lead ecg machines  (Philips Healthcare)

Journal: International Journal of Cardiology. Cardiovascular Risk and Prevention

Article Title: Deep learning model for identifying significant tricuspid regurgitation using standard 12-lead electrocardiogram

doi: 10.1016/j.ijcrp.2025.200557

Figure Lengend Snippet: Comparison of receiver operating characteristic curves Model_A: ECG signals + sex, age Model_B: Model_A + RR, QRSd, QTc, Afib, Hypertension Model_C: Model_B + Dyslipidemia, DM, CHF, CKD, COPD, Previous Stroke, Previous AMI, PAOD AUC = area under the receiver operating characteristic curve.

Article Snippet: All ECGs were acquired as digital standard 12-lead ECGs using Philips PageWriter TC70 ECG machine (Philips Healthcare, Andover, MA, USA).

Techniques: Comparison

Journal: Europace

Article Title: QTcNet: a deep learning model for direct heart rate corrected QT interval estimation

doi: 10.1093/europace/euaf274

Figure Lengend Snippet: Comparison of algorithm-based QTc estimates (machine measurements), QTcNet predictions, and expert annotations on the QTcMS and EDMS datasets. Panel A shows QTcNet predictions ( x -axis) vs. machine measurements ( y -axis) on EDMS; panel D shows machine measurements ( x -axis) vs. expert annotations ( y -axis) on QTcMS; and panel G presents QTcNet predictions ( x -axis) vs. expert annotations ( y -axis). Each point corresponds to an individual ECG, with the diagonal line marking perfect agreement. Six outlier ECGs are highlighted in blue, black, purple, red, yellow, and green on the scatter plots. Their corresponding waveforms (lead II) are displayed in panels B , C , E , F , H , and I , each with borders in the matching colour. Outliers were reduced with QTcNet despite high levels of noise and artefacts (panel G ).

Article Snippet: The dataset encompasses a high variability of clinical conditions and signal characteristics of ECG machines from various manufacturers (including Burdick/Spacelabs, Philips, and General Electric).

Techniques: Comparison

Journal: Europace

Article Title: QTcNet: a deep learning model for direct heart rate corrected QT interval estimation

doi: 10.1093/europace/euaf274

Figure Lengend Snippet: Integrated Gradients (IG) maps illustrating feature relevance for QTc prediction. Panel A shows dataset-wide aggregated importance scores computed using IG for each ECG lead and aligned to median beats. Black lines represent the average ECG signals across the test set, while the red overlay highlights regions the model identified as relevant for QTc prediction. Darker areas indicate higher attribution values. Panels B–D display three individual test set ECGs alongside their respective importance scores, illustrating how the model’s focus can vary across different signal morphologies.

Article Snippet: The dataset encompasses a high variability of clinical conditions and signal characteristics of ECG machines from various manufacturers (including Burdick/Spacelabs, Philips, and General Electric).

Techniques:

Journal: Europace

Article Title: QTcNet: a deep learning model for direct heart rate corrected QT interval estimation

doi: 10.1093/europace/euaf274

Figure Lengend Snippet: Comparison of algorithm-based QTc estimates (machine measurements), QTcNet predictions, and expert annotations on the QTcMS and EDMS datasets. Panel A shows QTcNet predictions ( x -axis) vs. machine measurements ( y -axis) on EDMS; panel D shows machine measurements ( x -axis) vs. expert annotations ( y -axis) on QTcMS; and panel G presents QTcNet predictions ( x -axis) vs. expert annotations ( y -axis). Each point corresponds to an individual ECG, with the diagonal line marking perfect agreement. Six outlier ECGs are highlighted in blue, black, purple, red, yellow, and green on the scatter plots. Their corresponding waveforms (lead II) are displayed in panels B , C , E , F , H , and I , each with borders in the matching colour. Outliers were reduced with QTcNet despite high levels of noise and artefacts (panel G ).

Article Snippet: The dataset encompasses a high variability of clinical conditions and signal characteristics of ECG machines from various manufacturers (including Burdick/Spacelabs, Philips, and General Electric).

