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Heartscape Technologies Inc 80-lead ecg body surface mapping
80 Lead Ecg Body Surface Mapping, supplied by Heartscape Technologies Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/lead+surface+ecg/80+lead+body+surface+map++bsm++ecg/pm19766352-35-22-30
Average 90 stars, based on 1 article reviews
80-lead ecg body surface mapping - by Bioz Stars, 2026-11
90/100 stars

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Article Title: Body surface mapping vs 12-lead electrocardiography to detect ST-elevation myocardial infarction.
Article Snippet: A prospective, multicenter trial was conducted in patients with nontraumatic chest pain in 4 hospitals to determine whether an 80-lead body surface map electrocardiogram system (80-lead BSM ECG) improves detection of ST-segment elevation in acute myocardial infarction (STEMI) compared with a standard 12-lead electrocardiogram (ECG) in an emergency department (ED) setting.. A trained ED or cardiology staff member (technician or nurse) recorded a 12-lead ECG and 80-lead BSM ECG from each subject at initial presentation.. Serial biomarkers (total creatine kinase [CK], CK-MB, and/or troponin) were obtained according to individual hospital practice.

Article Title: Optimization of the precordial leads of the 12-lead electrocardiogram may improve detection of ST-segment elevation myocardial infarction.
Article Snippet: Background: For the assessment of patients with chest pain, the 12-lead electrocardiogram (ECG) is ☆ Conflicts of Int ⁎ Corresponding Royal Victoria Hosp Kingdom.. E-mail address: p 1 Dr Peter Scott re Royal Research Fellow 0022-0736/$ – see fro doi:10.1016/j.jelectroc the initial investigation.. Major management decisions are based on the ECG findings, both for attempted coronary artery revascularization and risk stratification.

Article Title: Acute detection of ST-elevation myocardial infarction missed on standard 12-Lead ECG with a novel 80-lead real-time digital body surface map: primary results from the multicenter OCCULT MI trial.
Article Snippet: James W. Hoekstra, MD Brian J. O’Neill, MD Yuri B.. Pride, MD Cedric Lefebvre, MD Deborah B. Diercks, MD W. Frank Peacock, MD Gregory J. Fermann, MD C. Michael Gibson, MD Duane Pinto, MD Jim Giglio, MD Abhinav Chandra, MD Charles B. Cairns, MD Marvin A. Konstam, MD Joe Massaro, PhD Mitchell Krucoff, MD From Wake Forest University, Winston-Salem, NC (Hoekstra, Lefebvre); Wayne State University, Detroit, MI (O’Neill); Beth Israel Deaconess Medical Center, Boston, MA (Pride, Gibson, Gibson, Pinto); University of California Davis, Sacramento, CA (Diercks); Cleveland Clinic Foundation, Cleveland, OH (Peacock); University of Cincinnati, Cincinnati, OH (Fermann); New York Presbyterian Hospital, New York, NY (Giglio); Duke University Medical Center, Durham, NC (Chandra, Krucoff); University of North Carolina, Chapel Hill, NC (Cairns); Tufts University Medical Center, Boston, MA (Konstam); and Boston University, Boston, MA (Massaro).



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Respiratory rate (RR) estimation in spontaneously breathing humans. ( A ) RR estimates, in breaths per minute (bpm), during 3 levels of exercise in one subject. Algorithm-estimated RRs (estimated, blue) are compared with RR measured from the subject using the respiratory inductive plethysmography based Hexoskin monitor (expected, red) while performing three consecutive tasks: (1) resting, standing upright on a treadmill (Int 1); (2) walking on the treadmill at a moderate speed (1.2 m/s) (int 2); and (3) walking on the treadmill with 15% track inclination at the moderate speed (Int 3). ( B ) Summary results of algorithm-estimated and reference RRs (blue and red, respectively) during each subject-task interval. The data are from seven subjects, each performing either or all the three levels of exercise described above (subject-tasks), and presented in order of increasing average expected RR values. ( C ) The absolute errors (black) and relative errors (gray) of the algorithmic RR estimations across the subject-task intervals described above. Equivalence testing revealed that the expected and estimated RRs were the same ( p < 0.0001) for all subject-task intervals. ( D ) A comparison of the ECG cycle-to-cycle estimated and expected RR for all subjects and tasks with the indicated R 2 value (0.9092) and low root mean square error (RMSE, 2.2bpm) support a close linear relationship between the values. ( E ) Absolute error (bpm) and ( F ) relative error (%) distributions across all subjects.

Journal: Scientific Reports

Article Title: Open-source software for respiratory rate estimation using single-lead electrocardiograms

doi: 10.1038/s41598-023-50470-0

Figure Lengend Snippet: Respiratory rate (RR) estimation in spontaneously breathing humans. ( A ) RR estimates, in breaths per minute (bpm), during 3 levels of exercise in one subject. Algorithm-estimated RRs (estimated, blue) are compared with RR measured from the subject using the respiratory inductive plethysmography based Hexoskin monitor (expected, red) while performing three consecutive tasks: (1) resting, standing upright on a treadmill (Int 1); (2) walking on the treadmill at a moderate speed (1.2 m/s) (int 2); and (3) walking on the treadmill with 15% track inclination at the moderate speed (Int 3). ( B ) Summary results of algorithm-estimated and reference RRs (blue and red, respectively) during each subject-task interval. The data are from seven subjects, each performing either or all the three levels of exercise described above (subject-tasks), and presented in order of increasing average expected RR values. ( C ) The absolute errors (black) and relative errors (gray) of the algorithmic RR estimations across the subject-task intervals described above. Equivalence testing revealed that the expected and estimated RRs were the same ( p < 0.0001) for all subject-task intervals. ( D ) A comparison of the ECG cycle-to-cycle estimated and expected RR for all subjects and tasks with the indicated R 2 value (0.9092) and low root mean square error (RMSE, 2.2bpm) support a close linear relationship between the values. ( E ) Absolute error (bpm) and ( F ) relative error (%) distributions across all subjects.

Article Snippet: Single-lead body surface ECG signals were obtained during the procedure and 90 min after coil placement using the AD Instruments PowerLab 4/35 system with Labchart 8 software.

Techniques: Comparison

Block diagram of respiration rate estimation algorithm. Raw single-lead ECG data are filtered (panels a and b ), R-peaks are detected (red symbols, panel c ), R-peak intervals are determined, QRS complexes are extracted (panel d ) and their root mean square amplitude (RMS) values are calculated (panel e ), a power spectrum is generated for a moving window of 16 QRS RMS values incremented one value at a time (panel f ), and its peak frequency and the R-peak interval data within the window are used to calculate the respiratory rate (RR) using the equation shown.

Journal: Scientific Reports

Article Title: Open-source software for respiratory rate estimation using single-lead electrocardiograms

doi: 10.1038/s41598-023-50470-0

Figure Lengend Snippet: Block diagram of respiration rate estimation algorithm. Raw single-lead ECG data are filtered (panels a and b ), R-peaks are detected (red symbols, panel c ), R-peak intervals are determined, QRS complexes are extracted (panel d ) and their root mean square amplitude (RMS) values are calculated (panel e ), a power spectrum is generated for a moving window of 16 QRS RMS values incremented one value at a time (panel f ), and its peak frequency and the R-peak interval data within the window are used to calculate the respiratory rate (RR) using the equation shown.

Article Snippet: Single-lead body surface ECG signals were obtained during the procedure and 90 min after coil placement using the AD Instruments PowerLab 4/35 system with Labchart 8 software.

Techniques: Blocking Assay, Generated