Presentation is loading. Please wait.

Presentation is loading. Please wait.

Classification of the Pulse Signals Based on Self-Organizing Neural Network for the Analysis of the Autonomic Nervous System Present by: Yu Yuan-Chu.

Similar presentations


Presentation on theme: "Classification of the Pulse Signals Based on Self-Organizing Neural Network for the Analysis of the Autonomic Nervous System Present by: Yu Yuan-Chu."— Presentation transcript:

1 Classification of the Pulse Signals Based on Self-Organizing Neural Network for the Analysis of the Autonomic Nervous System Present by: Yu Yuan-Chu

2 NCTU BCI Group2 Outline Autonomic Nerve System(ANS) The test function The relationship between heart rate & blood pressure R-R interval variability Data acquisition System Architecture Experimental Results Clinical procedures Spectral analysis result Classification of pulse signal result Correlation between ECG and Pulse signal

3 NCTU BCI Group3 ANS Test Function ANS function The movement of many internal organs The tempture, blood pressure, heart rate, endocrine and emotion Opposing the outside pressure Elements sympathetic nerves, parasympathetic nerves, and α, β receptors ANS test function Sympathetic: BP change in the state of supine and standing The test of the sustained handgrip Dark-adapted pupil size after parasympathetic blockade Parasympathetic : deep breathing HR response to standing Pancreatic polypeptide concentration Return

4 NCTU BCI Group4 The Relationship among Heart Rate, Blood Pressure and Baroreflex Blood Pressure(BP): mainly mechanically induced Heart Rate Variability (HRV): under baroreflex control via the vagus nerves BP and RR oscillations occurring at respiratory or Mayer wave(0.1Hz) frequencies is mediated by a baroreflex mechanism Return

5 NCTU BCI Group5 The R-R interval variability HRV derived from the ECG signals Sympathetic and Parasympathetic activities regions in PSD The relationship between R-R interval variability and autonomic nerves Return

6 NCTU BCI Group6 Data Acquisition Hardware Finapres: Finger arterial pressure utilizes the principle of arterial wall unloading ECG(12 leads): 12 different potential differences from the body surface SCXI-1140: signal conditioning module, 8-channel differential amplifier AT-MIO-16F-5: DAQ board, 200 kHz, its resolution is 12 bits Software(LabVIEW): Data acquisition system Data analysis systemp PSD, 3D PSD, baroreflex analysis and ART2 analysis system Hardware Architecture electrodes connected in an lead Π configuration Return

7 NCTU BCI Group7 System Configuration Main Purpose: Signal validation between ECG & BP Hamming windows, Autoregression, PSD Improve the analytic results Preprocessor, Adaptive Resonance Theorem of Version 2(ART2) R-R intervals from ECG and Pulse signals Return

8 NCTU BCI Group8 Power Spectral Density Analysis Attenuate the spectral leakage Describe the signal “ parsimoniously ” by a small number of coefficients Hamming Window(Time Domain) Hamming Window(Freq. Domain) Autoregressive spectrum: Linear Predict Coefficients(LPC) Return

9 NCTU BCI Group9 ART2 Blood Pressure Parameters Q-U: the pulse transmission time V-D: the diastolic shut time U-P: the systolic ejection time U-U ’ : the one cardiac time P-V: the slow time of ejection Self-organizes stable pattern recognition codes in real-time Continuous speech recognition and synthesis, pattern recognition, classification of noisy data, nonlinear feature detection Not affected by factors: human fatigue, emotional states, and habituation Return

10 NCTU BCI Group10 Clinical Procedures Six young controlled subjects(23-26 years old) without any clinically evident disease were examined Two standard autonomic tests were undertaken: Rest- All subjects were asked to lie quietly for 5 minutes with spontaneous breath Tilting- recorded over 5 minutes following passive tilting to 75 degree position by the electrically rotating table Studies were performed between 2:00 PM and 5:00 PM Temperture The environment tempture was controlled on 24.1 ° C Body temperatures of all subjects were at the range of 35 ° C to 38 ° C The validation testing between the ECG and arterial pulse variability is 97.81 + 1.38% (a) ECG(b) PluseReturn

11 NCTU BCI Group11 Spectral Analysis IndicesLFHFT-test value Area ︽﹀ p = 0.001 Mean ︽﹀ p = 0.002 Max ︽﹀ p < 0.001 SD ︽﹀ p = 0.002 (a) ECG/Rest(b) Pulse/Rest(a) ECG/Tilt(a) Pulse/Tilt ECG in the state of tilting up, T-test value between LF and HF ︽ : increase significantly, ﹀ : decrease not significantly IndexLFHF Area0.910.95 Mean0.950.98 Max0.570.74 SD0.950.88 Correlation between ECG and Finapres, Index of Area is best for the PSD in the HRV tests Return

12 NCTU BCI Group12 Classification of pulse signal result Return 48.8%, sitting up 60 degree27.8%, deep breathing Status Distribute Plot Deep Breathing(Original) Deep Breathing(after ART2) Sitting up 60 degree(Original) Sitting up 60 degree(after ART2)

13 NCTU BCI Group13 Correlation between ECG and Pulse signal Subject 1 Date : 03/19/97 Time : 09:55 PM State : Tilting up Body temperature=36.5 Environment temperature=24.1, Man, Birthday : 65.5.15, Years : 22 Return


Download ppt "Classification of the Pulse Signals Based on Self-Organizing Neural Network for the Analysis of the Autonomic Nervous System Present by: Yu Yuan-Chu."

Similar presentations


Ads by Google