Techniques: Comparison

Journal: Europace

Article Title: QTcNet: a deep learning model for direct heart rate corrected QT interval estimation

doi: 10.1093/europace/euaf274

Figure Lengend Snippet: Integrated Gradients (IG) maps illustrating feature relevance for QTc prediction. Panel A shows dataset-wide aggregated importance scores computed using IG for each ECG lead and aligned to median beats. Black lines represent the average ECG signals across the test set, while the red overlay highlights regions the model identified as relevant for QTc prediction. Darker areas indicate higher attribution values. Panels B–D display three individual test set ECGs alongside their respective importance scores, illustrating how the model’s focus can vary across different signal morphologies.

Article Snippet: The dataset encompasses a high variability of clinical conditions and signal characteristics of ECG machines from various manufacturers (including Burdick/Spacelabs, Philips, and General Electric).

Techniques:

Journal: European Heart Journal. Digital Health

Article Title: Real-world application of deep learning for ECG-based prediction of coronary artery disease and revascularization needs

doi: 10.1093/ehjdh/ztaf096

Figure Lengend Snippet: Development, tuning, internal validation, and external validation sets. We utilized electrocardiography data from 227 288 patients at an academic medical center to establish development, tuning, and internal validation sets. In these sets, we excluded patients with troponin I levels >0.5 ng/mL and those without follow-up records. The first ECG recorded was used for tuning or internal validation. Additionally, we included ECG data from 61 777 patients at a community hospital as our external validation set for accuracy testing. CR, coronary revascularization; DLM, deep learning model; ECG, electrocardiography.

Article Snippet: The digital ECG signals were recorded using a Philips 12-lead ECG machine (PH080A, Philips Medical Systems, 3000 Minuteman Road, Andover, MA 01810, United States) in the standard 12-lead format, with a sampling rate of 500 Hz over a 10-s period.

Techniques: Biomarker Discovery

Journal: European Heart Journal. Digital Health

Article Title: Real-world application of deep learning for ECG-based prediction of coronary artery disease and revascularization needs

doi: 10.1093/ehjdh/ztaf096

Figure Lengend Snippet: AI-ECG performance stratified by sex, examination position, and comorbidities. Subgroup analysis was performed with a forest plot to evaluate the impact of sex, the position in which patients underwent ECG examination and common comorbidities. The dashed vertical lines indicate the reference (C-index: 0.8) and the overall diagnostic C-index (here, C-index: 0.825). Afib, atrial fibrillation; CKD, chronic kidney disease; DM, diabetes mellitus; ED, emergent department; HLP, hyperlipidemia; HTN, hypertension; IPD, inpatient department; OPD, outpatient department.

Article Snippet: The digital ECG signals were recorded using a Philips 12-lead ECG machine (PH080A, Philips Medical Systems, 3000 Minuteman Road, Andover, MA 01810, United States) in the standard 12-lead format, with a sampling rate of 500 Hz over a 10-s period.

Techniques: Diagnostic Assay

Journal: European Heart Journal. Digital Health

Article Title: Real-world application of deep learning for ECG-based prediction of coronary artery disease and revascularization needs

doi: 10.1093/ehjdh/ztaf096

Figure Lengend Snippet: Feature importance and predictive ability comparison of the DLM and XGBoost models. ( A ) The components of the AI-ECG identified the high-risk group. We trained three XGBoost models using patient characteristics, ECG features, and their combination to predict coronary revascularization. The bars represent the relative importance of the components in predicting coronary revascularization in the XGBoost models. ( B ) The prediction ability of all patient data for coronary revascularization within 1 year (C-index). The error bars are the 95% CIs of each C-index. The blue and red bars represent the results of prediction using individual patient characteristics and ECG features, respectively. The green bars represent predictions integrating features from XGBoost and logistic regression. The brown bars represent DLM data combined with XGBoost. All analyses were based on data from the entire population in this trial. DLM, deep learning model.

Article Snippet: The digital ECG signals were recorded using a Philips 12-lead ECG machine (PH080A, Philips Medical Systems, 3000 Minuteman Road, Andover, MA 01810, United States) in the standard 12-lead format, with a sampling rate of 500 Hz over a 10-s period.

Techniques: Comparison