NZ731861B2 - A screening test for detection of deep vein thrombosis - Google Patents
A screening test for detection of deep vein thrombosis Download PDFInfo
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- NZ731861B2 NZ731861B2 NZ731861A NZ73186115A NZ731861B2 NZ 731861 B2 NZ731861 B2 NZ 731861B2 NZ 731861 A NZ731861 A NZ 731861A NZ 73186115 A NZ73186115 A NZ 73186115A NZ 731861 B2 NZ731861 B2 NZ 731861B2
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- muscle
- dvt
- motion sensor
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- 206010051055 Deep vein thrombosis Diseases 0.000 title claims abstract description 56
- 238000001514 detection method Methods 0.000 title description 5
- 210000003205 Muscles Anatomy 0.000 claims abstract description 78
- 230000004044 response Effects 0.000 claims abstract description 54
- 230000003534 oscillatory Effects 0.000 claims abstract description 41
- 238000004458 analytical method Methods 0.000 claims abstract description 26
- 230000001702 transmitter Effects 0.000 claims abstract description 13
- 210000004872 soft tissue Anatomy 0.000 claims abstract description 9
- 230000000875 corresponding Effects 0.000 claims description 17
- 238000007619 statistical method Methods 0.000 claims description 9
- 210000003414 Extremities Anatomy 0.000 claims description 5
- 238000000034 method Methods 0.000 description 12
- 230000001133 acceleration Effects 0.000 description 7
- 210000002414 Leg Anatomy 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 230000000996 additive Effects 0.000 description 5
- 239000000654 additive Substances 0.000 description 5
- 238000005070 sampling Methods 0.000 description 5
- 210000001699 lower leg Anatomy 0.000 description 3
- 230000001575 pathological Effects 0.000 description 3
- 206010014522 Embolism venous Diseases 0.000 description 2
- 208000010378 Pulmonary Embolism Diseases 0.000 description 2
- 238000000692 Student's t-test Methods 0.000 description 2
- 208000004043 Venous Thromboembolism Diseases 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000002591 computed tomography Methods 0.000 description 2
- 230000034994 death Effects 0.000 description 2
- 231100000517 death Toxicity 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 210000000689 upper leg Anatomy 0.000 description 2
- 210000000988 Bone and Bones Anatomy 0.000 description 1
- 210000002082 Fibula Anatomy 0.000 description 1
- 210000003141 Lower Extremity Anatomy 0.000 description 1
- 210000004072 Lung Anatomy 0.000 description 1
- 210000002027 Muscle, Skeletal Anatomy 0.000 description 1
- 210000002303 Tibia Anatomy 0.000 description 1
- 230000003466 anti-cipated Effects 0.000 description 1
- 238000009534 blood test Methods 0.000 description 1
- 210000000038 chest Anatomy 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 238000009552 doppler ultrasonography Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000009114 investigational therapy Methods 0.000 description 1
- 235000015110 jellies Nutrition 0.000 description 1
- 239000008274 jelly Substances 0.000 description 1
- 238000007477 logistic regression Methods 0.000 description 1
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- 230000002285 radioactive Effects 0.000 description 1
- 238000010079 rubber tapping Methods 0.000 description 1
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Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
- A61B2562/02—Details of sensors specially adapted for in-vivo measurements
- A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
- A61B5/0024—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system for multiple sensor units attached to the patient, e.g. using a body or personal area network
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0048—Detecting, measuring or recording by applying mechanical forces or stimuli
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1107—Measuring contraction of parts of the body, e.g. organ, muscle
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4519—Muscles
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- A—HUMAN NECESSITIES
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- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6813—Specially adapted to be attached to a specific body part
- A61B5/6828—Leg
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- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
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- A—HUMAN NECESSITIES
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- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Abstract
system (100) for assessing a subject at risk of a soft tissue abnormality such as deep vein thrombosis (DVT), comprises a motion sensor (108) which is adapted to be fixed, in use, to a muscle (106) of the subject. The motion sensor includes a transmitter (206) configured to transmit a signal (400) representing motion of the sensor. A receiver (118) is configured to receive the signal from the transmitter of the motion sensor. A signal processor (112) is coupled to the receiver and configured to analyse first and second data sets received via the receiver from the motion sensor. The first and second data sets represent respective first and second oscillatory mechanical responses of first and second muscles of the subject resulting from mechanical stimuli. The analysis comprises determining first and second parameter sets characterising the first and second oscillatory mechanical responses, and comparing the first parameter set with the second parameter set to assess a possible presence of DVT in the subject. representing motion of the sensor. A receiver (118) is configured to receive the signal from the transmitter of the motion sensor. A signal processor (112) is coupled to the receiver and configured to analyse first and second data sets received via the receiver from the motion sensor. The first and second data sets represent respective first and second oscillatory mechanical responses of first and second muscles of the subject resulting from mechanical stimuli. The analysis comprises determining first and second parameter sets characterising the first and second oscillatory mechanical responses, and comparing the first parameter set with the second parameter set to assess a possible presence of DVT in the subject.
