A new signal decomposition to estimate breathing rate and heart rate from photoplethysmography signal

Li, Dazhou; Zhao, Hai; Dou, Shengchang

doi:10.1016/j.bspc.2015.03.008

Cited by 22 publications

(12 citation statements)

References 14 publications

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“…PCA has also been applied to intrinsic mode functions extracted using ensemble empirical mode decomposition [ 157 ], [ 158 ]. Extract the instantaneous amplitudes or frequencies of cardiac modulation using the continuous wavelet transform [ 41 ], the Teager-Kaiser energy operator [ 204 ], variable frequency complex demodulation [ 28 ], [ 67 ], the Hilbert transform [ 129 ], or the synchrosqueezing transform [ 79 ]. Filter using the centered correntropy function [ 90 ].…”

Section: Figmentioning

confidence: 99%

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Charlton

Birrenkott

Bonnici

et al. 2018

IEEE Rev. Biomed. Eng.

246

196

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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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Section: Figmentioning

confidence: 99%

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Charlton

Birrenkott

Bonnici

et al. 2018

IEEE Rev. Biomed. Eng.

246

196

View full text Add to dashboard Cite

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“…While various methods in the literature have used windowed segments of the PPG/ECG signals, very few methods have investigated the instantaneous respiratory rate (IRR) from ECG [ 20 , 21 ] or PPG [ 22 , 23 ]. Recently-described methods have applied singular spectrum analysis (SSA) [ 24 ], or nonlinear time-frequency analysis; examples of the latter include the continuous wavelet transform (CWT) [ 25 ], the Hilbert transform [ 26 ], and variable-frequency complex demodulation [ 27 , 28 ]. In this work described by this paper, we address the estimation of instantaneous RR from reflectance PPG signals rather than using ECG signals (which usually require the application of electrodes and can cause discomfort).…”

Section: Introductionmentioning

confidence: 99%

Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions

Jarchi

Salvi

Tarassenko

et al. 2018

Sensors

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Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors using windowed segments of the data; e.g., often using a minimum of 32 s. Little research has been reported in the literature concerning the instantaneous detection of respiratory rate from such sources. In this paper, we develop and evaluate a method to estimate instantaneous respiratory rate (IRR) from body-worn reflectance photoplethysmography (PPG) sensors. The proposed method relies on a nonlinear time-frequency representation, termed the wavelet synchrosqueezed transform (WSST). We apply the latter to derived modulations of the PPG that arise from the act of breathing.We validate the proposed algorithm using (i) a custom device with a PPG probe placed on various body positions and (ii) a commercial wrist-worn device (WaveletHealth Inc., Mountain View, CA, USA). Comparator reference data were obtained via a thermocouple placed under the nostrils, providing ground-truth information concerning respiration cycles. Tracking instantaneous frequencies was performed in the joint time-frequency spectrum of the (4 Hz re-sampled) respiratory-induced modulation using the WSST, from data obtained from 10 healthy subjects. The estimated instantaneous respiratory rates have shown to be highly correlated with breath-by-breath variations derived from the reference signals. The proposed method produced more accurate results compared to averaged RR obtained using 32 s windows investigated with overlap between successive windows of (i) zero and (ii) 28 s. For a set of five healthy subjects, the averaged similarity between reference RR and instantaneous RR, given by the longest common subsequence (LCSS) algorithm, was calculated as 0.69; this compares with averaged similarity of 0.49 using 32 s windows with 28 s overlap between successive windows. The results provide insight into estimation of IRR and show that upper body positions produced PPG signals from which a better respiration signal was extracted than for other body locations.

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“…These advancements in the non-invasive physiological detection, miniaturization of the hardware, and wireless communication are leading to the development of new wearable technologies having wide and important implications to the health area [ 67 ]. Wearable computation has the potential of revolutionizing health care implementing low-cost physiological monitoring, in addition to enabling comfortable a continuous cardiovascular monitoring away from the clinical ambient and during long periods of time [ 21 , 120 ].…”

Section: Discussion and Open Issuesmentioning

confidence: 99%

“…The rapid growing of Internet of Things (IoT) technology and biosensors resulted in new opportunities for personalized services of e-health and health, one of these services is the interconnection between the PPG technique and the Internet of Things technology [ 21 , 120 , 121 , 122 ]. The popularity of portable sensors and the Internet of Things (IoT) transfer significant privileges to the body sensors networks which could communicate which the cloud computing platforms to offer the interoperability in the monitoring of health and welfare [ 123 , 124 ].…”

Section: Discussion and Open Issuesmentioning

confidence: 99%

Advances in Photopletysmography Signal Analysis for Biomedical Applications

Moraes

Rocha

Vasconcelos

et al. 2018

Sensors

220

114

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Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost–benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique.

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A new signal decomposition to estimate breathing rate and heart rate from photoplethysmography signal

Cited by 22 publications

References 14 publications

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions

Advances in Photopletysmography Signal Analysis for Biomedical Applications

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