BREATHING PATTERN IDENTIFICATION FOR RESPIRATORY FUNCTION ASSESSMENT
What is disclosed is a system and method for identifying a patient's breathing pattern for respiratory function assessment without contact and with a depth-capable imaging system. In one embodiment, a time-varying sequence of depth maps are received of a target region of a subject of interest over a period of inspiration and expiration. Once received, the depth maps are processed to obtain a breathing signal for the subject. The subject's breathing signal comprises a temporal sequence of instantaneous volumes. One or more segments of the subject's breathing signal are then compared against one or more reference breathing signals each associated with a known pattern of breathing. As a result of the comparison, a breathing pattern for the subject is identified. The identified breathing pattern is then used to assess the subject's respiratory function. The teachings hereof find their uses in an array of diverse medical applications. Various embodiments are disclosed.
1 . A method for identifying a breathing pattern of a subject for respiratory function assessment in a remote sensing environment, the method comprising:
receiving a time-varying sequence of depth maps of a target region of a subject of interest being monitored for respiratory function assessment, said depth maps being of said target region over a period of inspiration and expiration;
processing said depth maps to obtain a breathing signal for said subject comprising a temporal sequence of instantaneous volumes across time intervals during inspiratory and expiratory breathing;
comparing at least one segment of said subject's breathing signal against a reference breathing signal associated with a known pattern of breathing; and
identifying, as a result of said comparison, a breathing pattern for said subject.
2 . The method of claim 1 , wherein said target region comprises one of: said subject's anterior thoracic region, a region of said subject's dorsal body, and a side view containing said subject's thoracic region.
3 . The method of claim 1 , wherein said depth maps are obtained from images captured using an image-based depth sensing device comprising any of: a red green blue depth (RGBD) camera, an infrared depth camera, a passive stereo camera, an array of cameras, an active stereo camera, and a 2D monocular video camera.
4 . The method of claim 1 , wherein said depth maps are obtained from data captured using a non-image-based depth sensing device comprising any of: a LADAR device, a LiDAR device, a photo wave device, and a time-of-flight measurement device.
5 . The method of claim 1 , wherein said depth maps are obtained from processing video images captured of said target region with patterned clothing using a video camera device comprising any of: a red green blue (RGB) camera, an infrared camera, a multispectral camera, and a hyperspectral camera.
6 . The method of claim 1 , wherein, in advance of said comparison, filtering said subject's breathing signal to remove unwanted noise.
7 . The method of claim 1 , wherein said segment comprises at least one of: a dominant cycle of said subject's breathing signal, multiple dominant cycles of said subject's breathing signal, a fraction of one dominant cycle of said subject's breathing signal, multiple fractions of a plurality of dominant cycles, and a phase-shifted portion of said subject's breathing signal.
8 . The method of claim 1 , wherein said identified breathing pattern is one of: Eupnea, Bradypnea, Tachypnea, Hypopnea, Apnea, Kussmaul, Cheyne-Stokes, Biot's, Ataxic, Apneustic, Agonal, and Thoracoabdominal.
9 . The method of claim 1 , further comprising using said identified breathing pattern to determine whether said subject has any of: pulmonary fibrosis, pneumothorax, Infant Respiratory Distress Syndrome, asthma, bronchitis, and emphysema.
10 . The method of claim 1 , wherein said instantaneous volumes comprise one of: a calibrated volume and an uncalibrated volume.
11 . The method of claim 1 , wherein said inspiration is a maximal forced inspiration and said expiration is a maximal forced expiration.
12 . The method of claim 1 , wherein said inspiration and expiration comprises forced inspiration and forced expiration.
13 . The method of claim 1 , wherein said reference breathing signal consists of a volume signal generated using a depth capable sensor in one of: a simulated environment by a respiratory expert or a computerized mannequin, and a clinical environment with patients with identified respiratory diseases.
14 . A system for identifying a breathing pattern of a subject for respiratory function assessment in a remote sensing environment, the system comprising:
a memory and a storage device; and
a processor in communication with said memory and said storage device, said processor executing machine readable program instructions for performing:
receiving a time-varying sequence of depth maps of a target region of a subject of interest being monitored for respiratory function assessment, said depth maps being of said target region over a period of inspiration and expiration;
processing said depth maps to obtain a breathing signal for said subject comprising a temporal sequence of instantaneous volumes across time intervals during inspiratory and expiratory breathing;
comparing at least one segment of said subject's breathing signal against a reference breathing signal associated with a known pattern of breathing; and
identifying, as a result of said comparison, a breathing pattern for said subject.
15 . The system of claim 14 , wherein said target region comprises one of: said subject's anterior thoracic region, a region of said subject's dorsal body, and a side view containing said subject's thoracic region.
16 . The system of claim 14 , wherein said depth maps are obtained from images captured using an image-based depth sensing device comprising any of: a red green blue depth (RGBD) camera, an infrared depth camera, a passive stereo camera, an array of cameras, an active stereo camera, and a 2D monocular video camera.
17 . The system of claim 14 , wherein said depth maps are obtained from data captured using a non-image-based depth sensing device comprising any of: a LADAR device, a LiDAR device, a photo wave device, and a time-of-flight measurement device.
18 . The system of claim 14 , wherein said depth maps are obtained from processing video images captured of said target region with patterned clothing using a video camera device comprising any of: a red green blue (RGB) camera, an infrared camera, a multispectral camera, and a hyperspectral camera.
19 . The system of claim 14 , wherein, in advance of said comparison, filtering said subject's breathing signal to remove unwanted noise.
20 . The system of claim 14 , wherein said segment comprises at least one of:
a dominant cycle of said subject's breathing signal, multiple dominant cycles of said subject's breathing signal, a fraction of one dominant cycle of said subject's breathing signal, multiple fractions of a plurality of dominant cycles, and a phase-shifted portion of said subject's breathing signal.
21 . The system of claim 14 , wherein said identified breathing pattern is one of: Eupnea, Bradypnea, Tachypnea, Hypopnea, Apnea, Kussmaul, Cheyne-Stokes, Biot's, Ataxic, Apneustic, Agonal, and Thoracoabdominal.
22 . The system of claim 14 , further comprising using said identified breathing pattern to determine whether said subject has any of: pulmonary fibrosis, pneumothorax, Infant Respiratory Distress Syndrome, asthma, bronchitis, and emphysema.
23 . The system of claim 14 , wherein said instantaneous volumes comprise one of: a calibrated volume and an uncalibrated volume.
24 . The system of claim 14 , wherein said reference breathing signal consists of a volume signal generated using a depth capable sensor in one of: a simulated environment by a respiratory expert or a computerized mannequin, and a clinical environment with patients with identified respiratory diseases.
25 . The system of claim 14 , wherein identifying a breathing pattern for said subject is performed by an artificial intelligence program.