lecture 2-3 classification of biosignals - kpi.ua
TRANSCRIPT
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Lecture 2-3Classification of
Biosignals
Digital Signal Processing and Analysis in Biomedical Systems
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Contents
- What and why biosignals?
- Classification of biosignals
- Examples
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What are biosignals?
All types of biomedical systems either generate the signals to influence the
human body, or analyze biosignals to extract useful information about
functioning of human body.
Signal – is the parameter that is observable from the object.
Biosignal is a description of physiological phenomenon of any nature.
Bio+Signal = “living object” + “function that carries information about the
behavior or state”. Biosignals are the key objects in Biosystems.
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Variety of tasks - 1
- Signal generation (therapy, calibration, control)
- Coding and compression (security)
- Transmission (telemedicine)
- Registration and measurement (diagnostics)
- Processing (filtration)
- Transformation (sampling, representations in other spaces)
- Defining the parameters (feature extraction)
- Analysis (extraction of information)
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Variety of tasks - 2
- Connectivity and influence (between various processes)
- Prediction (forecasting the future values, states and events)
- Classification (Machine Learning)
- Control (biofeedback systems)
- Visualization (signals, analysis results)
- Storing (databases, cloud storages etc.)
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Signal analysis
At the level of signals, the biomedical
system for diagnosis is based on
Machine Learning.
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Why biosignals?
Biosignal carries all information about the living object. We analyze signals
which are coming from the body (ECG, EEG etc.) or are connected to the body (X-
ray images, ultrasonic images).
Biosignal can be used to understand the underlying physiological mechanisms
of a specific biological event or system.
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Biosignals through centuries
8Hippocrates palpating a young patient (500 BC) ICU in modern hospital (2016 AD)
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Example - Polysomnogram
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PSG signal
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Example - 2
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http://wexnermedical.osu.edu/mediaroom/pressreleaselisting/new-device-allows-brain-to-bypass-spinal-cord-move-paralyzed-limbs
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Classification of biosignals - 1
According to the physical nature of biosignals- Electric- Magnetic- Chemical- Mechanical (acoustic)- Optical- Thermal
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Classification of biosignals - 2
According to the system of origin of biosignals- Endocrine system- Nervous system (Central and Peripheral)- Cardiovascular system- Vision system- Auditory system- Musculoskeletal system- Respiratory system- Gastrointestinal System- Blood system
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Classification according to the physical nature of
signal
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Electric signals
Electric field is generated in cells (nerve and muscle) and organs because of
intra- and extracellular ionic currents. They are the results of electrochemical
processes in the single ionic channels.
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Action potential generation
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Types of electrical signals
Neural cellsENG – electroneurogram
EEG – electroencephalogram
ERG – electroretinogram
Muscle cellsECG – electrocardiogram
EMG – electromyogram
Other cellsEOG – electrooculorgam
GSR – galvanic skin response17
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ECG generation
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Bioelectric signals
Amplitude and spectral ranges of
bioelectric signals
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Magnetic biosignals
Weak magnetic fields are generated by different organs and
cells.
Neural cellsMNG – magnetoneurogram
MEG – magnetoencephalogram
Muscle cellsMCG – magnetocardiogram
MMG – magnetomyogram
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Magnetic signal strength comparison
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Magnetoencephalography (MEG)
MEG is based on measuring the magnetic field outside the head using an array
of very sensitive magnetic field detectors (magnetometers). The signals
recorded by EEG and MEG directly reflect current flows generated by neurons
within the brain. The temporal frequency content of these signals ranges from
less than 1 Hz (one cycle per second) to over 100 Hz (100 cycles per second).
Because MEG and EEG measure neuronal activity in “real time” the connections
activated either at rest or during task can be measured, giving us a picture of the
dynamic interactions among brain networks. MEG has much greater temporal
resolution than fMRI so MEG-based analysis provides high temporal resolution
data for analyzing the neuromagnetic correlates of fMRI connectivity, its time-
frequency content, and temporal interactions.23
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MEG measurement
Magnes 3600 with 248 magnetometers within a shielded room with 64 EEG Voltage Channels and 23 MEG reference
channels (5 gradiometer, and 18 magnetometer).
Five current coils attached to the subject, in combination with structural imaging data and head surface tracings, are
used to localize the brain in geometric relation to the magnetometers. Auditory (earphone), visual (screen external to
shielded room) and somatosensory (air-driven tactile) stimuli can be delivered. 24
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MEG signal
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fMRI+MEG during rest
Source-space band-limited power (BLP) correlation maps obtained for the Dorsal Attentive Network. Left: fMRI
seed based conjunction maps. Right: MEG t-statistic images across subjects computed using epochs of
maximum correlation. The topography of the non-stationary MEG Resting State Network (RSN) is similar to the
RSN obtained by fMRI.26
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Magnetocardiography (MCG)
MCG is the measurement of magnetic fields emitted by the human heart from
small currents by electrically active cells of the heart muscle.
