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Biomedical Signal Extraction for Computer-assisted Clinical Decision Making - Dr. Behnaz Ghoraani - WiDS Miami 2018
- 1. Behnaz Ghoraani, Ph.D. Assistant Professor Florida Atlantic University Computer and Electrical Engineering Biomedical Signal Feature Extraction for Computer-assisted Clinical Decision Making Biomedical Signal and Image Analysis Lab
- 2. Agenda — Signal Processing andAnalysisTools — Research Projects at BSIA Lab — Atrial Fibrillation — Artifact Reduction inAuditory Evoked Potentials — AutomaticAssessment of Medication States of Patients with Parkinson’s Disease usingWearable Sensors — Automatic Localization of Epileptic Spikes in EEGs of Children with Infantile Spasms Talk @WDSC 20182
- 3. Signal Analysis/Information Extraction/Decision Making Feature Extraction Classifier Trained Classifier Training Phase Classification Phase Classification Result Sensor Classification Scheme Train Features Test Features Signal Processing Talk @WDSC 20183
- 4. -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0.5 1 1.5 2 2.5 3 Decision Making (Cont.) — What is Signal classification? Talk @WDSC 20184
- 6. Non-Stationary and Nondeterministic Talk @WDSC 20186
- 7. What Is Appropriate Signal Domain? — Time Domain? Talk @WDSC 20187
- 8. Fourier Transform dtetxX tj ò= - w w ).()( Signal comparator w1 w2 w3 ……………… w~ Sine(s) Cosine(s) w1 w2 w3 ……………… w~ time Basis ………………….w1 w~ Talk @WDSC 20188
- 9. What happens when you have a time varying spectrum ? Signal Fourier transform time Talk @WDSC 20189
- 10. Birth of Time-frequency Domain Short-time Fourier transform X(t,ω) = x(t +τ )g(τ ).e− jωτ dτ∫ t1 t2 t3 …… fft1 fft2 fft3 fft4 fft5 ……. Magnitude Magnitude fft stft fft1 fft2 fft3 Talk @WDSC 201810
- 11. What did we achieve ? Signal STFT time time Talk @WDSC 201811
- 12. Bird Example Talk @WDSC 201812
- 13. Time – frequency map (TF tiling) time frequency time frequency fr tr fr tr Heinsenberg’s boxes Talk @WDSC 2018 FT TF 13
- 14. Wavelets – Using a known small wave estimating an unknown signal CWTx ϕ (τ,s) = 1 s . x(t).w t −τ s " # $ % & 'dt∫ s s S=1/f t1 t2 t3 t3 …. s1,s2,s3,s4……………… Mother wavelet Talk @WDSC 201814
- 15. F>> F<< S<< S>> In Wavelets ‘scale’ is inversely tied to the ‘frequency ‘ time Scaling Wavelets • Good time resolution and poor freq resolution at higher frequencies • Poor time resolution and good freq resolution at lower frequencies. freq Time – frequency map (TF tiling) - Wavelets s= window size Variable, but restricted resolution constraints !!!Talk @WDSC 201815
- 16. Signal Wavelet timetime What did we achieve? Talk @WDSC 201816
- 17. Wigner-Ville distribution Wx (t,ω)= x(t).x(t +τ).e− jωt dτ∫ It is nothing but the Fourier transform of the auto correlation of the signal. In other words it is same as STFT where the window function is nothing but the signal itself !! So far the best TF resolution is achieved only by Wigner-Ville. Talk @WDSC 201817
- 18. Signal What did we achieve ? timetime Wigner-ville Cross terms Talk @WDSC 201818
- 19. Why do we have cross terms ? Wx (t,ω)= x(t).x(t +τ).e− jωt dτ∫ (a+b) = multi component signal x(t) . X(t+l) (a+b) (a+b) ( a2+b2+2ab) ( a2+b2+2ab) “ CROSS TERMS ”Talk @WDSC 201819
- 20. Choi- williams distribution Signal time time Talk @WDSC 201820
- 21. How to solve this restricted resolution problem ? An adaptive technique is needed, which should alter its TF tiling to any resolution ! “ Adaptive Signal Decompistion” “A Pursuit in search for the best TF localized match” Talk @WDSC 201821
- 22. Redundant dictionary of TF functions for all values of translation, scaling and modulation Signal projection over the TF dictionary After ‘m‘ iterations Original signal ‘m’ TF functions + Signal residue Matching Pursuit Talk @WDSC 201822
