On how to extract breathing rate from PPG signal using wearable devices

Fusco, Adele; Locatelli, D.; Durelli, Gianluca; Santambrogio, Marco D.

doi:10.1109/biocas.2015.7348369

Cited by 27 publications

(15 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fourth, the utility of BR algorithms would be greatly enhanced if the uncertainty associated with a BR estimate was quantified since unreliable BR estimates could be easily discarded [13]. Fifth, further research is required to identify BR algorithms with low-computational requirements that are suitable for use in miniaturized devices such as wearable sensors [88], [221]. Finally, BR algorithms that use a breath detection technique could be used to estimate BR variability, which may have utility as a marker of mental state and disease progression [210].…”

Section: A Areas For Algorithm Developmentmentioning

confidence: 99%

“…1) using SQIs to identify (and discard) artifactual data [231]; 2) using techniques to reduce the influence of motion artifact [55], [88], [107], [135], [144], [198]; and 3) fusing BRs according to the uncertainties associated with each determined by either deriving features from extracted respiratory signals (such as variation in breath-to-breath intervals) [65] or analyzing the respiratory signals using Gaussian processes [13].…”

Section: Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Charlton

Birrenkott

Bonnici

et al. 2018

IEEE Rev. Biomed. Eng.

265

213

View full text Add to dashboard Cite

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.

show abstract

Section: A Areas For Algorithm Developmentmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Charlton

Birrenkott

Bonnici

et al. 2018

IEEE Rev. Biomed. Eng.

265

213

View full text Add to dashboard Cite

show abstract

“…The EMD is an iterative process based on local maximum and minimum peaks identifi cation, creating of corresponding upper and lower envelopes of the studied signal, computing of a mean signal form the envelopes, subtraction this mean signal from the studied signal and a repetition of these steps until the diff erent between the studied signal and the computed mean signal is not signifi cant. The authors preferred to stop at the fi rst loop, the mean envelope calculation, as proposed Fusco [11]. This improvement reduces the algorithm complexity and the computational overload.…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach to the Respiratory Disease Follow-up Based on Plethhysmography Signal Parameters

Fernandez¹,

Caceres²,

Blanco³

2022

J Biomed Res Environ Sci

View full text Add to dashboard Cite

Aim: The aim of this paper is to present a novel approach for the monitoring of respiratory diseases based on the combined study of heart rate, respiratory rate and Pulse Oximetry (SpO2). Introduction: Since the start of the COVID-19 pandemic, respiratory diseases have increased its worldwide prevalence dramatically. Added to the impact of the pandemic is the already existing situation of a growing prevalence of diseases such as bronchial asthma and Chronic Obstructive Pulmonary Disease (COPD). On the other hand, it is known that the cardiovascular system is overloaded when there are respiratory insufficiencies and this condition can cause severe damage to health. Pulmonology and Cardiology are independent medical specialties that deal with these diseases and often do not cooperate with each other strongly, so a proposal is presented that combines respiratory and cardiovascular variables to deal with this health problem. Methods: A prototype was developed based on the STM32L073CZTx processor controlling the E305654 pulse oximetry module, which on demand delivers SpO2, photo plethysmography signal samples and pulse rate values. A vector was computed, every thirty minutes, to combine the respiratory rate obtained from the Plethysmography Signal (PPG), the mean value of SpO2 and the pulse rate. The vector module represents the cardiovascular and respiratory systems performance. Results: Twenty healthy volunteers, twenty people with COPD and five subjects suffering bronchial asthma, during periods without crisis, were studied. The dispersion within the group of each patient was not significant, but a notable difference can be observed between the healthy volunteers and the other people studied. A "normal region" can be set with clearly defined borders. Conclusion: The proposed solution seems promising for evaluating respiratory function even when it is compensated by a cardiac response, making it easier to identify people suffering from respiratory diseases and who may appear normal or healthy.

show abstract

“…For instance, Fleming et al [21] Figure 2: Principle of the PPG signals (adapted from [20]). present a comparative analysis of different signal processing techniques to extract breathing rate from the PPG.In addition to this, PPG signal has been also been used to extract the breathing rate [22] [23] [24]. Whether used to monitor breathing rate or blood pressure, a common noninvasive technique to gather PPG signal is through the fingertip of the patient.…”

Section: Motivationmentioning

confidence: 99%

Pervasive blood pressure monitoring using Photoplethysmogram (PPG) sensor

Riaz¹,

Azad

Arshad

et al. 2019

Future Generation Computer Systems

View full text Add to dashboard Cite

Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices.

show abstract

On how to extract breathing rate from PPG signal using wearable devices

Cited by 27 publications

References 8 publications

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

A Novel Approach to the Respiratory Disease Follow-up Based on Plethhysmography Signal Parameters

Pervasive blood pressure monitoring using Photoplethysmogram (PPG) sensor

Contact Info

Product

Resources

About