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 & Diagnostics

Article Title: Point-of-care and self-testing for potassium: recent advances

doi: 10.1039/d2sd00062h

Figure Lengend Snippet: Summary table of the reported potassium sensor devices, target fluid for which they were developed, the fluid that was used for sensor testing, the device materials, methods, performance and the stage of development

Article Snippet: Bukhari et al. (2022) , Non-invasive , Blood and ECG , 48 h 12-lead ECGs, H12+, Mortara Instruments (ECG) , Not reported , Study population of 29 end stage renal disease patients.

Techniques: Concentration Assay, Colorimetric Assay, Modification, Membrane, Impedance Spectroscopy, Ex Vivo, In Vitro, Extraction

Journal: Heart rhythm : the official journal of the Heart Rhythm Society

Article Title: Using Skin Sympathetic Nerve Activity to Estimate Stellate Ganglion Nerve Activity in Dogs

doi: 10.1016/j.hrthm.2015.02.012

Figure Lengend Snippet: Apamin increases nerve activities and heart rate (Protocol 1). A shows the actual recording made in Dog D after apamin injection, showing elevated heart rate (HR), along with the elevation of right stellate ganglion nerve activity (RSGNA), skin nerve activity from Lead-I (SKNA-I), skin nerve activity from Lead II (SKNA-II), skin nerve activity from right chest bipolar lead (SKNA-R) and left chest bipolar lead (SKNA-L). The ECG was from the same recording obtained from SKNA-I, but low pass filtered at 100 Hz. B shows correlation between integrated nerve activities recorded from different locations, and between integrated nerve activities and heart rate. Each filled circle represents the average of nerve activity and heart rate over a onemin window. A positive correlation was found in each comparison, but 3 of them (iRSGNA vs iSKNA-L, HR vs iSKNA-R and HR vs iSKNA-L) were statistically insignificant. The strongest correlation was found between iRSGNA and iSKNA- I, followed by the correlation between HR with iRSGNA and between HR and iSKNA-I.

Article Snippet: CI confidence interval DSI Data Sciences International ECG electrocardiogram EMG electromyogram FFT fast Fourier transform HR heart rate SCNA subcutaneous nerve activity SG stellate ganglia SGNA stellate ganglion nerve activity SK small conductance calcium activated K SKNA skin nerve activity

Techniques: Injection, Activity Assay, Comparison

Journal: Heart rhythm : the official journal of the Heart Rhythm Society

Article Title: Using Skin Sympathetic Nerve Activity to Estimate Stellate Ganglion Nerve Activity in Dogs

doi: 10.1016/j.hrthm.2015.02.012

Figure Lengend Snippet: SKNA in ambulatory dogs (Protocol 2). The recording came from Dog I. The LSGNA was recorded by DSI D70EEE radiotransmitter, while the remaining nerve activities and ECG were recorded by the Iso-Damm-8 amplifier. All data were then digitized simultaneously by Digidata 1400a. A shows increased LSGNA activity was associated with increased SKNA and heart rate (arrows). B shows positive correlations among the nerve activities and the heart rate. The p values of all correlations were < 0.05. The best correlation was that between iLSGNA and iSKNA-L.

Article Snippet: CI confidence interval DSI Data Sciences International ECG electrocardiogram EMG electromyogram FFT fast Fourier transform HR heart rate SCNA subcutaneous nerve activity SG stellate ganglia SGNA stellate ganglion nerve activity SK small conductance calcium activated K SKNA skin nerve activity

Techniques: Activity Assay

Journal: Heart rhythm : the official journal of the Heart Rhythm Society

Article Title: Using Skin Sympathetic Nerve Activity to Estimate Stellate Ganglion Nerve Activity in Dogs

doi: 10.1016/j.hrthm.2015.02.012

Figure Lengend Snippet: SKNA with wavelet filter in ambulatory dog I (same raw data as that used in Figure 3). Panel A shows the nerve activities after wavelet filtering. Note the elimination of the surface ECG artifacts by wavelet filtering. Panel B shows the correlations among nerve activities and heart rate (HR). Significant and positive correlations are present.

Article Snippet: CI confidence interval DSI Data Sciences International ECG electrocardiogram EMG electromyogram FFT fast Fourier transform HR heart rate SCNA subcutaneous nerve activity SG stellate ganglia SGNA stellate ganglion nerve activity SK small conductance calcium activated K SKNA skin nerve activity

Techniques: