Detection of a spontaneous pulse in photoplethysmograms during automated cardiopulmonary resuscitation in a porcine model

Wijshoff, Ralph; Sar, Toeno van der; Peeters, Wouter; Bezemer, Rick; Aelen, Paul; Paulussen, Igor; Ordelman, Simone; Venema, A.; Berkom, Paul F.J. van; Aarts, Ronald M.; Woerlee, P.H.; Scheffer, Gert-Jan; Noordergraaf, Gerrit J.

doi:10.1016/j.resuscitation.2013.07.019

Cited by 19 publications

(20 citation statements)

References 36 publications

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“…Here, r[n] contains noise and possibly frequency components resulting from interaction between spontaneous cardiac activity and compressions [20]. Interaction frequencies were observed at the sum and the difference of the compression rate and the PR and their harmonics [20].…”

Section: Removal Of the Compression Componentmentioning

confidence: 99%

“…Generally, photoplethysmography (PPG) is an easy-to-use and non-invasive technique to continuously measure a spontaneous pulse [18], [19]. Its potential to measure a spontaneous pulse during compressions has been observed in an automated-CPR animal study [20]. When the complexity of the PPG signal increased during compressions, the invasive blood pressure indicated presence of a spontaneous pulse.…”

mentioning

confidence: 99%

“…This paper describes a PPG-based algorithm that detects cardiogenic output during chest compressions to support ROSC detection, developed based on pre-clinical data from [20]. Cardiogenic output was detected using the compressionfree PPG signal and the baseline of the PPG signal.…”

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confidence: 99%

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Photoplethysmography-Based Algorithm for Detection of Cardiogenic Output During Cardiopulmonary Resuscitation

Wijshoff

Asten

Peeters

et al. 2015

IEEE Trans. Biomed. Eng.

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Detecting return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation (CPR) is challenging, time consuming, and requires interrupting chest compressions. Based on automated-CPR porcine data, we have developed an algorithm to support ROSC detection, which detects cardiogenic output during chest compressions via a photoplethysmography (PPG) signal. The algorithm can detect palpable and impalpable spontaneous pulses. A compression-free PPG signal which estimates the spontaneous pulse waveform, was obtained by subtracting the compression component, modeled by a harmonic series. The fundamental frequency of this series was the compression rate derived from the transthoracic impedance signal measured between the defibrillation pads. The amplitudes of the harmonic components were obtained via a least mean-square algorithm. The frequency spectrum of the compression-free PPG signal was estimated via an autoregressive model, and the relationship between the spectral peaks was analyzed to identify the pulse rate (PR). Resumed cardiogenic output could also be detected from a decrease in the baseline of the PPG signal, presumably caused by a redistribution of blood volume to the periphery. The algorithm indicated cardiogenic output when a PR or a redistribution of blood volume was detected. The algorithm indicated cardiogenic output with 94% specificity and 69% sensitivity compared to the retrospective ROSC detection of nine clinicians. Results showed that ROSC detection can be supported by combining the compression-free PPG signal with an indicator based on the detected PR and redistribution of blood volume.

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Section: Removal Of the Compression Componentmentioning

confidence: 99%

mentioning

confidence: 99%

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Photoplethysmography-Based Algorithm for Detection of Cardiogenic Output During Cardiopulmonary Resuscitation

Wijshoff

Asten

Peeters

et al. 2015

IEEE Trans. Biomed. Eng.

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“…PPG signals are highly susceptible to motion which hampers their use in, e.g., activities of daily living (ADL) [1], [5], [15], cardiopulmonary exercise testing (CPX) [16], [17], or cardiopulmonary resuscitation (CPR) [18], [19]. In ADL, the use of PPG is for instance researched to detect PR changes in patients with epilepsy [20], as this can indicate seizures [21].…”

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confidence: 99%

“…Motion can also affect SpO 2 measurements, e.g., causing false positive desaturations during CPX [16], [17]. During CPR, motion artifacts due to chest compressions complicate detection of a cardiac pulse in the signal [18], [19]. In this paper, we will focus on quasi-periodic motion artifacts, which is one type of motion artifact that can occur in ADL, CPX and CPR.…”

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confidence: 99%

Reduction of Periodic Motion Artifacts in Photoplethysmography

Wijshoff

Mischi²,

Aarts³

2017

IEEE Trans. Biomed. Eng.

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Abstract-Periodic motion artifacts affect photoplethysmography (PPG) signals in activities of daily living (ADL), cardiopulmonary exercise testing (CPX), and cardiopulmonary resuscitation (CPR). This hampers measurement of inter-beat-intervals (IBIs) and oxygen saturation (SpO2). Our objective was to develop a generic algorithm to remove periodic motion artifacts, recovering artifact-reduced PPG signals for beat-to-beat analysis. Methods: The algorithm was retrospectively evaluated on forehead PPG signals measured while walking on a treadmill. The step rate was tracked in a motion reference signal via a secondorder generalized-integrator with a frequency-locked loop. Two reference signals were compared: sensor motion relative to the skin (∆x[n]) measured via self-mixing interferometry, and head motion (av[n]) measured via accelerometry. The step rate was used in a quadrature harmonic model to estimate the artifacts. Quadrature components need only two coefficients per frequency leading to a short filter, and prevent undesired frequencyshifted components in the artifact estimate. Subtracting the estimate from the measured signal reduced the artifacts. Results: Compared to ∆x[n], av[n] had a better signal-to-noise ratio and more consistently contained a component at the step rate. Artifact reduction was effective for distinct step rate and pulse rate, since the artifact-reduced signals provided more stable IBI and SpO2 measurements. Conclusion: Accelerometry provided a more reliable motion reference signal. The proposed algorithm can be of significance for monitoring in ADL, CPX or CPR, by providing artifact-reduced PPG signals for improved IBI and SpO2 measurements during periodic motion.

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ECG-based pulse detection during cardiac arrest using random forest classifier

Elola

Aramendi

Irusta

et al. 2018

Med Biol Eng Comput

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Sudden cardiac arrest is one of the leading causes of death in the industrialized world. Pulse detection is essential for the recognition of the arrest and the recognition of return of spontaneous circulation during therapy, and it is therefore crucial for the survival of the patient. This paper introduces the first method based exclusively on the ECG for the automatic detection of pulse during cardiopulmonary resuscitation. Random forest classifier is used to efficiently combine up to nine features from the time, frequency, slope, and regularity analysis of the ECG. Data from 191 cardiac arrest patients was used, and 1177 ECG segments were processed, 796 with pulse and 381 without pulse. A leave-one-patient out cross validation approach was used to train and test the algorithm. The statistical distributions of sensitivity (SE) and specificity (SP) for pulse detection were estimated using 500 patient-wise bootstrap partitions. The mean (std) SE/SP for nine-feature classifier was 88.4 (1.8) %/89.7 (1.4) %, respectively. The designed algorithm only requires 4-s-long ECG segments and could be integrated in any commercial automated external defibrillator. The method permits to detect the presence of pulse accurately, minimizing interruptions in cardiopulmonary resuscitation therapy, and could contribute to improve survival from cardiac arrest.

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Detection of a spontaneous pulse in photoplethysmograms during automated cardiopulmonary resuscitation in a porcine model

Cited by 19 publications

References 36 publications

Photoplethysmography-Based Algorithm for Detection of Cardiogenic Output During Cardiopulmonary Resuscitation

Photoplethysmography-Based Algorithm for Detection of Cardiogenic Output During Cardiopulmonary Resuscitation

Reduction of Periodic Motion Artifacts in Photoplethysmography

ECG-based pulse detection during cardiac arrest using random forest classifier

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