JP-7857043-B2 - Deep learning-based atrial fibrillation detection system using PPG signal sensing ring

Inventors

• チャン， ヒョン ミン
• キム， ヘ ナ
• チョ， ソン ミ
• イ， ミン ヒョン
• キム， チャン ヒョン
• チョイ， チャン ウ
• イ， ビョン ファン

Assignees

• スカイ ラブズ インコーポレイテッド

Dates

Publication Date

20260512

Application Date

20220914

Priority Date

20210916

Claims (10)

1. This is a deep learning-based atrial fibrillation detection system that utilizes a photoplethysmography (PPG) signal sensing ring, and the system includes a server, the server is A signal quality classification component configured to classify the quality of PPG signals into good or bad, It includes an atrial fibrillation detection component configured to determine whether atrial fibrillation has occurred from the PPG signal using a deep learning model, The PPG signal sensing ring includes a plurality of sensors configured to simultaneously measure a plurality of biosignals at different locations. Each of the plurality of sensors includes a light source and a photoelectric converter, and the terminal wirelessly connected to the PPG signal sensing ring includes a sensor selection component configured to select, from among the plurality of sensors, the sensor that measured the test PPG signal with the highest signal quality from among a plurality of test PPG signals obtained from each of the plurality of sensors, in order to select the sensor from which the PPG signal should be acquired, as the sensor for measuring the PPG signal. A deep learning-based atrial fibrillation detection system utilizing a PPG signal sensing ring, characterized in that the PPG signal is measured using only the selected sensor of the PPG signal sensing ring that comes into contact with the user's finger, and the terminal receives the PPG signal measured by only the selected sensor from the PPG signal sensing ring and transmits it to the server.
2. The deep learning-based atrial fibrillation detection system using a PPG signal sensing ring according to claim 1, characterized in that the signal quality of the plurality of test PPG signals is evaluated by at least one of the following: the magnitude of the acceleration signal, the signal-to-noise ratio, and the AC component magnitude to DC component magnitude ratio.
3. The server further includes an atrial fibrillation index calculation component configured to calculate an atrial fibrillation index, The atrial fibrillation index is defined as the ratio of the time at which the signal quality classification component classifies the PPG signal as having good quality and the atrial fibrillation determination component determines that atrial fibrillation has occurred to the time at which the signal quality classification component classifies the PPG signal as having good quality, thus providing a deep learning-based atrial fibrillation determination system using a PPG signal sensing ring according to claim 1.
4. The terminal further includes a light source control component configured to control the light source of each of the multiple sensors so that the DC component of each of the multiple test PPG signals measured using the multiple sensors falls within a predetermined range, characterized in that it is a deep learning-based atrial fibrillation detection system utilizing a PPG signal sensing ring according to claim 1.
5. The light source control by the light source control component and the sensor selection by the sensor selection component are performed sequentially. The deep learning-based atrial fibrillation detection system using a PPG signal sensing ring according to claim 4, characterized in that the control of the light source by the light source control component and the selection of the sensor by the sensor selection component are performed periodically.
6. The process involves measuring the user's PPG signal using a photoplethysmography (PPG) signal-sensing ring that comes into contact with the user's finger, and receiving the PPG signal via a terminal wirelessly connected to the PPG signal-sensing ring. The server classifies the quality of the PPG signal into good or bad, The server includes the step of using a deep learning model to determine whether or not atrial fibrillation has occurred from the PPG signal, The PPG signal sensing ring includes a plurality of sensors configured to simultaneously measure a plurality of biosignals at different locations. Each of the plurality of sensors includes a light source and a photoelectric converter, and the terminal includes a sensor selection component configured to select, from among the plurality of sensors, the sensor that measured the test PPG signal with the highest signal quality from among a plurality of test PPG signals obtained from each of the plurality of sensors, in order to select the sensor from which the PPG signal should be acquired, as the sensor for measuring the PPG signal. A deep learning-based atrial fibrillation detection method using a PPG signal sensing ring, characterized in that the PPG signal is measured using only selected sensors of the PPG signal sensing ring that come into contact with the user's finger, and the terminal device receives the PPG signal measured by only the selected sensors from the PPG signal sensing ring and transmits it to the server.
7. The deep learning-based atrial fibrillation detection method using a PPG signal sensing ring according to claim 6, characterized in that the signal quality of the plurality of test PPG signals is evaluated by at least one of the following: the magnitude of the acceleration signal, the signal-to-noise ratio, and the AC component magnitude to DC component magnitude ratio.
8. The process further includes the step in which the server calculates an atrial fibrillation index, The atrial fibrillation index is defined as the ratio of the time during which the PPG signal quality is classified as good by the signal quality classification component and atrial fibrillation is determined to have occurred by the atrial fibrillation determination component to the time during which the PPG signal quality is classified as good by the signal quality classification component, and a deep learning-based atrial fibrillation determination method using a PPG signal sensing ring according to claim 6.
9. The terminal further includes a light source control component configured to control the light source of each of the multiple sensors so that the DC component of each of the multiple test PPG signals measured using the multiple sensors falls within a predetermined range, characterized in that it is a deep learning-based atrial fibrillation detection method using a PPG signal sensing ring according to claim 6.
10. The light source control by the light source control component and the sensor selection by the sensor selection component are performed sequentially. The deep learning-based atrial fibrillation detection method using a PPG signal sensing ring according to claim 9, characterized in that the light source control by the light source control component and the sensor selection by the sensor selection component are performed periodically.