Description
A SCREENING TEST FOR DETECTION OF DEEP VEIN THROMBOSIS
FIELD OF THE INVENTION
The present invention relates to systems, methods and apparatus for
assisting healthcare workers in the screening and detection of abnormalities and
pathological states in soft tissue, and has particular application at least, but not
confined to, the screening and detection of deep vein thrombosis (DVT) of the
lower limbs.
BACKGROUND OF THE INVENTION
Deep vein thrombosis (DVT) is a significant complication in all surgical
and medical wards, as well as in other aspects of community life. It is a condition
that has significant implications for pulmonary embolism, and possible death.
There are also dangers of continuing morbidity in the legs, and also in the lungs,
from the presence of venous thromboembolism.
Currently, there is no recognised clinical assessment for DVT that has
an accuracy greater than 60 percent, and many patients with DVT have no overt
clinical findings.
In cases of suspected DVT, it is necessary to undertake specific
investigations for confirmation. These investigations are expensive, and include
techniques such as Doppler ultrasonography which requires significant equipment
and expertise. Tests for pulmonary embolism may also be necessary, involving
methods such as computed tomography (CT) or chest scanning using a
radioactive marker. Blood tests may also be performed. As will be appreciated, a
number of these tests are invasive and/or uncomfortable for the patient. Many of
these tests cannot be performed at the bedside.
18004361_1 (GHMatters) P38862NZPC
A particular concern is that many patients develop DVT with minimal or
no significant changes or symptoms (e.g. pain and swelling), and as a result
many patients are sent home early following surgery without any assessment of
the possibility of DVT. Deaths from complications of venous thromboembolism
have been known to result in such cases.
There is, accordingly, a pressing need for a reproducible, objective
investigation that can be performed by a trained technician, a trained clinician, or
other healthcare worker. Ideally, assessment of the possible presence of DVT
should be able to be conducted simply, with high reliability, at relatively low cost,
and without the requirement for invasive or uncomfortable procedures. The
present invention seeks to address these requirements.
SUMMARY OF THE INVENTION
Disclosed herein is a system for assessing a subject at risk of a soft
tissue abnormality such as deep vein thrombosis (DVT), comprising:
a motion sensor, adapted to be fixed, in use, to a muscle of the
subject, the motion sensor including a transmitter configured to transmit a signal
representing motion of the sensor;
a receiver, configured to receive the signal from the transmitter of the
motion sensor; and
a signal processor, coupled to the receiver, and configured to analyse
first and second data sets received via the receiver from the motion sensor, the
first and second data sets representing respective first and second oscillatory
mechanical responses of first and second muscles of the subject resulting from
mechanical stimuli, wherein the analysis comprises determining first and second
parameter sets characterising the first and second oscillatory mechanical
responses, and comparing the first parameter set with the second parameter set
to assess a possible presence of DVT in the subject.
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For convenience of description, where the term ‘muscle’ is employed in
this specification (including in the appended claims), this should be understood to
encompass a single muscle, or one or more muscles comprising a muscle group.
Embodiments of the invention take advantage of the observation that,
upon clinical examination, in certain pathological states there is a change in the
calf muscles that may be identified in a response to a percussive stimulus applied
to the tissues. In DVT, it has been observed that the normal mobility of the calf is
reduced. It has thus been observed that tapping the calf, which under normal
circumstances has considerable mobility like the bounce of jelly, results in a more
‘dough-like’ response in the presence of DVT. It has been found, however, that
skill and experience are required in order to master the use of this technique for
the detection of DVT. It is reasonably anticipated that similar effects will also
occur in comparable pathological states of other soft tissues.
Embodiments of the invention further take advantage of the fact that
disorders such as DVT are normally present in only one comparable muscle of
the subject. Thus, for example, if a DVT is present in the right calf muscle, the
response of this muscle to a mechanical stimulus should be different from the
response of the subject’s left calf muscle, in which DVT is unlikely to be present.
Embodiments of the present invention therefore advantageously
employ a consistent, reproducible, objective analysis technique, comprising
determining and comparing suitable parameter sets of corresponding pairs of
muscles of the subject, in order to detect significant differences indicative of the
possible presence of DVT or other abnormality.
Accordingly, in embodiments of the invention the first and second
muscles are corresponding muscles of respective left and right limbs of the
subject. Particularly, the first and second muscles are left and right calf muscles
of the subject.
