Characterization of Artifact Signals in Neck Photoplethysmography

Abstract

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.

Artifacts classification and apnea events detection in neck photoplethysmography signals

“…In the specific case of neck PPG, we have previously defined and characterized the most common neck PPG artifacts [14]. However, in our previous work and any previous research, there is no evidence of the development of algorithms for neck PPG artifacts classification.…”

“…In addition, we recently found that neck PPG signals were more strongly modulated by the respiratory frequency than finger PPG [7]. This makes the identification of different breathing states of interest very clear, specially when having at hand the most discriminative features [14].…”

“…For example, in Sudden Unexpected Death in Epilepsy (SUDEP), the prompt detection of apneic events could be a matter of life or death. Neck PPG signals could offer a solution to the current limitations, as apneic respiratory arrests can be instantaneously recognized by monitoring time and frequency features [7,14].…”

Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks

“…Despite ECG waveform, the PPG waveform is highly contingent on the measurement location [ 25 ] and is overall subject-dependent [ 1 , 18 ]. In addition, systematic noise due to motion artifacts [ 26 , 27 , 28 ] can easily distort the waveform by, for example, flattening the end of the diastolic phase, making traditional processing techniques unreliable [ 29 ]. Adaptive filtering techniques based on independent component analysis and Kalman filtering were proved unreliable in the presence of arrhythmia, as in the case of atrial fibrillation [ 12 ].…”

Proof of Concept of a Novel Neck-Situated Wearable PPG System for Continuous Physiological Monitoring

“…Previous work using neck-based PPG has been very limited, only demonstrating the feasibility of extracting signals [23,24,25] characterizing the waveform [26], and analyzing potential artefacts and exploring the optimal location [24,27]. This paper advances the state-of-the-art by presenting for the first time the a proof-of-concept of a wearable system for PPG signal acquisition, together with analysis algorithms for extraction of heart rate (HR), respiratory rate (RR), and peripheral oxygen saturation (SpO2).…”