Description

This invention relates to an atrial fibrillation detection system, and more specifically, to a deep learning-based atrial fibrillation detection system utilizing a photoplethysmography (PPG) signal sensing ring. Atrial fibrillation is an arrhythmia disorder characterized by irregular heartbeats occurring in the atria. Because atrial fibrillation occurs intermittently, early diagnosis has been difficult due to the need for hospital visits for diagnosis via conventionally widely used electrocardiograms (ECGs). However, with reliable and continuous monitoring, patients can be detected and managed early, reducing their risk. Therefore, a system and method for real-time monitoring of atrial fibrillation in daily life is needed. This is a three-dimensional view of a PPG signal sensing ring according to one embodiment of the present disclosure.This is an exploded three-dimensional view of a PPG signal sensing ring according to one embodiment of the present disclosure.This is a block diagram of a deep learning-based atrial fibrillation detection system using a PPG signal sensing ring according to one embodiment of the present disclosure.This graph shows the PPG signal before pretreatment.This graph shows the PPG signal before pretreatment.This graph shows the PPG signal from Figure 4A after preprocessing.This graph shows the PPG signal from Figure 4B after preprocessing.This graph shows PPG signals classified as good quality.This graph shows PPG signals classified as good quality.This graph shows the PPG signal classified as substandard quality.This graph shows the PPG signal classified as substandard quality.This graph shows PPG signals that were determined not to be atrial fibrillation.This graph shows PPG signals that were determined not to be atrial fibrillation.This graph shows the PPG signal that was diagnosed as atrial fibrillation.This graph shows the PPG signal that was diagnosed as atrial fibrillation.This flowchart illustrates a deep learning-based atrial fibrillation detection method using a PPG signal sensing ring.This is a flowchart showing how to configure the sensor for the PPG signal sensing ring. Figure 1 is a three-dimensional view of a PPG (photoplethysmography) signal-sensing ring 100 according to one embodiment of the present disclosure. Figure 2 is an exploded three-dimensional view of the PPG signal-sensing ring 100 according to one embodiment of the present disclosure. Referring to Figures 1 and 2, the PPG signal sensing ring 100 also includes an external electrode 130, an internal electrode 140, an insulating unit 150, a top cover 110, an operation indicator unit 120, and multiple sensors 160. The external electrode 130 may have an arc shape. The external electrode 130 is made of a conductor and can function as an electrode for measuring an electrocardiogram (ECG). Furthermore, the external electrode 130 forms the appearance of the PPG signal sensing ring 100, and the external electrode 130 can come into contact with the user's body. The internal electrode 140 has a ring shape and may have multiple openings 145 for multiple sensors 160. The internal electrode 140 is made of a conductor and can function as an electrode for measuring an electrocardiogram. Furthermore, the internal electrode 140 forms the interior of the PPG signal sensing ring 100, and the internal electrode 140 may come into contact with the user's finger. The insulating unit 150 may be positioned between the external electrode 130 and the internal electrode 140. The insulating unit 150 can enable electrical insulation between the external electrode 130 and the internal electrode 140. The top cover 110 has an arc shape and can form a ring shape together with the external electrode 130. The top cover 110 can form the appearance of the PPG signal sensing ring 100. The operation indicator unit 120 may be coupled to the top cover 110. The operation indicator unit 120 may also include multiple LEDs (light-emitting diodes), such as green and red LEDs. The operation indicator unit 120 can use multiple LEDs to display the operation of the PPG signal sensing ring 100. For example, the green LED may turn on for 2 seconds to indicate the start of measurement, and the green LED may turn on for 1 second to indicate the end of measurement. Also, the red LED may flash repeatedly at a 1-second interval to indicate a malfunction. Multiple sensors 160 may be positioned to contact the user's finger. Each of the sensors 160 may be located within a plurality of openings 145 of the internal electrode 140 and may protrude from the surface of the internal electrode 140. The multiple sensors 160 may be configured to acquire different PPG signals at different positions. Each sensor 160 may also include a light source and a photoelectric converter. In some embodiments, although not shown in Figures 1 and 2, the PPG signal sensing ring 100 further includes an acceleration sensor positioned between the external electrode 130 and the internal electrode