2 Brain-Machine Interface Play games? Communicate? Assist disable?
3 Brain-Machine Interface
4 Brain-Machine Interface (By ATR-Honda)
5 The Need for Biomedical Signal Processing
6 What is it? Biomedical Signal Processing: Application of signal processing methods, such as filtering, Fourier transform, spectral estimation and wavelet transform, to biomedical problems, such as the analysis of cardiac signals, the breathing cycle, etc. A broader aspect: Biomedical imaging, genomic signal processing, etc.
7 Medical Diagnosis: Heart Attack as another Example Heart attack: Coronary artery disease, blockage of blood supply to the myocardium.
8 Medical Diagnosis: Heart Attack as an Example Plaque: A gradual buildup of fat (cholesterol) within the artery wall.
9 Medical Diagnosis: Heart Attack as an Example Symptoms: Chest pressure with stress, heart burn, nausea, vomiting, shortness of breath, heavy sweating. Chest pain, heart attack, arrhythmias.
10 Medical Diagnosis: Heart Attack as an Example Diagnosis: Prehospital electrocardiography (ECG). Continuous/serial ECG. Exercise stress ECG. Biochemical tests and biomarkers. Sestamibi myocardial perfusion imaging. Echocardiography. Computer-based decision aids.
11 Medical Diagnosis: ECG
12 Medical Diagnosis: ECG
13 Medical Diagnosis: ECG
14 Medical Diagnosis: Heart Attack as an Example Treatment: Angioplasty. Stent implantation. Atherectomy. Coronary bypass surgery. Intravascular radiotherapy. Excimer laser.
15 Medical Diagnosis: Heart Attack as an Example Imaging: Ultrasound.
16 Medical Diagnosis: Heart Attack as an Example Imaging: Optics.
17 Biomedical Signals: Broader Definition Signals as a result of physiological activities in the body: Electrical and Non-electrical Invasive/Non-invasive interrogation of an external field with the body Diagnosis and therapy Will focus mostly on bioelectric signal.
18 Outline Bioelectrical signals: Excitable cells Resting/action potential ECG, EEG, etc Applications of signal processing techniques Sampling, filtering, data compression, etc Non-stationary nature of biomedical signals
19 Bioelectrical Signals ERG EEG ECG EGG The bioelectric signals represent many physiological activities. ENG EMG
20 Excitable Cells Neuron (Rabbit Retina) Ionic Relations in the Cell
21 Structural unit
22 Functional unit
23 Neural signaling (I)
24 Neural signaling (II)
25 Neural signaling (III)
26 Neural signaling (IV)
27 Measurements of Action Potential A C =ε d 6250 ions/µm2 for 100mV membrane potential
28 Goldman Equation E = RT F P ln P K K [ K ] [ ] [ ] o + PNa Na o + PCl Cl i [ K ] + P [ Na] + P [ Cl] i Na E: Equilibrium resting potential R: Universal Gas Constant (8.31 J/(mol*K)) T: Absolute temperature in K F: Faraday constant (96500 C/equivalent) P M : Permeability coefficient of ionic species M. i Cl o
29 Example: Ion Concentration Species Na + K + Cl - Intracellular (millimoles/l) (For frog skeletal muscle) Extracellular (millimoles/l)
30 Example: Equilibrium Resting Potential for frog skeletal muscle P Na : 2 X 10-8 cm/s P K : 2 X 10-6 cm/s P Cl : 4 X 10-6 cm/s E=-85.3 mv
31 Electrocardiogram (ECG)
32 ECG One of the main methods for assessing heart functions. Many cardiac parameters, such as heart rate, myocardial infarction, and enlargement can be determined. Five special groups of cell: SA, AV, common bundle, RBB and LBB.
47 ECG Diagnosis Other abnormalities: Myocardial infarction Atrial/Ventricular enlargement ST segment elevation
48 Pace Makers
49 Electroencephalogram (EEG)
50 EEG Electrical potential fluctuations of the brain. Under normal circumstances, action potentials in axons are asynchronous. If simultaneous stimulation, projection of action potentials are detectable. The analysis is based more on frequency than morphology.
51 EEG: Instrument
52 EEG: Spatial and Temporal Characteristics
53 EEG: Presentation
54 EEG Classification
55 EEG Classification Alpha: 8 to 13Hz. Normal persons are awake in a resting state. Alpha waves disappear in sleep. Beta: 14 to 30Hz. May go up to 50Hz in intense mental activity. Beta I waves: frequency about twice that of the alpha waves and are influenced in a similar way as the alpha waves. Beta II waves appear during intense activation of the central nervous system and during tension.
56 EEG Classification Theta waves: 4 to 7Hz. During emotional stress. Delta waves Below 3.5Hz. Deep sleep or in serious organic brain disease.
59 Other Non-Electrical Biomedical Signals Circulatory system Blood pressure Heart sound Blood flow velocity Blood flow volume
60 Other Non-Electrical Biomedical Signals Respiratory system Respiratory pressure Gas-flow rate Lung volume Gas concentration
61 Applications of Signal Processing Techniques
62 Sampling Digital analysis and presentation of biomedical signals. Sampling requirements. Low frequencies. Frequency ranges of different physiological signals may be overlapping. Electronic noise and interference from other physiological signals. Very weak (maybe µv level), the pre-amp circuit is often very challenging.