18004361_1 (GHMatters) P38862NZPC
Embodiments of the invention decompose the oscillatory mechanical
responses of the first and second muscles, such that the first and second
parameter sets each comprise at least one frequency parameter and at least one
corresponding damping parameter characterising the oscillatory mechanical
response. Typically, the first and second parameter sets each comprise
frequency parameters and corresponding damping parameters of two or more
oscillatory components of the mechanical responses of the first and second
muscles of the subject.
According to embodiments of the invention, a possible presence of
DVT in the subject is assessed based on a difference between the frequency
parameters and/or the damping parameters of the first and second parameter
sets. For example, a lower frequency response and/or more rapid damping in
one of the first and second muscles, as compared with the other, is indicative of
the possible presence of DVT in the subject. The frequency response may
typically be a resonant frequency, while damping may be measured as a damping
factor.
For the purpose of comparing the first parameter set with the second
parameter set, the signal processor may be configured to present information
relating to the first and second parameter sets on a display, for evaluation and
further diagnostic consideration, by an operator such as a trained technician,
trained clinician, or other healthcare worker.
In other embodiments, further statistical analysis of the first and
second parameter sets, obtained over multiple responses of the first and second
muscles of the subject to multiple mechanical stimuli, may be performed in order
to assess the statistical and/or clinical significance of differences between the
first and second parameter sets, and to provide a specific indication, such as a
likelihood or probability of the presence of an abnormality such as DVT in the
subject.
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According to embodiments of the invention, the motion sensor
comprises an accelerometer. Advantageously, the accelerometer comprises a
multi-axis accelerometer, such as a two-axis or three-axis accelerometer.
In some embodiments, a communications channel between the
transmitter and the receiver comprises a wired connection.
In other embodiments, a communications channel between the
transmitter and the receiver comprises a wireless connection. Advantageously,
the use of a wireless connection between the motion sensor and the signal
processor avoids the possibility that the presence of physical connections may
influence the mechanical response of the first and second muscles resulting from
the applied mechanical stimuli.
In various embodiments, the signal communicated from the transmitter
to the receiver may be an analog signal, which may be sampled and digitised at
the receiver, or may be a digital signal, having been sampled and digitised at the
motion sensor.
In one aspect, the present invention provides an apparatus for
assessing a subject at risk of a soft tissue abnormality such as deep vein
thrombosis, DVT, comprising: a receiver, configured to receive a signal from a
transmitter associated with a motion sensor, the motion sensor being adapted to
be fixed, in use, to a muscle of the subject, and configured to transmit a signal
representing motion of the sensor; and a signal processor, configured to analyse
data received via the receiver from the motion sensor, wherein the signal
processor receives first and second data sets via the receiver from the motion
sensor, the first data set representing a first decaying oscillatory mechanical
response of a first muscle of the subject resulting from a first percussive
mechanical impulse applied to the first muscle while in a relaxed state, and the
second data set representing a second decaying oscillatory mechanical response
of a second muscle of the subject resulting from a second percussive mechanical
18004361_1 (GHMatters) P38862NZPC
impulse applied to the second muscle while in a relaxed state; and the analysis
comprises: determining a first parameter set characterising the first decaying
oscillatory mechanical response, determining a second parameter set
characterising the second decaying oscillatory mechanical response, and
comparing the first parameter set with the second parameter set to identify
differences therebetween, indicative of a possible presence of DVT in the subject.
In another aspect, the present invention provides an analysis method
for assessing a subject at risk of a soft tissue abnormality such as DVT, the
method comprising: receiving a first data set representing a first decaying
oscillatory mechanical response of a first muscle of the subject resulting from a
first percussive mechanical impulse applied to the first muscle while in a relaxed
state; receiving a second data set representing a second decaying oscillatory
mechanical response of a second muscle of the subject resulting from a second
percussive mechanical impulse applied to the second muscle while in a relaxed
state; analysing the first data set to determine a first parameter set characterising
the first decaying oscillatory mechanical response; analysing the second data set
to determine a second parameter set characterising the second decaying
oscillatory mechanical response; and comparing the first parameter set with the
second parameter set to identify differences therebetween, indicative of a
possible presence of DVT in the subject.
Also disclosed herein is a method of assessing a subject at risk of
DVT, comprising the steps of:
applying a mechanical stimulus to a first muscle of the subject,
resulting in an oscillatory mechanical response of the first muscle;
acquiring a first data set representing the oscillatory mechanical
response of the first muscle, and analysing the first data set to obtain a first
parameter set characterising the oscillatory mechanical response;
applying a mechanical stimulus to a second muscle of the subject,
resulting in an oscillatory mechanical response of the second muscle;
acquiring a second data set representing the oscillatory mechanical
18004361_1 (GHMatters) P38862NZPC
response of the second muscle and analysing the second data set to obtain a
second parameter set characterising the oscillatory mechanical response; and
comparing the first parameter set and the second parameter set to
assess possible presence of DVT in the subject.
