A Machine Learning Driven Pipeline for Automated Photoplethysmogram Signal Artifact Detection

Oliveira, Luca Cerny; Lai, Zhengfeng; Geng, Wenbo; Siefkes, Heather; Chuah, Chen-Nee

doi:10.1109/chase52844.2021.00035

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Our motion artifact ML model has been previously described in more detail. 12 Briefly, this model used multiple photoplethysmography recordings from 1 hand and 1 foot of 21 newborns (a subset from the cohort presented here) that were labeled for artifacts by 3 trained observers. A total of 6 hours and 42 minutes of recordings, which included 57 658 beats, were used to train and test the artifact detection model.…”

Section: Methodsmentioning

confidence: 99%

“…These features include but are not limited to systolic phase duration, diastolic phase duration, and dynamic time‐warped Euclidean distance that describe the pulse shape and how they relate to neighboring pulses. 12 Random forest was chosen as the classifier. The best features were chosen through recursive feature elimination in 5‐fold cross‐validation.…”

Section: Methodsmentioning

confidence: 99%

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Machine Learning–Based Critical Congenital Heart Disease Screening Using Dual‐Site Pulse Oximetry Measurements

Siefkes,

Oliveira,

Koppel

et al. 2024

JAHA

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Background Oxygen saturation (Sp o 2 ) screening has not led to earlier detection of critical congenital heart disease (CCHD). Adding pulse oximetry features (ie, perfusion data and radiofemoral pulse delay) may improve CCHD detection, especially coarctation of the aorta (CoA). We developed and tested a machine learning (ML) pulse oximetry algorithm to enhance CCHD detection. Methods and Results Six sites prospectively enrolled newborns with and without CCHD and recorded simultaneous pre‐ and postductal pulse oximetry. We focused on models at 1 versus 2 time points and with/without pulse delay for our ML algorithms. The sensitivity, specificity, and area under the receiver operating characteristic curve were compared between the Sp o 2 ‐alone and ML algorithms. A total of 523 newborns were enrolled (no CHD, 317; CHD, 74; CCHD, 132, of whom 21 had isolated CoA). When applying the Sp o 2 ‐alone algorithm to all patients, 26.2% of CCHD would be missed. We narrowed the sample to patients with both 2 time point measurements and pulse‐delay data (no CHD, 65; CCHD, 14) to compare ML performance. Among these patients, sensitivity for CCHD detection increased with both the addition of pulse delay and a second time point. All ML models had 100% specificity. With a 2‐time‐points+pulse‐delay model, CCHD sensitivity increased to 92.86% ( P =0.25) compared with Sp o 2 alone (71.43%), and CoA increased to 66.67% ( P =0.5) from 0. The area under the receiver operating characteristic curve for CCHD and CoA detection significantly improved (0.96 versus 0.83 for CCHD, 0.83 versus 0.48 for CoA; both P =0.03) using the 2‐time‐points+pulse‐delay model compared with Sp o 2 alone. Conclusions ML pulse oximetry that combines oxygenation, perfusion data, and pulse delay at 2 time points may improve detection of CCHD and CoA within 48 hours after birth. Registration URL: https://www.clinicaltrials.gov/study/NCT04056104?term=NCT04056104&rank=1 ; Unique identifier: NCT04056104.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Machine Learning–Based Critical Congenital Heart Disease Screening Using Dual‐Site Pulse Oximetry Measurements

Siefkes,

Oliveira,

Koppel

et al. 2024

JAHA

Self Cite

View full text Add to dashboard Cite

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Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A Review

Argüello-Prada,

Castillo García

2024

Sensors

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Machine learning algorithms have brought remarkable advancements in detecting motion artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference data. However, no study has provided a synthesis of these methods, let alone an in-depth discussion to aid in deciding which one is more suitable for a specific purpose. This narrative review examines the application of machine learning techniques for the reference signal-less detection of MAs in PPG signals. We did not consider articles introducing signal filtering or decomposition algorithms without previous identification of corrupted segments. Studies on MA-detecting approaches utilizing multiple channels and additional sensors such as accelerometers were also excluded. Despite its promising results, the literature on this topic shows several limitations and inconsistencies, particularly those regarding the model development and testing process and the measures used by authors to support the method’s suitability for real-time applications. Moreover, there is a need for broader exploration and validation across different body parts and a standardized set of experiments specifically designed to test and validate MA detection approaches. It is essential to provide enough elements to enable researchers and developers to objectively assess the reliability and applicability of these methods and, therefore, obtain the most out of them.

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A Machine Learning Driven Pipeline for Automated Photoplethysmogram Signal Artifact Detection

Cited by 2 publications

References 15 publications

Machine Learning–Based Critical Congenital Heart Disease Screening Using Dual‐Site Pulse Oximetry Measurements

Machine Learning–Based Critical Congenital Heart Disease Screening Using Dual‐Site Pulse Oximetry Measurements

Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A Review

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