US 12,350,054 B2
Stretchable and wearable wireless 3-leads ECG monitoring apparatus
Xinge Yu, Hong Kong (HK); Yiming Liu, Hong Kong (HK); and Jingkun Zhou, Hong Kong (HK)
Assigned to City University of Hong Kong, Hong Kong (HK)
Filed by City University of Hong Kong, Hong Kong (HK)
Filed on Jun. 12, 2022, as Appl. No. 17/838,251.
Claims priority of provisional application 63/214,308, filed on Jun. 24, 2021.
Prior Publication US 2023/0021804 A1, Jan. 26, 2023
Int. Cl. A61B 5/271 (2021.01); A61B 5/00 (2006.01); A61B 5/259 (2021.01); A61B 5/308 (2021.01); A61B 5/339 (2021.01); A61B 5/349 (2021.01)
CPC A61B 5/271 (2021.01) [A61B 5/0006 (2013.01); A61B 5/259 (2021.01); A61B 5/308 (2021.01); A61B 5/339 (2021.01); A61B 5/349 (2021.01); A61B 5/7203 (2013.01); A61B 5/7455 (2013.01); A61B 5/746 (2013.01); A61B 2562/222 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A multi-leads electrocardiogram (ECG) monitoring apparatus, comprising:
a stretchable and flexible main patch containing a primary circuitry, wherein the main patch is worn on a body of a subject user by adhesion on skin during use; and
at least four flexible leads, each lead connecting to the primary circuitry at a first end and connecting to an ECG electrode patch at a second end, wherein each of the ECG electrode patches is worn on a skin area of the body of the subject user, which is not wearing another one of the ECG electrode patches, by adhesion on skin during use for measuring ECG signals;
wherein the primary circuitry is configured to receive measured ECG signals from the ECG electrode patches via the leads, process the measured ECG signals received, generate and transmit measured ECG signal data to a computing device implementing an electronic user interface (UI) for displaying of the measured ECG signal data;
wherein the ECG electrode patches are organohydrogel ECG electrode patches; and
wherein organohydrogel material of the organohydrogel ECG electrode patches is fabricated by a photo-triggered gelation procedure in a binary solvent of glycerol-water for enhanced adhesion strength and enhanced sustained conductivity;
wherein the computing device implementing the electronic UI is configured to execute an arrhythmia detection algorithm to predict heart rhythm abnormality from the measured ECG signal data;
wherein the arrhythmia detection algorithm comprises:
a signal pre-processing comprising signal denoising, data segmentation, data augmentation, data resampling, and normalization of the measured ECG signal data to generate preprocessed measured ECG signal data; and
a machine learning (ML)-based heart rhythm classification configured to generate one or more types of heart rhythm prediction probabilities from the preprocessed measured ECG signal data; wherein the ML-based heart rhythm classification is implemented by a convolutional neural network (CNN) with a long short-term memory (LSTM) network.