Further features, benefits and advantages of embodiments of the
invention will be apparent from the following description, which is provided by way
of example only, and should not be considered as limiting of the scope of the
invention as defined in any of the preceding statements, or in the claims
appended hereto.
BRIEF DESCRIPTION OF THE DRAWINGS
Embodiments of the invention will now be described with reference to
the accompanying drawings, in which like reference numerals indicate like
features, and wherein:
Figure 1 is a schematic diagram of a system embodying the invention;
Figure 2 is a schematic diagram of a motion sensor suitable for use in
the system of Figure 1;
Figure 3 is a diagram illustrating position and support of a leg of a
subject arranged for testing by the system of Figure 1;
Figure 4 is a graph of a motion sensor data set representing an
exemplary mechanical response obtained according to an embodiment of the
invention;
Figure 5 is a graph illustrating decomposition of a data set into three
decaying sinusoidal components according to an embodiment of the invention;
Figure 6 is a chart illustrating an illustrative statistical analysis of
frequency parameters obtained in accordance with an embodiment of the
invention; and
Figure 7 is a chart illustrating an illustrative statistical analysis of
damping parameters obtained in accordance with an embodiment of the
invention.
18004361_1 (GHMatters) P38862NZPC
DETAILED DESCRIPTION OF EMBODIMENTS
Figure 1 is a schematic diagram of a system 100 embodying the
invention. The system 100 captures data sets obtained by applying mechanical
stimuli to muscles of a subject. Shown schematically in Figure 1 is a
cross-section through the lower leg 102 of a subject, comprising bones 104 (i.e.
fibula and tibia), muscles 106 (i.e. peroneals, tibialis posterior, gastrocnemius,
and/or soleus muscles). Collectively, the various muscles making up the back
portion of the lower leg are called ‘the calf’.
In accordance with the exemplary system 100, one or more motion
sensors 108 are fixed to the exterior of the limb, e.g. at the rear and/or side of the
calf. In some embodiments of the invention, a single motion sensor, such as a
multi-axis accelerometer, may be sufficient. It may be advantageous to provide
multiple motion sensors to enable data from each to be combined and/or
selection of signals from a sensor providing the strongest or cleanest signal.
The system 100 further includes signal processing apparatus 110,
which is configured to receive and analyse information captured by the motion
sensor, or sensors, 108. As shown in Figure 1, the signal processor 110
comprises a combination of hardware and software, in which the hardware and/or
the software are configured to embody various features of the invention. The
signal processing apparatus 110 may comprise a standard hardware
configuration, such as a personal computer, a smartphone or other portable
device, or any other suitable computing and communications platform.
Alternatively, the signal processing apparatus 110 may comprise custom or
semi-custom hardware, including programmable components such as
microprocessors, or programmable-logic devices such as a field programmable
gate array (FPGA). Where programmable-logic devices are used, these may be
configured to include functional blocks implementing features of discrete devices,
such as microprocessors, memory devices, and custom analog and/or digital
components. As will be appreciated by electronics system designers and
18004361_1 (GHMatters) P38862NZPC
engineers, various hardware and/or software implementation options are
available, falling within the scope of the present invention.
The exemplary signal processor 110, as illustrated in Figure 1,
comprises a microprocessor component or functional block 112. The
microprocessor 112 is interfaced to, or otherwise operably associated with, a
non-volatile memory/storage device 114. The non-volatile storage 114 may be a
hard disk drive (e.g. if the signal processor 110 is implemented using a personal
computer), or may include a solid-state non-volatile memory, such as read-only
memory (ROM), flash memory, or the like. The microprocessor 112 is also
interfaced to volatile storage 116, such as random access memory (RAM) which
contains program instructions and/or transient data relating to the operation of the
signal processor 110.
In a conventional configuration, the non-volatile storage device 114
maintains program and data content relevant to the normal operation of the signal
processing apparatus 110. For example, if the apparatus 110 is implemented
using a personal computer, smartphone, or the like, the storage device 114 may
contain operating system programs and data, as well as other executable
application software necessary to the intended functions of the signal processing
apparatus 110. The storage device 114 may also contain program instructions
which, when executed by the microprocessor 112, instruct the apparatus 110 to
perform operations in accordance with an embodiment of the present invention,
for assessing a subject at risk of DVT. In operation, instructions and data held in
non-volatile storage 114 may be transferred to volatile memory 116 as required.
