electrocardiogram and respiration data recording system  (BIOPAC)

Journal: JACC Asia

Article Title: Deep Learning-Based Identification of Echocardiographic Abnormalities From Electrocardiograms

doi: 10.1016/j.jacasi.2024.10.012

Figure Lengend Snippet: Flow Chart Showing the Study Datasets The paired electrocardiogram (ECG) and echocardiography data collected from 8 centers were used in 6 data sets, namely, Mitsui Memorial Hospital (Mitsui), Asahi General Hospital (Asahi), Sakakibara Heart Institute (Sakakibara), Jichi Medical University Saitama Medical Center (Jichi), Tokyo Bay Urayasu Ichikawa Medical Center (TokyoBay), and JR Tokyo General Hospital (JR), for model development, and in 2 data sets, namely, The University of Tokyo Hospital (UTokyo) and NTT Medical Center Tokyo (NTT), for external validation. The data sets for model development were split further into a training set, a validation set, and a test set.

Article Snippet: ECG data from Fukuda Denshi (Tokyo, Japan) were used for UTokyo and Mitsui while ECG data from Nihon Kohden (Tokyo, Japan) were used for Asahi, Sakakibara, Jichi, TokyoBay, JR, and NTT.

Techniques:

Journal: JACC Asia

Article Title: Deep Learning-Based Identification of Echocardiographic Abnormalities From Electrocardiograms

doi: 10.1016/j.jacasi.2024.10.012

Figure Lengend Snippet: Overview of the Study Twelve echocardiographic finding labels for left-sided cardiac abnormalities, valvular heart diseases, and right-sided cardiac abnormalities were assigned from paired ECGs and echocardiograms. These labeled data sets were trained using convolutional neural network (CNN) to generate models for each specific echocardiographic finding. Subsequently, logistic regression was used on the output from these CNN models to predict the composite findings label. AR = aortic regurgitation; AS = aortic stenosis; DD = diastolic dysfunction; ECG = electrocardiogram; echo = echocardiographic; LAD = left atrial dilatation; LVD = left ventricular dilatation; LVEF = left ventricular ejection fraction; LVH = left ventricular hypertrophy; MR = mitral regurgitation; PH = pulmonary hypertension; RVD = right ventricular dysfunction; TR = tricuspid regurgitation; WMA = wall motion abnormality.

Article Snippet: ECG data from Fukuda Denshi (Tokyo, Japan) were used for UTokyo and Mitsui while ECG data from Nihon Kohden (Tokyo, Japan) were used for Asahi, Sakakibara, Jichi, TokyoBay, JR, and NTT.

Techniques: Labeling

Journal: JACC Asia

Article Title: Deep Learning-Based Identification of Echocardiographic Abnormalities From Electrocardiograms

doi: 10.1016/j.jacasi.2024.10.012

Figure Lengend Snippet: Patient Demographic and Clinical Characteristics

Article Snippet: ECG data from Fukuda Denshi (Tokyo, Japan) were used for UTokyo and Mitsui while ECG data from Nihon Kohden (Tokyo, Japan) were used for Asahi, Sakakibara, Jichi, TokyoBay, JR, and NTT.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Investigation of Possible Sources of Electrodermal Activity in Surgical Personnel to Assess Workplace Stress Levels

doi: 10.3390/s24227172

Figure Lengend Snippet: Changes in EDA and ECG data during mental and physical exertion compared to the resting measurements.

Article Snippet: ECG data were analyzed using the Signal Processing Toolbox in MATLAB, specifically through the detection of peak values from which the heart rate was determined.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Validity and Reliability of Movesense HR+ ECG Measurements for High-Intensity Running and Cycling

doi: 10.3390/s24175713

Figure Lengend Snippet: Configuration of 3-lead ECG, Movesense and Garmin devices. Participants were also equipped with a respiratory face mask; however, respiratory data were not included in the current study.

Article Snippet: ECG data collection involved two systems: a 3-lead ECG setup (ADInstruments, Dunedin, New Zealand) and the Movesense HR+ single-channel ECG with a chest belt (Movesense, Vantaa, Finland).

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Validity and Reliability of Movesense HR+ ECG Measurements for High-Intensity Running and Cycling

doi: 10.3390/s24175713

Figure Lengend Snippet: Equipment and materials.

Article Snippet: ECG data collection involved two systems: a 3-lead ECG setup (ADInstruments, Dunedin, New Zealand) and the Movesense HR+ single-channel ECG with a chest belt (Movesense, Vantaa, Finland).

Techniques: Sampling

Journal: Sensors (Basel, Switzerland)

Article Title: Validity and Reliability of Movesense HR+ ECG Measurements for High-Intensity Running and Cycling

doi: 10.3390/s24175713

Figure Lengend Snippet: ( a ) Correlation of standard deviation (SD) of the RR’ intervals for Movesense HR+ and 3-lead ECG; ( b ) Bland-Altman plot of the SD of the RR’ intervals for Movesense HR+ and 3-lead ECG; ( c ) Correlation of SD of the RR’ intervals for Garmin HRM-Pro and 3-lead ECG; ( d ) Bland-Altman plot of the SD of the RR’ intervals for Garmin HRM-Pro and 3-lead ECG.

Article Snippet: ECG data collection involved two systems: a 3-lead ECG setup (ADInstruments, Dunedin, New Zealand) and the Movesense HR+ single-channel ECG with a chest belt (Movesense, Vantaa, Finland).

Techniques: Standard Deviation

Journal: Sensors (Basel, Switzerland)

Article Title: Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data

doi: 10.3390/s24175544

Figure Lengend Snippet: Comparison of real and estimated ECG Lead II signals for six volunteers using the EQV sensor. The estimated signal is in red, and the real signal is in blue.

Article Snippet: In Step 1, the heart rate (HR) and electrocardiogram (ECG) data are loaded into MATLAB.

Techniques: Comparison

Journal: Sensors (Basel, Switzerland)

Article Title: Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data

doi: 10.3390/s24175544

Figure Lengend Snippet: Electrode placement for ECG Lead I and Lead II, forming Einthoven’s triangle.

Article Snippet: In Step 1, the heart rate (HR) and electrocardiogram (ECG) data are loaded into MATLAB.

Techniques:

Journal: Open Medicine

Article Title: miR-let-7a inhibits sympathetic nerve remodeling after myocardial infarction by downregulating the expression of nerve growth factor

doi: 10.1515/med-2024-0975

Figure Lengend Snippet: Effects of miR-let-7a on sympathetic nerve activity and programmed electrical stimulation at 7 days after MI. (a) Typical recordings from the left RSNA and integrated RSNA in the sham + NC, sham + Len-let-7a, MI + NC, and MI + Len-let-7a groups. (b) Baseline of RSNA. (c) Representative electrocardiogram of programmable electrical stimulation at 7 days after MI in the sham + NC, sham + Len-let-7a, MI + NC, and MI + Len-let-7a groups. (d) Comparison of arrhythmia scores between the four groups. * p < 0.05 and ** p < 0.01 vs sham groups. † p < 0.05 vs MI + NC group.

Article Snippet: Electrocardiogram (ECG) data were continually recorded by a PowerLab physiology system and analyzed using LabChart Pro software (AD Instruments, Australia).

Techniques: Activity Assay, Comparison