Research on methods for detecting respiratory rate from photoplethysmographic signal

Kuwałek, Piotr; Burlaga, Bartłomiej; Jesko, Waldemar; Konieczka, Patryk

doi:10.1016/j.bspc.2021.102483

Cited by 14 publications

(8 citation statements)

References 23 publications

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“…Conversely, the accuracy of RR extraction has a larger 1 compared to the HR measurement, which is 2.56 ± 0.34 and 2.99 ± 0.15 breaths/min in cycling and treadmill exercises with different motion intensities. On the other hand, the RR is located in a much lower frequency range (0.1-1.0 Hz), and it is more susceptible to interference from MAs than HR measurement [30]. As shown in Fig.…”

Section: Discussionmentioning

confidence: 98%

Adaptive notch-filtration to effectively recover photoplethysmographic signals during physical activity

Zheng

Dwyer

Barrett

et al. 2022

Biomedical Signal Processing and Control

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Section: Discussionmentioning

confidence: 98%

Adaptive notch-filtration to effectively recover photoplethysmographic signals during physical activity

Zheng

Dwyer

Barrett

et al. 2022

Biomedical Signal Processing and Control

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“…The first way is to interpolate the local minimas in the PPG signal with a cubic spline. This method has been used for respiratory rate detection with PPG signals [ 26 ], and it can accurately capture the general outlines of the PPG waveform. The second way is to use an N-tap Hilbert filter to obtain the upper envelope of the analytic signal; this method has been successfully used for abnormal heart sound analysis [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia

et al. 2022

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Objective: We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. Methods: Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung’s Gear S3 and Galaxy Watch 3. Results: The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors—30% and 66% lower—and mean heart rate and mean interbeat interval estimation errors—60% and 77% lower—when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. Conclusion: The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. Significance: By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.

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“…The exception to this rule is the axes correlations, which are carried out using the filtered tri-axial components (𝑎 𝑥 (𝑡), 𝑎 𝑦 (𝑡), 𝑎 𝑧 (𝑡)). The non-normalized feature set, 𝑞 = (𝑞 1 , … , 𝑞 16 ), was then normalized between the range of zero and one per feature, for all features in the set 𝑓 = (𝑓 1 , … , 𝑓 16 ), such that 𝑓 𝑖 is the 𝑖th normalized feature of the sixteen final features, whereby:…”

Section: Feature Set For Respiratory Activitymentioning

confidence: 99%

“…Wearable accelerometers have been introduced as a potential noninvasive alternative to PSGs. Body-worn sensors, for example, measure the displacement of the thorax [8,9] where the respiratory rate (RR) is estimated [10][11][12][13][14][15][16]. Accelerometers have, in a more complex setup, even be used to estimate the airflow [17].…”

Section: Introductionmentioning

confidence: 99%

Respiratory analysis during sleep using a chest-worn accelerometer: A machine learning approach

Ryser

Hanassab

Lambercy

et al. 2022

Biomedical Signal Processing and Control

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Research on methods for detecting respiratory rate from photoplethysmographic signal

Cited by 14 publications

References 23 publications

Adaptive notch-filtration to effectively recover photoplethysmographic signals during physical activity

Adaptive notch-filtration to effectively recover photoplethysmographic signals during physical activity

A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia

Respiratory analysis during sleep using a chest-worn accelerometer: A machine learning approach

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