The microprocessor 112 is also operably associated with a
communications interface 118 in a conventional manner. The communications
interface 118 enables communication between the signal processor 110 and the
one or more motion sensors 108. Communications between the signal
processing apparatus 110 and each motion sensor 108 may be via a wired
connection, such as a Universal Serial Bus (USB) connection, or may be via a
18004361_1 (GHMatters) P38862NZPC
wireless connection, such as a Bluetooth, Bluetooth Low Energy (BLE), Wi-Fi, or
other wireless communications channel. In the exemplary embodiment of the
system 100, communication is conducted via a wireless BLE channel.
In use, the volatile storage 116 includes a corresponding body 120 of
program instructions configured to perform processing and operations embodying
features of the present invention, as described in greater detail below, particularly
with reference to Figures 4 to 7.
The signal processing apparatus 110 further includes a display 122
interfaced with the microprocessor 112, enabling information to be communicated
to an operator of the apparatus 110. As shown in Figure 1, the display 122 is
integrated with the apparatus 110, and may be, for example, a touchscreen
display of a smartphone or similar device. Alternatively, the display 122 may be
incorporated into a custom- or semi-custom-designed signal processing
apparatus 110. In still other embodiments, the display 122 may be the display of
a personal computer, a tablet device, a notebook, or other similar conventional
computing platform.
Figure 2 is a schematic diagram of a motion sensor suitable for use in
the system of Figure 1. The exemplary motion sensor 108 comprises an
accelerometer 202, a local processor 204, and a communications interface 206.
Various commercially available components may be used in the design
and implementation of the motion sensor 108. For example, the motion sensor
202 may be the part number KXTJ9 Tri-axis Accelerometer, available from Kionix
Incorporated of Ithaca, New York. The local processor 204 may comprise part
number CC2541, Bluetooth Low Energy and Proprietary System-on-Chip,
available from Texas Instruments Incorporated of Dallas Texas. The CC2541
part comprises all of the components necessary to communicate with the
accelerometer 202, and to implement a wireless BLE communications interface
for transmitting accelerometer data to the signal processor 110. The CC2541
18004361_1 (GHMatters) P38862NZPC
part requires only an external antenna 206 for transmitting and receiving BLE
signals at 2.4 GHz.
A more complete off-the-shelf motion sensor 108 may be obtained
from corporations such as Texas Instruments, in the form of components such as
the CC2541 SensorTag reference design, which is a ‘development kit’ comprising
the CC2541 System-on-Chip, the KXTJ9 Accelerometer, along with additional
sensors for detecting and recording movement, magnetic fields, humidity,
pressure and temperature. The SensorTag reference design is configured for
communications with smartphones and other mobile devices executing either the
android or iOS operating systems. It is supported by software development tools,
and the ability to download software/firmware updates directly from a smartphone
or other device. Accordingly, although the SensorTag reference design
incorporates sensors that may not be required in a basic implementation of the
invention, it provides a convenient development platform for motion sensors 108
embodying the invention.
Figure 3 is a schematic diagram illustrating exemplary position and
support of a leg 300 of a subject, arranged for testing using the system 100 of
Figure 1. Supports 302, 304 are provided for the upper leg and foot, respectively,
and one or more motion sensors 108 are fixed, e.g. via a strap, tape or other
suitable fixing mechanism, to the lower leg 102. The arrangement of supports
302, 304 is provided by way of example only, and other arrangements are
possible, e.g. the provision of upper leg support 302 may be optional. In this
position, the subject may relax his or her calf muscle, such that a trained
technician, clinician, or other healthcare worker may apply a mechanical stimulus,
such as a percussive impulse (represented by arrow 124 in Figure 1) resulting in
an oscillatory mechanical response of the subject’s calf muscles. The oscillatory
mechanical response causes corresponding voltages to be generated along the
tri-axis detectors within the accelerometer 202. In the case of the KXTJ9
accelerometer part, the voltages are digitised on-chip, at a selectable resolution
of 8, 12 or 14 bits, and transferred to the local processor 204 via an inter-
18004361_1 (GHMatters) P38862NZPC
integrated circuit (I C) standard communications interface. The local processor
204 is programmed to collect, format and transmit the data set corresponding with
the digitally sampled accelerometer readings via the transmitter 206 to the
receiver 118 of the signal processor 110. In an exemplary embodiment, the BLE
communications channel supports a sampling rate of about 800 samples per
second, using a 12-bit resolution. Increasing the bandwidth of the
communications channel, and/or improving the efficiency of transmission (e.g. by
compressing the transmitted data) may enable higher resolution data acquisition,
such as one kilosamples/s at up to 14 bits per sample.
