Classification of the quality of wristband-based photoplethysmography signals

Pradhan, Nikhilesh; Rajan, Sreeraman; Adler, Andy; Redpath, Calum J.

doi:10.1109/memea.2017.7985887

Cited by 14 publications

(12 citation statements)

References 5 publications

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“…Zero-Crossing Rate [F 25 ], as shown in [33], it was expected that the number of times that the PPG signal crosses zero would increase when an artifact occurred, as the PPG signal would have a more chaotic behaviour than clean PPG.…”

Section: Features Extractionmentioning

confidence: 99%

Characterization of Artifact Signals in Neck Photoplethysmography

García-López

Rodriguez‐Villegas

2020

IEEE Trans. Biomed. Eng.

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Objective: The neck is a very attractive measurement location for multimodal physiological monitoring, since it offers the possibility of extracting clinically relevant parameters, which cannot be obtained from other body locations, such as lung volumes. It is for this reason that obtaining PPG from the neck would be of interest. PPG signals, however, are very susceptible to artifacts which greatly compromise their quality. But the extent of this is going to depend on, the nature of the artifacts and the strength of the sensed signal, both of which are location dependent. This paper presents for the first time the characterization of artifacts affecting neck PPG signals. Methods: Neck PPG data was recorded from 19 participants, who performed ten different activities to deliberately introduce common artifacts. 41 PPG features were extracted and statistically analyzed to investigate which ones showed the greatest ability to differentiate normal PPG from each artifact. A customized minimum Redundancy Maximum Relevance (mRMR) feature selection approach was implemented, to select the top 10 features. Results: Artifacts caused by Swallowing, Yawning and Coughing exhibited larger Spectral Entropy, Average Power and smaller Spectral Kurtosis, than normal PPG. Head movement artifacts, also demonstrated highly disordered and noisy frequency spectra, and were characterized by having larger and irregular time domain features. In addition, the analysis showed that different respiratory states that could be of clinical interests, such as presence of apneas, were also distinguishable from sources of interference. Significance: These findings are important for the development of PPG denoising algorithms and subsequent obtention of biomarkers of interest, or alternatively for applications where the events of interest are the artifacts themselves.

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Section: Features Extractionmentioning

confidence: 99%

Characterization of Artifact Signals in Neck Photoplethysmography

García-López

Rodriguez‐Villegas

2020

IEEE Trans. Biomed. Eng.

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“…According to the histogram, the feature provides high discriminability between classes 1 and 5. This is similar to results from our preliminary study [2]. The table of AUC values in Table 5.41 further indicates high discriminability of classes 1, 4, and 5 from each other, as well as from all other classes.…”

Section: Value Of First Autocorrelation Peak (Acpeakvals1)supporting

confidence: 87%

“…According to the histogram, the feature provides high discriminability between classes 1 and 5. This is similar to results from our preliminary study [2]. The table of AUC values in Table 5.42 indicates results similar to the ACPeakVals1 feature, with the notable exception of the poor discrimination between class 1 and 2 combination, in which there is an AUC value of 0.334.…”

Section: Value Of Second Autocorrelation Peak (Acpeakvals2)supporting

confidence: 85%

“…It was postulated that lower quality classes would produce noisier instantaneous frequency plots, thus the number of median crossings were used as a feature. The use of this feature was inspired by preliminary work conducted by the research group [2].…”

Section: Signal Quality Classesmentioning

confidence: 99%

“…The autocorrelation and location (in time) at each of the peaks are computed, and used as features. These features were inspired by the work of Wander and Morris [21], and previously used in preliminary work done by our research team [2].…”

Section: Correlogram Featuresmentioning

confidence: 99%

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Evaluation of the Signal Quality of Wrist-Based Photoplethysmography

Pradhan¹

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Detection of paroxysmal atrial fibrillation requires continuous cardiovascular monitoring due to its episodic nature. Such monitoring is impractical with electrocardiogram Holter monitors, which are the currently employed for ambulatory cardiovascular monitoring, but are cumbersome for prolonged use. This thesis studies monitoring using photoplethysmography (PPG) devices, which may be embedded into wristband devices which can be easily worn continuously. However, the quality of wrist-based PPG is highly variable, and is subject to artifacts from motion and other interferences.The goal of this thesis is to evaluate the signal quality obtained from wrist-based PPG when used in an ambulatory setting. Ambulatory data is collected over a 24-hour period for 10 elderly, and 16 non-elderly participants. Visual assessment is used as the gold standard for PPG signal quality, with Fleiss's Kappa being used to evaluate the agreement between raters. With this gold standard, 5 classifiers are evaluated using a modified 13-fold cross-validation approach. Based on this evaluation, a Random Forest quality classification algorithm is selected, with an accuracy of 74.5%. The algorithm is used to evaluate the ambulatory use of wrist-based PPG over a 24-hour period. Overall, it is found that data quality is high at night, and low during the day.

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Artifacts classification and apnea events detection in neck photoplethysmography signals

García-López

Pramono²,

Rodriguez‐Villegas³

2022

Med Biol Eng Comput

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The novel pulse oximetry measurement site of the neck is a promising location for multi-modal physiological monitoring. Specifically, in the context of respiratory monitoring, in which it is important to have direct information about airflow. The neck makes this possible, in contrast to common photoplethysmography (PPG) sensing sites. However, this PPG signal is susceptible to artifacts that critically impair the signal quality. To fully exploit neck PPG for reliable physiological parameters extraction and apneas monitoring, this paper aims to develop two classification algorithms for artifacts and apnea detection. Features from the time, correlogram, and frequency domains were extracted. Two SVM classifiers with RBF kernels were trained for different window (W) lengths and thresholds (Thd) of corruption. For artifacts classification, the maximum performance was attained for the parameters combination of [W = 6s-Thd= 20%], with an average accuracy= 85.84%(ACC), sensitivity= 85.43%(SE) and specificity= 86.26%(SP). For apnea detection, the model [W = 10s-Thd= 50%] maximized all the performance metrics significantly (ACC= 88.25%, SE= 89.03%, SP= 87.42%). The findings of this proof of concept are significant for denoising novel neck PPG signals, and demonstrate, for the first time, that it is possible to promptly detect apnea events from neck PPG signals in an instantaneous manner. This could make a big impact in crucial real-time applications, like devices to prevent sudden-unexpected-death-in-epilepsy (SUDEP).

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Classification of the quality of wristband-based photoplethysmography signals

Cited by 14 publications

References 5 publications

Characterization of Artifact Signals in Neck Photoplethysmography

Characterization of Artifact Signals in Neck Photoplethysmography

Evaluation of the Signal Quality of Wrist-Based Photoplethysmography

Artifacts classification and apnea events detection in neck photoplethysmography signals

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