wireless amplifier for 3-lead ecg  (BIOPAC)

Journal: IEEE journal of biomedical and health informatics

Article Title: Automatic Detection of Target Engagement in Transcutaneous Cervical Vagal Nerve Stimulation for Traumatic Stress Triggers

doi: 10.1109/JBHI.2020.2981116

Figure Lengend Snippet: (a) ECG, PPG, SCG, RSP signals were processed and HR, PAT, PEP, PPG amplitude, RR, RW, RP were extracted as physiological parameters. (b) Using the extracted parameters, dataset constructed after normalization, resampling, and windowing. (c) After standardization, dimensionality reduction methods were applied for dataset visualization. Then, feature selection and machine learning were conducted. PATF: PATFOOT; PATP: PATPEAK; AO: Aortic opening; PPGA: PPG amplitude.

Article Snippet: ECG, SCG, PPG, RSP signals were collected continuously throughout the protocol using wireless amplifiers for 3-lead ECG, RSP, and transmissive PPG signals (Bionomadix RSPEC-R and PPGED-R, Biopac Systems, Goleta, CA).

Techniques: Construct, Selection

Journal: IEEE journal of biomedical and health informatics

Article Title: Automatic Detection of Target Engagement in Transcutaneous Cervical Vagal Nerve Stimulation for Traumatic Stress Triggers

doi: 10.1109/JBHI.2020.2981116

Figure Lengend Snippet: Dimensionality reduction and classification outcomes for separating the stimulus types: active tcVNS and sham. (a) Dimensionality reduction applied to the high-dimensional feature matrix using t-SNE constructed from features from ECG and PPG. (b) Number of Top features selected using ANOVA F-score-based feature selection versus receiver operator characteristics (ROC) area under curve (AUC). ROC AUC is robust to Top Features from 70 to 88. c) Confusion matrix for the classifier, obtained with LOSO-CV and minimum number of features (71). (d) Receiver operator characteristics (ROC) for the classifier. A ROC area under curve (AUC) of 0.96 was obtained. Classification outcomes vary minorly with Top Features from 70 to 88.

Article Snippet: ECG, SCG, PPG, RSP signals were collected continuously throughout the protocol using wireless amplifiers for 3-lead ECG, RSP, and transmissive PPG signals (Bionomadix RSPEC-R and PPGED-R, Biopac Systems, Goleta, CA).

Techniques: Construct, Selection

Journal: IEEE journal of biomedical and health informatics

Article Title: Automatic Detection of Target Engagement in Transcutaneous Cervical Vagal Nerve Stimulation for Traumatic Stress Triggers

doi: 10.1109/JBHI.2020.2981116

Figure Lengend Snippet: Top 5 features sorted by ANOVA F-values and their boxplots grouped by sham and active tcVNS classes. The top features were calculated from the full feature set obtained from ECG and PPG sensors.

Article Snippet: ECG, SCG, PPG, RSP signals were collected continuously throughout the protocol using wireless amplifiers for 3-lead ECG, RSP, and transmissive PPG signals (Bionomadix RSPEC-R and PPGED-R, Biopac Systems, Goleta, CA).

Techniques:

Journal: IEEE journal of biomedical and health informatics

Article Title: Automatic Detection of Target Engagement in Transcutaneous Cervical Vagal Nerve Stimulation for Traumatic Stress Triggers

doi: 10.1109/JBHI.2020.2981116

Figure Lengend Snippet: (a) ECG, PPG, SCG, RSP signals were processed and HR, PAT, PEP, PPG amplitude, RR, RW, RP were extracted as physiological parameters. (b) Using the extracted parameters, dataset constructed after normalization, resampling, and windowing. (c) After standardization, dimensionality reduction methods were applied for dataset visualization. Then, feature selection and machine learning were conducted. PATF: PATFOOT; PATP: PATPEAK; AO: Aortic opening; PPGA: PPG amplitude.

Article Snippet: D emographics and B aseline P hysiological P arameters C. Physiological Monitoring ECG, SCG, PPG, RSP signals were collected continuously throughout the protocol using wireless amplifiers for 3-lead ECG, RSP, and transmissive PPG signals (Bionomadix RSPEC-R and PPGED-R, Biopac Systems, Goleta, CA).

Techniques: Construct, Selection

Journal: IEEE sensors journal

Article Title: Fusing Near-Infrared Spectroscopy with Wearable Hemodynamic Measurements Improves Classification of Mental Stress

doi: 10.1109/jsen.2018.2872651

Figure Lengend Snippet: a) Illustration of the sensing modalities attached to each subject: headband PPG, NIRS, ECG, SCG signals were collected throughout the mental stress protocol. b) Block-diagrams of each sensing modality. The headband includes PPG and NIRS parts. c) Block diagram of signal processing and feature extraction. d) Summary of peripherally-measured cardiovascular parameters. HR, R-Ao (PEP), PTT, PAT, PPG amplitude were extracted from the peripherally-measured cardiovascular sensing part.

Article Snippet: For ECG data collection, a commercially available wireless 3-lead ECG amplifier was used (RSPEC-R, Biopac Systems, Goleta, CA).

Techniques: Blocking Assay, Extraction