Journal: Data in Brief

Article Title: Physiological responses to emotional video stimuli: ECG, EDA, and temperature data

doi: 10.1016/j.dib.2026.112720

Figure Lengend Snippet: BITalino (r)evolution biosignal acquisition board.

Article Snippet: Data collection , Data collection was performed using BITalino (r)evolution boards (PLUX Wireless Biosignals, S.A.), which recorded ECG, EDA, skin temperature, and accelerometer signals. Participants wore the NK-G04E-VR Virtual Reality Glasses during the experiment to view 18 emotionally evocative videos designed to elicit a range of emotions. Data were captured at a 1000 Hz sampling rate and stored with timestamps. Demographic data (age, gender, health conditions) were also collected. Participants were healthy adults, and those with neurological disorders were excluded. No data normalization was applied..

Techniques:

Journal: Data in Brief

Article Title: Physiological responses to emotional video stimuli: ECG, EDA, and temperature data

doi: 10.1016/j.dib.2026.112720

Figure Lengend Snippet: Mobile application (BitalinoScientiSST) interface used during data acquisition. (a) The main acquisition screen is used to start and stop recording after the participant ID is entered. (b) The Bluetooth device discovery and pairing screen is used to connect the BITalino device. (c) New-segment action used immediately before each video stimulus to split the session into stimulus-aligned recordings. (d) Example of the acquisition screen after creating a new segment for the next stimulus. Pressing New creates a new output file bitalino_n.txt ( n = 1 to 18), with one file per video clip, matching the file organisation described in the Data Description section.

Article Snippet: Data collection , Data collection was performed using BITalino (r)evolution boards (PLUX Wireless Biosignals, S.A.), which recorded ECG, EDA, skin temperature, and accelerometer signals. Participants wore the NK-G04E-VR Virtual Reality Glasses during the experiment to view 18 emotionally evocative videos designed to elicit a range of emotions. Data were captured at a 1000 Hz sampling rate and stored with timestamps. Demographic data (age, gender, health conditions) were also collected. Participants were healthy adults, and those with neurological disorders were excluded. No data normalization was applied..

Techniques:

Journal: medRxiv

Article Title: Pharmacological enhancement of glymphatic function in humans increases the clearance of Alzheimer’s disease-related proteins

doi: 10.64898/2026.03.10.26348048

Figure Lengend Snippet: The Dexmedetomidine Trial was conducted at Stanford University’s Clinical and Translational Research Unit (CTRU), while the ACX-02 Clinical Trial was conducted at both Stanford University’s CTRU and Washington State University’s Sleep and Performance Research Center. Both trials used a cross-over design - treatment followed by placebo - to assess the effect of DEX on the glymphatic clearance of Aβ and tau from the brain to the blood. (A) The Dexmedetomidine Trial enrolled nine participants, of whom eight completed both study visits. (B) The ACX-02 Trial enrolled 22 participants, eight at Stanford University and 14 at Washington State University. Of the eight participants at Stanford, six completed both study visits. At Washington State University, 11 of 14 participants completed both study visits. (C) Illustration of the determinants of glymphatic clearance of Aβ and tau during wake, NREM sleep, DEX and ACX-02 treatment. To capture these physiological determinants, participants were instrumented with ECG telemetry, percutaneous oxygen saturation (SpO₂), nasal cannula for low-flow oxygen and continuous end-tidal CO₂ (EtCO₂) monitoring, and a radial arterial line for continuous blood pressure monitoring and blood sampling (Philips IntelliVue MP50). An intravenous line was placed for drug or saline placebo infusion. Participants were also fitted with an investigational in-ear wearable device from Applied Cognition¹⁷ that measured key determinants of glymphatic function, including sleep features (hypnogram and spectral band power) by EEG, heart rate variability (HRV) by photoplethysmography (PPG), cerebrovascular pulse transit time (PTT cereb ) by impedance plethysmography (IPG), and brain parenchymal resistance (R P ) by dynamic electrical impedance spectroscopy.

Article Snippet: Participants were instrumented for monitoring by electrocardiography (ECG) telemetry, percutaneous oxygen saturation (SpO 2 ), nasal canula for administration of supplemental oxygen at 2 liters per minute (LPM) and continuous end-tidal CO 2 (EtCO 2 ) monitoring, and a 20 gauge (g) radial arterial catheter at the wrist for continuous systemic blood pressure measurement and blood sampling (Philips IntelliVue MP50 Patient Monitor).

Techniques: Clinical Proteomics, Sampling, Saline, Impedance Spectroscopy

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Architecture of the SimCardioNet model for ECG classification, integrating pre-processing, CNN-based feature extraction, contrastive learning through SimCLR, and classification using ResNet blocks.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Extraction

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: PCA visualization of the 128-dimensional projection head features, showing the separation of ECG classes along the first two principal components.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques:

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: PCA visualization of self-supervised ECG feature representations from the Dataset III, illustrating class-wise distributions and overlap among normal and pathological conditions.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques:

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: t-SNE visualization of the 128-dimensional projection head features, illustrating the clustering and separation of ECG classes in a 2D space.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques:

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: t-SNE projection of self-supervised ECG feature embeddings from the Dataset III, illustrating local clustering behavior and class-wise distribution of normal and pathological recordings.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques:

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Conv1 activation maps for sample (Class: 0), showing the extraction of low-level features such as rhythm and shape from the ECG signal, with activation focused on specific regions of the waveform of Dataset I.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Activation Assay, Extraction

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Conv1 activation maps for sample (Class: 0), showing the extraction of low-level features such as rhythm and shape from the ECG signal, with activation focused on specific regions of the waveform of Dataset II.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Activation Assay, Extraction

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Conv2 activation maps for sample (Class: 0), highlighting intermediate-level features such as the P-QRS-T wave patterns and focusing on the shape of the ECG waveform Dataset I.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Activation Assay

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Conv2 activation maps for sample (Class: 0), highlighting intermediate-level features such as the P-QRS-T wave patterns and focusing on the shape of the ECG waveform Dataset II.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Activation Assay

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Conv3 activation maps for sample (Class: 0), highlighting deep features and advanced patterns in the ECG signal, such as arrhythmias and myocardial infarctions Dataset I.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Activation Assay

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Conv3 activation maps for sample (Class: 0), highlighting deep features and advanced patterns in the ECG signal, such as arrhythmias and myocardial infarctions of Dataset II.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Activation Assay

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Visualization of conv1 activation maps for a normal ECG recording, showing channel-wise responses of the first convolutional layer to different temporal and morphological signal patterns.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Activation Assay

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Mean confusion matrix for cross-validation (CV) in multi-class ECG classification of Dataset I.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Biomarker Discovery

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Mean confusion matrix for cross-validation (CV) in multi-class ECG classification of Dataset II.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Biomarker Discovery

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Mean confusion matrix for cross-validation in multi-class ECG classification of Dataset III.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Biomarker Discovery

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: ECG waveform samples with true and predicted labels.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques:

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Grad-CAM heatmaps highlighting regions driving the model’s predictions. Warm colors indicate higher contribution; overlays show attention focused on ECG waveforms rather than page artifacts od Dataset I.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques:

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Grad-CAM heatmaps highlighting regions driving the model’s predictions. Warm colors indicate higher contribution; overlays show attention focused on ECG waveforms rather than page artifacts od Dataset II.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques:

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Grad-CAM heatmaps highlighting regions driving the model’s predictions. Warm colors indicate higher contribution; overlays show attention focused on ECG waveforms rather than page artifacts od Dataset III.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: