Efficient Feature Extraction and Classification Methods in Neural Interfaces

Transcription

1 Efficient Feature Extraction and Classification Methods in Neural Interfaces Azita Emami Professor of Electrical Engineering and Medical Engineering

2 Next Generation Therapeutic Devices 2

3 Where We Started: Epiretinal Prosthesis Retinitis Pigmentosa (RP) and Age-related Macular Degeneration (AMD) affect millions worldwide 3

4 Integrated Circuit Chip Supporting 512 Channels PT: Power Telemetry DT: Data Telemetry GL : Global Logic ESD Ch1 Caps ESD Ch2 PAD1 Calib PAD2 3.1mm 260 um Ch3 Biasing Ch4 PAD3 Local Logic PAD4 DT GL PT ESD Caps ESD Manuel Monge now with 4

5 Neural Interfaces and Devices DBS for Parkinson's Epilepsy Implants Motor Prosthetics Brain-Spine Interface 5

6 Neuromodulation for Epilepsy FDA-approved anti-seizure devices: Open-loop: Vagus Nerve Stimulator (VNS) Closed-loop: Responsive Neurostimulator (RNS) Cortical strip lead Depth lead Neurostimulator VNS RNS 6

7 Implantable Neural Interfaces Wireless transmission of raw data Offline study of disease Signal acquisition Decoding algorithms Robot control Feature extraction Machine learning Neuromodulation Low-power & area-efficient integrated circuits 7

8 Electrophysiological Recording Modalities EEG Skull Epidural ECoG Subdural ECoG AP Skin Arachnoid Dura mater Pia mater Cortex Intracranial EEG (ieeg): A powerful modality in BMI applications 8

9 Seizure Detection at High Resolution 1cm 1mm 4mm ~70s M. Stead et al., Brain 10 9

10 Wireless Implantable Device Challenges RF wireless power delivery Sever absorption and scattering in the tissue Higher frequency, higher loss Higher frequency, smaller antenna 10

11 Closed-Loop Seizure Control System Major Challenges: Size, power consumption, latency Patient specific Early prediction 11

12 Closed-Loop System In-Vivo Test Results 93% Sensitivity, 0.15/h FAR Maximum Detection Delay: 0.5s Over 40 Reduction in Power-Area Product per Channel M. Shoaran et al., VLSI Symp 16 12

13 Next: Classifier-Based Integrated Systems Towards classifier-based detection: Improve the detection accuracy and reduce false positives Avoid the challenges associated with threshold setting and feature combination Prev. design: used compressed sensing Reduced the feature extraction overhead Based on thresholding: limited accuracy 13

14 Machine Learning in Medicine PRECISION MEDICINE IMPLANTABLE DEVICES WEARABLE DEVICES MEDICAL DIAGNOSTIC SYSTEMS MEDICAL IMAGING SYSTEMS PERSONAL HEALTH MONITORING ONCOLOGY 14

15 On-Chip Classification Model Decision Tree-based Classifiers Support Vector Machines Learning Models Linear Models K-nearest Neighbor Neural Networks 15

16 Implementation Challenges Complexity for on-chip implementation Multipliers, on-chip memories, not scalable Artificial Neural Networks Support Vector Machines K Nearest Neighbors 16

17 Decision Trees Low-depth Trees Frequency and time domain features: Line length Power in multiple freq bands Time domain variance 17

18 Gradient Boosted Decision Trees Gradient Boosted Trees Additive Ensemble of Shallow Decision Trees Final Answer General online memoryless seizure detection approach 18

19 Gradient Boosted Decision Trees Low-depth Trees Gradient Boosted Trees 19

20 Hardware Architecture 20

21 Integrated Circuit Chip in 65nm CMOS Area: 1.2 mm 2 8 fully programmable decision trees Supports 32 channels Frequency and time domain features Line length Power in multiple frequency bands Time domain variance 21

22 Results for 23 Patients F1= 2 1 Sensitivity + 1 Specificity 22

23 Why Start with Epilepsy? ü Most-studied neurological disorder 4th MOST COMMON NEUROLOGICAL DISORDER MOST COMMON ONE IN CHILDREN ü First step to learn basic brain mechanisms ü Human tissue and ieeg data available 65 MILLION 1/3 DRUG-RESISTANT $15.5 BILLION <%35 SURGERY SUCCESS Seizure-free with medications Partially controlled by surgery Need alternative therapies 23

24 Parkinson s Disease (PD) One of the most common neurodegenerative diseases Symptoms include muscle rigidity, 4-7 Hz rest tremor and akinesia 24

25 Deep Brain Stimulation Constant high-frequency (130 Hz) stimulation to the internal segment of the globus pallidum (GPi) or the subthalamic nucleus (STN) DBS reduces by 50% the motor scores of UPDRS (Unified Parkinson s Disease Rating Scale) 25

26 Closed-Loop v.s. Open-Loop Conventional DBS: parameters adjusted by a clinician frequency, pulse width and intensity Closed-loop adaptive DBS (adbs) Better efficacy Lower surgical and stimulation-related side effects Lower cost B. Rosin et al. 26

27 Other Applications: Migraine Prediction The most prevalent neurological disorder In the U.S. more than 37 million people During the most productive years Treatment Most effective if applied in an early stage Potential diagnostic biomarkers: HFOs in somatosensory evoked potentials (SSEPs) Brainstem auditory evoked potentials Features of resting-state EEG Na+ fluctuations in CSF 27

28 Conclusion Neural interfaces as next generation therapeutic devices Efficient hardware implementation is crucial Classification and feature extraction Wearable and implantable devices Power, size and performance Many challenges remain to be solved! 28

29 Mixed Mode Integrated Circuits & Systems 29