Stretchable and wearable wireless 3-leads ECG monitoring apparatus

Patent metadata

Field	Value
Publication number	US-12350054-B2
Application number	US-202217838251-A
Country	US
Kind code	B2
Filing date	Jun 12, 2022
Priority date	Jun 24, 2021
Publication date	Jul 8, 2025
Grant date	Jul 8, 2025

Abstract

The present invention provides a stretchable and flexible multi-leads ECG monitoring apparatus having a primary circuitry encased in a thin and flexible polymer patch configured to be worn on a human body. At least four flexible leads are connected to the primary circuitry at a first end and are configured to be connected to ECG electrode patches at a second end. The ECG electrode patches being configured to be attached to a plurality positions on a human body. The wireless transmitter is configured to transmit the ECG monitoring signals to a receiving device for recording or displaying the ECG monitoring signal.

First claim

What is claimed is:

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.

2. The multi-leads ECG monitoring apparatus of claim 1 ,
wherein the primary circuitry comprises a first electrode layer of soft printed circuit and a second electrode layer of soft printed circuit stacked on each other, forming a two-layer soft printed circuit;
wherein the first electrode layer includes electronic components and conductive traces, and the second electrode layer includes additional conductive traces, completing connections among the electronic components on the first electrode layer;
wherein layouts of the conductive traces are in interconnecting serpentine shape; and
wherein the electronic components comprise at least:
a microcontroller configured to process the measured ECG signal received from the leads, process control signals received from the electronic UI, and facilitate wireless data communication with the electronic UI and other external devices, and a wireless communication antenna.

3. The multi-leads ECG monitoring apparatus of claim 1 ,
wherein the leads consist of three measurement leads for measuring ECG signals, and one neutral lead for applying a dynamic feedback signal to the body of the subject user;
wherein each of the ECG electrode patches connected to the leads is placed on one of human torsos of the subject user during use; and
wherein the dynamic feedback signal is generated from a difference between an average of measured ECG signal voltage readings received from the three measurement leads and a reference voltage.

4. The multi-leads ECG monitoring apparatus of claim 1 ,
wherein the photo-triggered gelation procedure comprises:
adding a proportion equivalent to 1 g gelatin into a proportion equivalent to 9 ml deionized water;
stirring the gelatin-deionized water mixture at 60° C. for 2 hours to produce a gelatin solution,
adding a proportion equivalent to 2.1 g SBMA, a proportion equivalent to 0.9 g AAc, a proportion equivalent to 5 mg MBAA, a proportion equivalent to 50 mg 2959, a proportion equivalent to 0.33 g CaCl2, and a proportion equivalent to 3 ml glycerol into the gelatin solution to produce a mixed solution;
degassing the mixed solution for 30 minutes; and
placing the mixed solution at illumination of UV light for 30 minutes to produce an organohydrogel ionic polymer skeleton of the organohydrogel ECG electrode patches.

5. The multi-leads ECG monitoring apparatus of claim 1 ,
wherein the primary circuitry further comprises:
a plurality of instrumentation amplifiers with input of each of the instrumentation amplifiers connected to one or more of the leads and configured to receive measured ECG signals from the leads; and
an operational amplifier for each of the instrumentation amplifiers with input of the operational amplifier connected to an output of the instrumentation amplifier;

wherein the instrumentation amplifiers and the operational amplifier are configured to amplify the measured ECG signals before further processing by the primary circuitry.

6. The multi-leads ECG monitoring apparatus of claim 1 ,
wherein the main patch is primarily made of polydimethylsiloxane (PDMS).

7. The multi-leads ECG monitoring apparatus of claim 1 ,
wherein each of the leads comprises wire is encapsulated in a layer of polydimethylsiloxane (PDMS).

8. The multi-leads ECG monitoring apparatus of claim 1 ,
wherein the primary circuitry comprises a vibration actuator for tactile alarm configured to receive driving signals based on detections of abnormality in the measured ECG signals.

9. The multi-leads ECG monitoring apparatus of claim 1 ,
wherein the types of heart rhythm prediction probabilities comprise: NORMAL N Normal beat, PVC V Premature ventricular contraction, APC A Atrial premature beat, LBBB L Left bundle branch block beat, RBBB R Right bundle branch block beat, NPC J Nodal (junctional) premature beat, NESC j Nodal (junctional) escape beat, UNKNOWN Q Unclassifiable beat, SVPB S Supraventricular premature or ectopic beat (atrial or nodal), VESC E Ventricular escape beat, and PFUS f Fusion of paced and normal beat.

10. 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 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 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 machine learning (ML)-based heart rhythm classification configured to generate one or more types of heart rhythm prediction probabilities from a preprocessed measured ECG signal data generated from the measured ECG signal data; and
wherein the ML-based heart rhythm classification is implemented by a convolutional neural network (CNN) with a long short-term memory (LSTM) network.

11. The multi-leads ECG monitoring apparatus of claim 10 ,
wherein the primary circuitry comprises a first electrode layer of soft printed circuit and a second electrode layer of soft

Assignees

• Univ City Hong Kong

Inventors

• Yu Xinge
• Liu Yiming
• Zhou Jingkun

Classifications

• A61B5/7455

  characterised by tactile indication, e.g. vibration or electrical stimulation · CPC title

• A61B5/349

  Detecting specific parameters of the electrocardiograph cycle · CPC title

• A61B5/7203

  for noise prevention, reduction or removal · CPC title

• A61B5/339

  Displays specially adapted therefor · CPC title

• A61B5/746

  Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms · CPC title