Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals

Shuzan, Md. Nazmul Islam; Chowdhury, Moajjem Hossain; Chowdhury, Muhammad E. H.; Murugappan, M.; Bhuiyan, Enamul; Ayari, Mohamed Arselene; Khandakar, Amith

doi:10.3390/bioengineering10020167

Cited by 22 publications

(6 citation statements)

References 31 publications

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“…The algorithm's performance can be improved further by adding more accurate R-peak detection algorithms and sophisticated QRS identifiers to detect and replace erroneous or abnormal heartbeats automatically. In addition, the algorithm can be combined with predictive artificial intelligence models to accurately identify impending episodes of apnea or cardio-respiratory distress based on RR changes 37 – 40 .…”

Section: Discussionmentioning

confidence: 99%

Open-source software for respiratory rate estimation using single-lead electrocardiograms

Roberts,

Walton,

Loyer

et al. 2024

Sci Rep

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Respiratory rate (RR) is a critical vital sign used to assess pulmonary function. Currently, RR estimating instrumentation is specialized and bulky, therefore unsuitable for remote health monitoring. Previously, RR was estimated using proprietary software that extract surface electrocardiogram (ECG) waveform features obtained at several thoracic locations. However, developing a non-proprietary method that uses minimal ECG leads, generally available from mobile cardiac monitors is highly desirable. Here, we introduce an open-source and well-documented Python-based algorithm that estimates RR requiring only single-stream ECG signals. The algorithm was first developed using ECGs from awake, spontaneously breathing adult human subjects. The algorithm-estimated RRs exhibited close linear correlation to the subjects’ true RR values demonstrating an R2 of 0.9092 and root mean square error of 2.2 bpm. The algorithm robustness was then tested using ECGs generated by the ischemic hearts of anesthetized, mechanically ventilated sheep. Although the ECG waveforms during ischemia exhibited severe morphologic changes, the algorithm-determined RRs exhibited high fidelity with a resolution of 1 bpm, an absolute error of 0.07 ± 0.07 bpm, and a relative error of 0.67 ± 0.64%. This optimized Python-based RR estimation technique will likely be widely adapted for remote lung function assessment in patients with cardiopulmonary disease.

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Section: Discussionmentioning

confidence: 99%

Open-source software for respiratory rate estimation using single-lead electrocardiograms

Roberts,

Walton,

Loyer

et al. 2024

Sci Rep

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“…Aly et al 43 utilized the accelerometer and gyroscope of a mobile phone held on a human chest to extract RR, while 44 used the discrete wavelet transform to measure RR from video recorded from the fingertip, extracting the vPPG. Moreover, several studies have investigated deep learning (DL)-based approaches for RR estimation, with Shuzan et al 45 recently investigating 19 DL models for estimating RR and HR, with the Gaussian process regression model demonstrating the best performance.…”

Section: Video Ppg-based Vitals Estimationmentioning

confidence: 99%

Your smartphone could act as a pulse-oximeter and as a single-lead ECG

Mehmood,

Sarouji,

Rahman

et al. 2023

Preprint

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In the post-covid19 era, every new wave of the pandemic causes an increased concern/interest among the masses to learnmore about their state of well-being. Therefore, it is the need of the hour to come up with ubiquitous, low-cost, non-invasivetools for rapid and continuous monitoring of body vitals that reflect the status of one’s overall health. In this backdrop, this workproposes a deep learning approach to turn a smartphone—the popular hand-held personal gadget—into a diagnostic tool tomeasure/monitor the three most important body vitals, i.e., pulse rate (PR), blood oxygen saturation level (aka SpO2), andrespiratory rate (RR). Furthermore, we propose another method that could extract a single-lead electrocardiograph (ECG)of the subject. The proposed methods include the following core steps: subject records a small video of his/her fingertip byplacing his/her finger on the rear camera of the smartphone, and the recorded video is pre-processed to extract the filteredand/or detrended video-photoplethysmography (vPPG) signal, which is then fed to custom-built convolutional neural networks(CNN), which eventually spit-out the vitals (PR, SpO2, and RR) as well as a single-lead ECG of the subject. To be precise,the contribution of this paper is two-fold: 1) estimation of the three body vitals (PR, SpO2, RR) from the vPPG data usingcustom-built CNNs, vision transformer, and most importantly by CLIP model (a popular image-caption-generator model); 2) anovel discrete cosine transform+feedforward neural network-based method that translates the recorded video-PPG signal to asingle-lead ECG signal. The significance of this work is two-fold: i) it allows rapid self-testing of body vitals (e.g., self-monitoringfor covid19 symptoms), ii) it enables rapid self-acquisition of a single-lead ECG, and thus allows early detection of atrialfibrillation (abormal heart beat or arrhythmia), which in turn could enable early intervention in response to a range of cardiovascular diseases,and could help save many precious lives. Our work could help reduce the burden on healthcare facilities and could lead toreduction in health insurance costs.

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“…Such prototypes are of good use for wearable respiration monitoring using optical technology; however, they suffer from the same disadvantages as traditional PPG technology. Similarly, there is a plethora of research focused on SpO 2 estimation using machine learning and deep learning models, as well as calculations based on PPG signals [20,21], but none based on real-time respiration signals. Our approach overcomes some of the mentioned drawbacks of using PPG signals, as well as the need for preprocessing of PPG signals when used in ML/ANN/DNN models [22].…”

Section: Introductionmentioning

confidence: 99%

Blood Oxygen Saturation Estimation with Laser-Induced Graphene Respiration Sensor

Madevska Bogdanova,

Koteska,

Vićentić

et al. 2024

Journal of Sensors

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Measuring blood oxygen saturation (SpO2) is crucial in a triage process for identifying patients with respiratory distress or shock, since low SpO2 levels indicate compromised hemostability and the need for priority treatment. This paper explores the use of wearable mechanical deflection sensors based on laser-induced graphene (LIG) for SpO2 estimation. The LIG sensors are attached to a subject’s chest for real-time monitoring of respiratory signals. We have developed a novel database of the respiratory signals, with corresponding SpO2 values ranging from 86% to 100%. The database is used to develop an artificial neural network model for SpO2 estimation. The neural network performance is promising, with regression metrics mean squared error = 0.184, mean absolute error = 0.301, root mean squared error = 0.429, and R-squared = 0.804. The use of mechanical respiration sensors in combination with neural networks in biosensing opens new possibilities for noninvasive SpO2 monitoring and other innovative applications.

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Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals

Cited by 22 publications

References 31 publications

Open-source software for respiratory rate estimation using single-lead electrocardiograms

Open-source software for respiratory rate estimation using single-lead electrocardiograms

Your smartphone could act as a pulse-oximeter and as a single-lead ECG

Blood Oxygen Saturation Estimation with Laser-Induced Graphene Respiration Sensor

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