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Flowchart of our proposed method. Green boxes indicate fiducial points detection. Yellow boxes indicate artifact detection.
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With the rise of the concept of smart cities and healthcare, artificial intelligence helps people pay increasing attention to the health of themselves. People can wear a variety of wearable devices to monitor their physiological conditions. The pulse wave is a kind of physiological signal which is widely applied in the physiological monitoring syst...
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Citations
... Raw VitalRecorder waveform data may be prone to unprocessed artifacts, which could have a detrimental effect on data analysis because they are not indicative of the patient's true physiological status and should be eliminated. 18,52,53 We extensively reviewed the VitalRecorder files to identify patterns of unwanted artifacts (e.g., those caused by sensor detachment or disconnection) and found that critical artifacts could be removed using a rule-based approach. Figure S3 shows examples of the artifacts, and Table S9 lists the rules adopted to remove these artifacts. ...
Early identification of patients at high risk of delirium is crucial for its prevention. Our study aimed to develop machine learning models to predict delirium after cardiac surgery using intraoperative biosignals and clinical data. We introduced a novel approach to extract relevant features from continuously measured intraoperative biosignals. These features reflect the patient’s overall or baseline status, the extent of unfavorable conditions encountered intraoperatively, and beat-to-beat variability within the data. We developed a soft voting ensemble machine learning model using retrospective data from 1,912 patients. The model was then prospectively validated with data from 202 additional patients, achieving a high performance with an area under the receiver operating characteristic curve of 0.887 and an accuracy of 0.881. According to the SHapley Additive exPlanation method, several intraoperative biosignal features had high feature importance, suggesting that intraoperative patient management plays a crucial role in preventing delirium after cardiac surgery.
... Following this approach, several machine learning algorithms have been proposed in the literature to discriminate artifacts from clean PPG. Examples of signal processing techniques used in these algorithms include: decision lists [39][40][41][42][43], decision trees [44,45], naïve Bayes classifiers [46], support vector machines (SVM) [36,[47][48][49][50], multi-layered perceptrons [51], personalized neural networks (NN) [52], and 1-D CNNs [53,54]. ...
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).
Recent advances in Critical Congenital Heart Disease (CCHD) research using Photoplethysmography (PPG) signals have yielded an Internet of Things (IoT) based enhanced screening method that performs CCHD detection comparable to SpO2 screening. The use of PPG signals, however, poses a challenge due to its measurements being prone to artifacts. To comprehensively study the most effective way to remove the artifact segments from PPG waveforms, we performed feature engineering and investigated both Machine Learning (ML) and rule based algorithms to identify the optimal method of artifact detection. Our proposed artifact detection system utilizes a 3-stage ML model that incorporates both Gradient Boosting (GB) and Random Forest (RF). The proposed system achieved 84.01% of Intersection over Union (IoU), which is competitive to state-of-the-art artifact detection methods tested on higher resolution PPG.