63 Filtering Digital filters are used to keep the in-band signals and to reject out-of-band noise. Low-pass, band-pass, high-pass and bandreject. Similar to those of other applications.
64 Noise Sources of ECG
65 High Voltage 50/60Hz AC AC 110V Ground
67 Noise Sources Inherent Instrumentation Environment From the body
68 Ideal Signal Vs. Signal with Powerline Noise
69 Ideal Signal Vs. Signal with Powerline Noise Powerline interference consists of 60Hz tone with random initial phase. It can be modeled as sinusoids and its combinations. The characteristics of this noise are generally consistent for a given measurement situation and, once set, will not change during a detector evaluation. Its typical SNR is in the order of 3dB.
70 Ideal Signal Vs. Signal with Electromyographic Noise
71 Ideal Signal Vs. Signal with Electromyographic Noise EMG noise is caused by muscular contractions, which generate millivolt-level potentials. It is assumed to be zero mean Gaussian noise. The standard deviation determines the SNR, whose typical value is in the order of 18dB.
72 Ideal Signal Vs. Signal with Respirational Noise
73 Ideal Signal Vs. Signal with Respirational Noise Respiration noise considers both the sinusoidal drift of the baseline and the ECG sinusoidal amplitude modulation. The drift can be represented as a sinusoidal component at the frequency of respiration added to the ECG signal. The amplitude variation is about 15 percent of peak-to-peak ECG amplitude. It is simulated with a sinusoid of 0.3Hz frequency with typical SNR 32dB. The modulation is another choice of representing respiration noise. It can be simulated with 0.3Hz sinusoid of 12dB SNR.
74 Ideal Signal Vs. Signal with Motion Artifacts
75 Ideal Signal Vs. Signal with Motion Artifacts Motion artifact is caused by displacements between electrodes and skin due to patients slow movement. It is simulated with an exponential function that decays with time. Typically the duration is 0.16 second and the amplitude is almost as large as the peak-to-peak amplitude. The phase is random with a uniform distribution.
76 Noise Removal The four types of noises are mostly sinusoidal or Gaussian. The sinusoidal noises are usually removed with a notch filter. Other distortions are zeroed out using the moving average.
77 Adaptive Noise Cancellation Noise from power line (60Hz noise). The noise is also in the desired frequency range of several biomedical signals (e.g., ECG), notch filter is required. Adaptive filtering: The amplitude and exact frequency of the noise may change.
78 Adaptive Filter ECG pre-amp output s + n 0 output y 60Hz outlet n 1 attenuator adaptive filter z
79 Adaptive Filter ) ( ) ( ) ( ) ( 0 nt z nt n nt s nt y + = ) ( 2 ) ( z n s z n s y + + = ] ) [( ] [ )] ( [ 2 ] ) [( ] [ ] [ z n E s E z n s E z n E s E y E + = + + = ] ) [( min ] [ ] [ min z n E s E y E + =
80 Adaptive Filtering for Fetal ECG
81 Pattern Recognition Abnormal physiological signals vs. the normal counterparts. An average of several known normal waveforms can be used as a template. The new waveforms are detected, segmented and compared to the template. Correlation coefficient can be used to quantify the similarity. = = = = N i X i N i T i N i X i T i X T X T ) ( ) ( ) )( ( µ µ µ µ ρ
82 Ex. ECG Pattern Recognition
83 Data Compression For large amount of data (e.g., 24 hour ECG). Must not introduce distortion, which may lead to wrong diagnosis. Formal evaluation is necessary.
84 ECG Data Compression WGAQQQQQQRBCCCCCHZY WGAQ*6RBC*5HZY
85 ECG Data Compression
86 ECG Data Compression
87 ECG Data Compression
88 Is straightforward implementation sufficient for biomedical signals?
89 Characteristics of Biomedical Signals (I): Weak Unimportant The information is in the details:
91 Characteristics of Biomedical Signals (II): Nonstationarity Fourier Transform : X + ( ω ) ( ) jωt = x t e dt Fourier transform requires signal stationarity. Biomedical signals are often time-varying. Short-time Fourier analysis Time-frequency representation Cyclo-stationarity
92 Nonstationarity: An EEG Example
93 Spectral Estimation for Nonstationary Signals Fourier Transform Short Time Fourier Transform X ( ω ) = + x( t) e jωt dt X ( ω, a) = + x( t) g( t a) e jωt dt
94 Another Example: Signal Processing for Blood Velocity Estimation (Please refer to the class notes.)
95 Other Important Biomedical Applications Biomedical imaging: X-ray, CT, MRI, PET, OCT, Ultrasound, Genomic signal processing,etc
96 Term Project
97 Term Project Part I: Implementation of the Pan-Tompkins Technique + heart rate estimation. Part II: ECG paper survey. Please read the description on the web site carefully.
98 The Pan-Tompkins Technique Band-Pass Filter Differentiator Squaring Moving Average Integrator
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