

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Gender recognition is trivial for a physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) (Hsu et al.) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SVM). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient and effective for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Barani's work (90.34%), Nazarloo's work (92.50%), and other classifiers.

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“…The RR intervals, the time between two adjacent R peaks, were calculated for all three filtering methods and used to estimate the respiration signal using cubic spline interpolation. The respiration function can be obtained from ECG for subjects that exhibit the phenomenon of respiratory sinus arrhythmia (RSA) [18,19,20,21].…”

Section: Face-lead Analysismentioning

confidence: 99%

Vital signs from inside a helmet: A multichannel face-lead study

Rosenberg

Chanwimalueang

Looney

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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It is essential to measure physiological parameters such as heart rate variability and respiratory rate of drivers to evaluate their performance. The results from this measurement can be used to assess the state of body and mind, for instance concentration and stress. However, current systems only work in controlled environments, or sensors obstruct and interfere with operations of the driver. In this study, a face-lead ECG is placed inside a helmet to enhance comfort and convenience in racing scenarios. Multiple electrodes were attached to facial locations, which exhibit good contact with a helmet, and bipolar configurations were examined between the left and right side of the subject's face. Standard and data-driven filtering algorithms were employed to improve the extraction of R peaks from the ECG data. The so-extracted R peaks were subsequently used to estimate heart activity and respiration effort, and the results were compared with standard recording protocols. It is shown that ECG recordings obtained from locations on the lower jaw match closely with conventional recording paradigms (limb-lead ECG), highlighting the potential of vital sign monitoring from within a racing helmet.Index Terms-Electrocardiogram ECG, vital signs, racing helmet, respiratory rate, MEMD.

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Data fusion for estimating respiratory rate from a single-lead ECG

Cited by 84 publications

References 23 publications

Respiratory rate estimation from multilead directions, based on ECG delineation

Respiratory rate estimation from multilead directions, based on ECG delineation

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

Vital signs from inside a helmet: A multichannel face-lead study

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