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Data derived from MCG
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Map of magnetic field distribution
Averaged MCG 36-channels
Map of current density distribution
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Myocardium current density maps
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Chemical biosignals
Signals providing information about concentration of various chemical agents in
the body
- Level of glucose (diabetes)
- Blood oxygen level (asthma, obstructive pulmonary disease, heart and kidney
failure)
- Gases in blood and breathing airflow (anesthetic gases, carbon dioxide etc.)
- pH
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Blood saturation of gases
SaO2 – arterial blood oxygen saturation, describes the percentage of
hemoglobin molecules carrying oxygen.
SvO2 – venous oxygen saturation, describes how much oxygen the body
consumes
SpO2 – peripheral capillary oxygen saturation – the same as SaO2 but in the
capillary system
SpCO2 – concentration of carboxyhemoglobin in blood
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Photoplethysmography
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Mechanical biosignals
Biomechanical signals reflect mechanical functions of body parts
Examples:
- Blood Pressure
- Accelerometer signals describing human movements, gait, balance and pose
(Parkinson disease, mobile applications, fitness)
- Chest movements during respiration
- Air flow characteristics during MLV
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3D tracking using accelerometer
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Acoustic biosignals
Subset of mechanical signals that describe the acoustic sound produced by the
body (vibrations and motions). Bioacoustic signals give access to diverse body
sounds:
- Cardiac sounds (phonocardiography)
- Snoring (Obstructive Sleep Apnea detction)
- Swallowing
- Respiratory sounds
- Crackles of joints and muscles
Often measured at the skin using acoustic transducers such as microphones
and accelerometers. 37
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Phonocardiogram (PCG)
PCG reflects sounds of heartbeats, produced by heart sounds corresponding to
two consecutive heart valve closures. Indicates closure strength and the valve’s
stiffness.
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Respiratory sounds
Reflect normal breathing sounds superimposed with crackles, cough sounds,
rhonchus, snoring, squawk, stridor and wheeze sounds, which are associated
with pulmonary disorders.
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Electronic stethoscope
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Optical biosignals
Optical methods are among the oldest and best-established techniques for
sensing biochemical analytes.
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Immunology (antibody-antigen interaction)
Surface plasmon resonance is the collective oscillation of electrons
stimulated by incident light.42
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Thermal biosignals
Body temperature in the point and temperature maps, may describe heat loss
and heat absorption in the body, or temperature distribution over the body
surface.
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Thermography (temperature maps)
- Cancer
- Varicose veins
- Osteochondrosis, osteoporosis
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Classification according to the system of origin
of signal
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Endocrine System
Is the collection of glands of an organism that secrete hormones directly into the
circulatory system to be carried toward a distant target organ.
Signals:
- Chemical
- Optical
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Signals from Nervous System
Neurons and spinal cord
- Electroneurogram (Spike trains)
- Magnetoneurogram
Brain
- EEG, MEG
- Event-Related Potentials (acoustic, visual)
- Neurovisualization (MRI/fMRI, CT, PET, SPECT)
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Cardiovascular System
Heart & blood vessels
- ECG
- MCG (Current Density Maps)
- Blood pressure
- Heart Rate Variability
Visualization
- Ultrasonic Imaging
- MRI, Ultrasonic, X-ray
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Vision system
- EEG (visual cortex)
- VEP (Visual Evoked Potentials)
- EOG (Electrooculogram)
- ERG (Electroretinogram)
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Electroretinography
Electroretinography measures the electrical responses of various cell types in
the retina, including the photoreceptors (rods and cones), inner retinal cells
(bipolar and amacrine cells), and the ganglion cells.
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Auditory system
- EEG (Auditory Evoked Potentials)
- Audiometry
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Example of audiogram
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Musculoskeletal system
- EMG (Electromyogram)
- Visualization (MRI, X-Ray)
- Reography (myorelaxation)
- Accelerometry (gait)
- Stabilorgaphy (Parkinson’s)
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Stabilography
Analysis of balancing act and postural control
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Respiratory system
- Chemical signals (gas concentration)
- Mechanical (airflow, pressure, volume)
- Spirometry (flow-volume)
- Plethysmography (volume)
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Spyrography
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Gastrointestinal System
- MRI
- X-ray
- Ultrasound Imaging
- Chemical signals
- Electrogastrogram
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Elecrtogastrogram
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Blood System
- Chemical signals (concentrations)
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Other Classificationof biosignals
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Dimesionality
• 1D (ECG)
• 2D (temperature map)
• 3D (MRI image)
• 4D (fMRI image)
62Task-related activation
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Number of channels
• One channel (pulse wave)
• Three channels (accelerometer data)
• Multichannel (EEG)
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256 channels of EEG
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Analysis approach (signal models)
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Summary
Biosignals are the only source of information describing the functioning of the
human body in healthy and disease conditions.
Biosignals are of various nature and origin.
Many biosignals can contain information about same organ or system.
Researchers and practitioners should do
measurement,
processing,
analysis,
interpretation
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