- 23. In MP the scaling parameter is independent of frequency time Scaling freq This approach is much more adaptive with no restrictions on windowing patterns . s s Still the dictionary selection limits complete modeling of the signal Matching Pursuit Talk @WDSC 201823
- 24. Signal time What did we achieve ? Matching pursuit time Talk @WDSC 201824
- 26. Feature Extraction 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 time time frequency 20 40 60 80 100 120 50 100 150 200 250 300 350 400 450 500 0 0.5 1 1.5 2 0 1 2 -1 -0.5 0 0.5 1 1 2 Signal Representation • Adaptive • Representative • Discriminative Talk @WDSC 201826
- 27. 20 40 60 80 100 120 140 160 180 50 100 150 200 250 300 350 400 450 500 Adaptive Segmentation 0.02 0.04 0.06 50 100 150 200 250 300 350 400 450 500 2 4 6 x 10 -3 50 100 150 200 250 300 350 400 450 500 0.02 0.04 0.06 50 100 150 200 250 300 350 400 450 500 188512´V 3512´W NrH ´ » ´ 20 40 60 80 100 120 140 160 180 50 100 150 200 250 300 350 400 450 500 20 40 60 80 100 120 140 160 180 50 100 150 200 250 300 350 400 450 500 20 40 60 80 100 120 140 160 180 0 5 10 15 20 40 60 80 100 120 140 160 180 0 10 20 20 40 60 80 100 120 140 160 180 0 5 10 15 20 40 60 80 100 120 140 160 180 50 100 150 200 250 300 350 400 450 500 NrrMNM ´´´ ´» HWV Talk @WDSC 201827
- 28. Time (s)(d) Frequency(kHz) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 Time (s)(c) Frequency(kHz) 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 0 2 4 TFM Decomposition 0.5 1 1.5 -2 -1 0 1 2 Time (s) (a) Time (s) (b) Frequency(kHz) 0 0.5 1 1.5 0 1 2 3 4 W HTalk @WDSC 201828
- 31. TF Features — Joint TF Moments — Sparsity — Discontinuity — Coherency Classifier W H )(tx V Matrix Decomposition TFD Feature Extraction Talk @WDSC 201831
- 32. TF Feature Example — 3-second audio signal — TFD with 512 and 2048 frequency and time bins. — TFM: — TFM decomposition: — TFM Features: 2048512´V 15512´W 204815´H { }MPDDSSMOMO whwhwh ,,,,,, )1()1( Talk @WDSC 201832
- 33. Extension to Multi-channel Signals Ankle Ankle Trunk Time (s) Frequency (Hz) Sensors Wrist TrunkWrist Talk @WDSC 201833
- 34. Tensor Decomposition — Canonical Polyadic Decomposition (CPD) — Decomposition to rank-1 components — Rank-1 decompositions are the 𝒂𝒊, 𝒃𝒊 and 𝒄𝒊 where 𝑖 ∈ 1,2,3, … 𝑹; 𝑨, 𝑩 and 𝑪 are Factor Matrices Talk @WDSC 2018 T +⋯+ = 𝒂 𝟏 𝒃 𝟏 𝒃 𝑹 𝒂 𝑹 𝒄 𝟏 𝒄 𝑹 𝑨 𝑩 𝑪 ≈ 34
- 35. Some of the Research Projects @ BSIA Lab 35 Talk @WDSC 2018
- 36. Current BSIA Lab Projects — Atrial Fibrillation (AF) • Catheter guidance in AF ablation • AF detection from surface ECG • Risk detection in external DC cardioversion • Scar quantification in endocardium duringAF • Scar quantification in LA from delayed-enhance MRI — Sudden Cardiac Death (SCD) • Risk stratification of SCD • 3D distribution of stress inducedTWA — Artifact Rejection of EEGAuditory Evoked Potential — Quantification of Hypsarrhythmia in Infantile Spasms • Temporal and Spectral quantification of EEG • Epileptic discharge detection from EEG — Parkinson’s Disease — Automated detection of medication off vs. dyskinesia from ambulatory gate data in PD patients — Structure-constrained Basis Pursuit For Compressed Sensing — Real-time Speech Recognition Using Compressively Sensed Samples — TF feature extraction using Dictionary learning Talk @WDSC 201836
- 37. Atrial Fibrillation — Among these heart problems, atrial fibrillation (AF) is the most common heart rhythm disorder that affects millions of people, accounting for frequent hospitalizations, increased risks of stroke, heart failure and mortality. Talk @WDSC 201837
- 38. • QRS Complex • Represents contraction of the ventricles • T Wave • Represents relaxation of the ventricles Electrocardiogram • PWave • Represents contraction of the atria Talk @WDSC 201838