As noted above, in the exemplary embodiment a tri-axis accelerometer
is employed. This provides motion information, in the form of voltage levels
representing acceleration, along three orthogonal axes. Embodiments of the
invention may utilise acquired data from a single axis (e.g. the axis providing the
strongest signal), or may combine data from multiple axes by computing a
resultant magnitude of the acceleration vector. Computation of the magnitude of
the acceleration vector may be implemented by software executing on the local
processor 204, or on the signal processing apparatus 110. Advantageously,
computing the resultant acceleration vector magnitude at the local processor 204
reduces the volume of information that must be transmitted via the BLE
communications link from the motion sensor 108 to the signal processor 110.
Figure 4 shows a graph 400 of a motion sensor data set representing
an exemplary oscillatory mechanical response obtained using the arrangement
and apparatus described above with reference to Figures 1 to 3. Time is
represented on the horizontal axis 402, while the accelerometer voltage level
corresponding with the resultant acceleration magnitude is represented on the
vertical axis 404. As can be seen from the resulting acquired data trace 406, a
mechanical percussive impulse is applied after approximately 0.17 seconds,
resulting in a generally oscillatory motion of the subject’s calf, apparent in the
portion 408 of the trace 406. This motion substantially settles over a period of
approximately 0.8 seconds. A low level of additive noise is also visible,
18004361_1 (GHMatters) P38862NZPC
particularly on the steady state portions of the trace 406. The goal of analysis of
the oscillatory mechanical response of the subject’s calf can therefore be defined
as extracting a relevant parameter set, useful for assessing the possible presence
of DVT, from the acquired data, in the presence of additive noise.
According to exemplary embodiments of the invention, a starting
assumption for the analysis, which may be performed using suitably configured
software, firmware and/or hardware implemented on the signal processing
apparatus 110, is that the acquired data set 406 may be represented as a
superposition of two or more exponentially decaying sinusoidal waveforms. On
this assumption, a waveform such as the trace 406 may generally be represented
in the following form:
−γ t
V(t)= Ae cos(ωt+φ )+n(t)
i i i
In the above equation, N represents the number of exponentially
decaying sinusoidal components to be used in fitting the acquired data set 406.
Experiments conducted by the inventors have indicated that N=3 is generally a
suitable choice. Each component also has an amplitude A, a characteristic
frequency ω, an associated phase Φ, and a damping coefficient γ. The function
n(t) represents the additive noise. It should also be noted that, for the purposes
of analysis, the signal is a discrete time sequence of samples of the motion
sensor voltage.
Various techniques are available for analysing signals such as that
shown in the graph 400 of Figure 4. For example, Fourier analysis could be
employed, based upon the Fast Fourier transform (FFT). However, the FFT may
suffer from limited frequency resolution, spectral leakage, picket fence effect, and
scalloping loss. While these may be mitigated through choice of proper
apodisation, averaging, zero padding, up-sampling, and other well-known
18004361_1 (GHMatters) P38862NZPC
approaches, the use of FFT-based methods for analysis of short, aperiodic,
signals is likely to be suboptimal.
An alternative technique would be to use a nonlinear regression
technique, such as nonlinear least-squares fitting, in order to estimate the
unknown parameters of the above equation. Such general techniques may be
unstable or unreliable in the presence of additive noise, and may perform poorly
when fitting large numbers of unknown parameters. Accordingly, it may be
preferable to employ more-targeted techniques for fitting or estimating the
parameters of a sum of exponentially decaying sinusoids in the presence of
additive background noise. One such targeted technique is Prony’s method,
however it has been shown that better performance can be obtained using a
Matrix Pencil Method (MPM) as described in Hua and Sarkar, ‘Matrix Pencil
Method for estimating parameters of exponentially damped/undamped sinusoids
in noise’, IEEE Transactions on Acoustics, Speech and Signal Processing,
Volume 38, No. 5, May 1990.
Using the MPM technique, a generally damped oscillatory response,
such as the trace 406 shown in the graph 400, may be represented as a
superposition of a plurality of exponentially-damped sinusoids, in accordance with
the above equation, as illustrated by the graph 500 shown in Figure 5. Again, the
horizontal axis 502 represents time, while the vertical axis 504 represents the
equivalent acceleration amplitude in volts, relative to the steady state reading.
The acquired data set has been decomposed into three oscillatory components,
comprising a dominant component 506, and two smaller components, 508, 510.
By design, the MPM technique minimises error due to noise, and thus any further
exponentially decaying sinusoidal components would necessarily be of smaller
magnitude relative to the three components 506, 508, 510 shown in the graph
500. It is, accordingly, apparent that the inclusion of additional components in the
analysis would be of minimal benefit. Indeed, increasing the number of
parameters to be estimated may impact upon the stability and reliability of the
analysis method.
