Non-contact automated noise classification method and system

Abstract

This invention provides a non-contact automated agitation classification method and system, wherein the method includes the following steps: Step A: collecting historical data of multiple patients, including historical images; Step B: training a machine learning model using the historical images to generate an agitation classification model; Step C: obtaining an image of a patient to be analyzed, extracting at least one agitation feature from the image, and analyzing the agitation feature using the agitation classification model to determine whether the patient is in an agitated state; wherein the agitation feature is a facial movement feature, a trunk movement feature, a lower limb movement feature, and / or a restraint behavior feature. Accordingly, this invention can achieve continuous and accurate monitoring of patient agitation, assisting medical staff in adjusting sedation strategies in a timely manner, improving patient safety, and reducing the occurrence of adverse events.

Claims

1. A non-contact automated agitation classification method, comprising the following steps: Step A: collecting historical data of multiple patients, including historical images; Step B: training a machine learning model using the historical images to generate an agitation classification model; further comprising: a data screening procedure to remove data from the historical data that does not meet a screening criterion, and using the historical images of the historical data that meet the screening criterion as training data; a preprocessing procedure, including an image segmentation procedure, an annotation procedure, a background separation procedure, and a data quantization procedure, wherein: The image segmentation program segments each historical image into multiple parent segments, and then segments each parent segment into multiple sub-segments, with the duration of each sub-segment being shorter than the duration of each parent segment; the annotation program identifies and annotates at least one agitation marker in each parent segment and each sub-segment, where the agitation marker is the head, torso, lower limbs, or restraint behavior; the background separation program removes the background from each parent segment and each sub-segment; the data quantization program analyzes the agitation markers in each parent segment and each sub-segment, and records and quantifies the degree of movement of the head, torso, or lower limbs when the agitation marker is the head, torso, or lower limbs; a data classification program analyzes the parent segments or sub-segments and, based on the analysis results, labels each parent segment or sub-segment as requiring attention or not requiring attention, wherein the data classification program includes a random forest classification program or a long short-term memory network classification program; When the data classification program includes the random forest classification program, it acquires the movement levels of the head, torso, and lower limbs in the sub-segments as training samples, and uses the Random Forest (RF) algorithm to construct a disturbance classification model that is a random forest classification model capable of making judgments and analyses, labeling each sub-segment as requiring attention or not requiring attention. When the data classification program includes the Long Short-Term Memory (LSTM) network classification program, it acquires the movement levels of the head, torso, and lower limbs in the parent segments as training samples, and uses Long Short-Term Memory (LSTM) to construct a disturbance classification model that is a LSTM network classification model capable of making judgments and analyses, labeling each parent segment as requiring attention or not requiring attention. Step C: An image of a patient to be analyzed is obtained, and at least one agitation feature is extracted from the image. The agitation feature is then analyzed using the agitation classification model to determine whether the patient is in an agitated state. The agitation feature is a facial movement feature, a trunk movement feature, and / or a lower limb movement feature.

2. The non-contact automated noise classification method as described in claim 1, wherein, The screening criterion is to check whether the historical RASS score included in the patient's historical data is within a preset range, and use it as the judgment standard. If the historical RASS score does not fall within the preset range, the historical data is deleted.

3. The non-contact automated noise classification method as described in claim 2, wherein, The preset range is a set of numbers, including -2, -1, 0, 1, 2, 3, 4.

4. The non-contact automated noise classification method as described in claim 1, wherein, Each parent segment has a duration of 30 seconds, and each child segment has a duration of 2 seconds.

5. The non-contact automated noise classification method as described in claim 1, wherein, The preprocessing procedure also includes a noise removal procedure to exclude all parent segments containing noise.

6. The non-contact automated noise classification method as described in claim 5, wherein, The noise removal program determines whether the duration of noise in each parent segment is below a preset time. If the duration of noise exceeds the preset time, the parent segment is deleted.

7. The non-contact automated noise classification method as described in claim 6, wherein, The preset time is 10 seconds, and the noise refers to camera shake or interference from medical staff.

8. The non-contact automated noise classification method as described in claim 1, wherein, The data classification program includes a threshold classification program, which classifies the degree of movement into three levels: no movement (1 point), bed movement (2 points), and significant out-of-bed movement (3 points). The program scores the degree of movement of the head, trunk, and lower limbs in these sub-segments and sums them up to a total score, which ranges from 3 to 9 points. When the total score exceeds a preset threshold, the sub-segment is marked as requiring attention; when the total score does not exceed the preset threshold, the sub-segment is marked as not requiring attention.

9. The non-contact automated noise classification method as described in claim 8, wherein, The preset threshold is 5.

10. A non-contact automated noise classification system, comprising a computing device for performing the non-contact automated noise classification method as described in any one of claims 1 to 9.

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