18004361_1 (GHMatters) P38862NZPC
A key insight of the present inventors is that the mechanical response
of a muscle is altered in the presence of DVT. While there is considerable
variation between muscles of different subjects, the expected variation between
corresponding muscles of pairs of limbs of a single subject is expected to be
considerably smaller. Therefore, the possible presence of DVT in one muscle of
a subject, such as the right calf muscle, may be assessed by comparison with the
corresponding muscle of the other limb, e.g. the left calf muscle. As described
below, with reference to Figures 6 and 7, a suitable comparison may be made
using parameter sets derived from analysis, such as described above, of the
mechanical response of each muscle.
According to embodiments of the present invention a parameter set is
derived from each data set using MPM analysis. The characteristic frequencies
and damping coefficients (or, equivalently, damping factors) of the principal
exponentially decaying sinusoidal component have been found to comprise useful
parameter sets for the purpose of comparison. Highly statistically significant
differences in these parameters have been identified in subjects experiencing
DVT in one calf muscle.
By way of example, Figure 6 is a chart 600 illustrating a preliminary
statistical analysis of characteristic frequency parameters obtained in accordance
with an embodiment of the invention. The horizontal axis 602 of the chart shows
the characteristic frequency (in radians per second), while the vertical axis 604
represents the number of events (out of 10 repeated tests for the left leg, and
nine for the right), falling within each identified characteristic frequency band. The
result of this analysis is a histogram 606 for the left leg of the subject, to which a
corresponding normal distribution curve 608 has been fitted, and a further
histogram 610 for the right leg, to which a corresponding normal distribution curve
612 has been fitted. In this particular case, the subject is known, from other tests,
to have DVT in the right calf muscle, but not in the left calf muscle. The reduction
in the characteristic frequency resulting from the presence of the DVT is
18004361_1 (GHMatters) P38862NZPC
statistically significant, having a p-value of 0.01 (i.e. less than 0.05) when
analysed using the statistical t-test..
Figure 7 shows a similar chart 700 resulting from a preliminary
statistical analysis of damping factors. In this case, the horizontal axis 702
represents damping factor, while the vertical axis 704 once again shows the
number of events within each identified damping factor range obtained from a
sample of 10 repeated mechanical stimulus impacts. A histogram 706
representing damping factors for the left calf muscle has been fitted with a
corresponding normal distribution curve 708, while a histogram 710
corresponding with damping factors for the right calf muscle has been fitted with a
normal distribution curve 712. There is, once again, a clear difference between
the left and right calf muscle response, and t-test analysis has established that
the difference is statistically significant, having a p-value of 0.02. It is clear that a
higher damping factor is observed in the muscle having DVT.
In accordance with embodiments of the invention, the above analysis
may be performed by the signal processor 110, and graphs such as the response
400 shown in Figure 4, and results of statistical analyses such as the charts 600,
700 shown in Figures 6 or 7, rendered on the display 122. An operator, i.e. a
trained technician, clinician or other healthcare worker, will thus be able to assess
the possible presence of DVT in the subject of the test by visual inspection of the
displayed results.
According to some embodiments of the invention, the statistical
analysis may be used in further comparisons performed by the signal processor
110 in order to offer the operator a preliminary assessment of likelihood of the
presence of DVT. For example, based upon the analysis of aggregate data using
computational and statistical methods including, but not confined to, logistic
regression, the results may be used to assess the differences in characteristic
frequency and/or damping factor between a series of tests performed on left and
right calf muscles of the subject. If the likelihood or probability of the differences
18004361_1 (GHMatters) P38862NZPC
is above a clinically and empirically determined threshold (or any other desired
criterion, depending upon requirements) then the signal processor 110 may
highlight to the operator the likelihood or probability that DVT is present, and
identify the relevant muscle.
Advantageously, therefore, embodiments of the invention provide a
useful diagnostic tool that may be used by healthcare workers to identify patients
having a high likelihood of DVT. Such patients may then be referred for further
testing and diagnosis. Used as a preliminary test, embodiments of the invention
may be able to eliminate the likelihood that DVT is present in individual subjects,
thus avoiding the need for unnecessary further testing, which may be costly,
invasive and inconvenient.
While particular embodiments and variations of the invention have
been described herein, further modifications and alternatives will be apparent to
persons skilled in the relevant art. For example, while the system described with
reference to Figure 1 and 2 employs a motion sensor 108 which performs data
acquisition and digital transmission, along with a signal processing apparatus 110
that receives the digital signals and performs further processing and analysis,
other arrangements are also possible. For example, additional analysis and
processing could be performed on-board the motion sensor unit 108.
Furthermore, a wired, rather than a wireless, connection may be provided
between the motion sensor unit 108 and the signal processing apparatus 110.
Furthermore, analog signals may be obtained by the motion sensor, e.g. voltage
representing acceleration or other motion parameters, and transmitted in analog
form to the signal processing apparatus 110, which would receive and digitise the
analog signals. Various combinations and arrangements of the above
alternatives are also possible, and fall within the scope of the present invention.
In other variations, alternative forms of analysis may be employed to
determine parameter sets characterising muscle response to mechanical stimuli,
such as modal analysis.
18004361_1 (GHMatters) P38862NZPC
Accordingly, the particular embodiments described in detail should be
understood as provided by way of example, for the purpose of teaching the
general features of the invention, but should be understood as not limiting of the
scope of the invention, which is defined in the following claims.
18004361_1 (GHMatters) P38862NZPC
Claims (13)
1. An apparatus for assessing a subject at risk of a soft tissue abnormality such as deep vein thrombosis, DVT, comprising: a receiver, configured to receive a signal from a transmitter associated with a motion sensor, the motion sensor being adapted to be fixed, in use, to a muscle of the subject, and configured to transmit a signal representing motion of the sensor; and a signal processor, configured to analyse data received via the receiver from the motion sensor, characterised in that: the signal processor receives first and second data sets via the receiver from the motion sensor, the first data set representing a first decaying oscillatory mechanical response of a first muscle of the subject resulting from a first percussive mechanical impulse applied to the first muscle while in a relaxed state, and the second data set representing a second decaying oscillatory mechanical response of a second muscle of the subject resulting from a second percussive mechanical impulse applied to the second muscle while in a relaxed state; and the analysis comprises: determining a first parameter set characterising the first decaying oscillatory mechanical response, determining a second parameter set characterising the second decaying oscillatory mechanical response, and comparing the first parameter set with the second parameter set to identify differences therebetween, indicative of a possible presence of DVT in the subject.
2. The apparatus of claim 1 wherein the first and second muscles are corresponding muscles of respective left and right limbs of the subject.
3. The apparatus of claim 1 wherein the first and second muscles are left and right calf muscles of the subject. 18004361_1 (GHMatters) P38862NZPC
4. The apparatus of claim 1 wherein the analysis comprises decomposing the oscillatory mechanical responses of the first and second muscles, such that the first and second parameter sets each comprise at least one frequency parameter and at least one corresponding damping parameter characterising the oscillatory mechanical response.
5. The apparatus of claim 4 wherein the first and second parameter sets each comprise frequency parameters and corresponding damping parameters of two or more oscillatory components of the mechanical responses of the first and second muscles of the subject.
6. The apparatus of claim 4 wherein a possible presence of DVT in the subject is assessed based on a difference between the frequency parameters and/or the damping parameters of the first and second parameter sets.
7. The apparatus of claim 1 wherein, for the purpose of comparing the first parameter set with the second parameter set, the signal processor is configured to present information relating to the first and second parameter sets on a display.
8. The apparatus of claim 1 wherein the signal processor is configured to perform further statistical analysis of the first and second parameter sets, obtained over multiple responses of the first and second muscles of the subject to multiple mechanical stimuli, in order to assess a statistical significance of differences between the first and second parameter sets, and to provide a specific indication of the presence of DVT or other soft tissue abnormality in the subject.
9. The apparatus of claim 1 wherein a communications channel between the transmitter and the receiver comprises a wired connection. 18004361_1 (GHMatters) P38862NZPC
10. The apparatus of claim 1 wherein a communications channel between the transmitter and the receiver comprises a wireless connection.
11. A system comprising the apparatus of any one of claims 1 to 10 and the motion sensor.
12. The system of claim 11 wherein the motion sensor comprises an accelerometer.
13. An analysis method for assessing a subject at risk of a soft tissue abnormality such as DVT, the method comprising: receiving a first data set representing a first decaying oscillatory mechanical response of a first muscle of the subject resulting from a first percussive mechanical impulse applied to the first muscle while in a relaxed state; receiving a second data set representing a second decaying oscillatory mechanical response of a second muscle of the subject resulting from a second percussive mechanical impulse applied to the second muscle while in a relaxed state; analysing the first data set to determine a first parameter set characterising the first decaying oscillatory mechanical response; analysing the second data set to determine a second parameter set characterising the second decaying oscillatory mechanical response; and comparing the first parameter set with the second parameter set to identify differences therebetween, indicative of a possible presence of DVT in the subject. 18004361_1 (GHMatters) P38862NZPC
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU2014904149 | 2014-10-17 | ||
AU2014904149A AU2014904149A0 (en) | 2014-10-17 | A screening test for detection of deep vein thrombosis | |
PCT/AU2015/050636 WO2016058053A1 (en) | 2014-10-17 | 2015-10-15 | A screening test for detection of deep vein thrombosis |
Publications (2)
Publication Number | Publication Date |
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NZ731861A NZ731861A (en) | 2021-10-29 |
NZ731861B2 true NZ731861B2 (en) | 2022-02-01 |
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