- 39. Fibrillation — Ventricular tachycardia (VT) orVentricular fibrillation (VF) — Atrial fibrillation (AF) Talk @WDSC 201839
- 40. Ventricular Fibrillation Talk @WDSC 201840
- 41. Atrial Fibrillation — Disorganized contraction of atrial tissue Sinus Rhythm ECG AF ECG Characteristics: FibrillatoryWaves Irregular RR Interval Talk @WDSC 201841
- 42. Radio frequency ablation (RFA) AF Treatment Talk @WDSC 201842
- 43. AF Projects — Identifying Atrial Fibrillation Episodes from ECG recordings. — Developing a New Computer-aided Clinical Decision Support System For Prediction of Successful Post- cardioversion PatientsWith Persistent Atrial Fibrillation. Talk @WDSC 201843
- 44. Computer-aided Clinical Decision Support System For Prediction of Successful Post-cardioversion Patients With Persistent Atrial Fibrillation Talk @WDSC 201844 Overall outline of the study. Standard supervised learning approach is applied consisting of a feature extraction step followed by a classification step. Leave- one-out cross validation is used to evaluate the predictive power of our technique.
- 45. Atrial Activity Extraction Talk @WDSC 201845
- 46. Feature Extraction Talk @WDSC 201846 x(t) = bm AWm,Sm,TM( )(t)+ Rx M m=1 M ∑
- 47. Talk @WDSC 2018 Sym$Coif$$ A.$Occupancy$Matrix$ C.$Atrial$Ac6vity$ B.$Occupancy$Distribu6on$ Sym$Coif$$ A.$Occupancy$Matrix$ C.$Atrial$Ac6vity$ B.$Occupancy$Distribu6on$ M. Sterling, D. Huang and B. Ghoraani, “Developing a New Computer-aided Clinical Decision Support System For Prediction of Successful Post-cardioversion PatientsWith Persistent Atrial Fibrillation", submitted to the Computational and Mathematical Methods in Medicine Journal, 2015. Successful Cardioversion Unsuccessful Cardioversion 47
- 48. Results Talk @WDSC 201848 Dataset: 40 persistent AF patients who had a successful external DCE cardioversion therapy. Prior to cardioversion, a10-minute 12-lead ECG was recorded. Twenty patients had maintained SR (AF-Free) after 2-week follow-up and 20 had a relapse of AF (AF-Relapse). Receiver operating characteristic analysis using leave-one-out cross validation. The AUC is 0.97 and the best sensitivity and specificity are 100% and 95%, respectively.
- 49. EEG Auditory Evoked Potentials Talk @WDSC 201849 Latency – neural conduction/processing time Amplitude – strength/magnitude of response
- 50. EEG Auditory Evoked Potentials (Cont.) — Cochlear Implant Artifact Reduction From Auditory Evoked Potentials in EEG Recordings. Talk @WDSC 201850
- 51. EEG Auditory Evoked Potentials (Cont.) — Cochlear Implant Artifact Reduction From Auditory Evoked Potentials in EEG Recordings. Talk @WDSC 201851
- 52. Talk @WDSC 2018 D. Sinkiewicz, L. Friesen and B. Ghoraani,“Analysis of Cochlear Implant Artifact RemovalTechniques Using the ContinuousWavelet Transform”, in the proceedings of the 36th Annual International IEEE EMBS Conference, Pages: 5482-5485, September, 2014. D. Sinkiewicz, L. Friesen, and B. Ghoraani,“A Novel Method for Extraction of Neural Response from Cochlear Implant Auditory Evoked Potentials”, MEP Journal, December 2016. 52
- 53. Parkinson’s Disease — Parkinson disease (PD) is a chronic progressive neurological disorder that will affect over half a million Americans by 2030. — A chronic progressive neurological disorder that leads to different motor and non-motor disabilities. — Bradykinesia: impairment of the power of voluntary movements that leads to slow movements. — Tremor: consists of hand and leg tremor while resting. — Dyskinesias: Uncontrolled muscles movements that is induced by levodopa with long-term medication. Talk @WDSC 201853
- 54. 54 OFF and ON States for One ofThe Patients Dataset Talk @WDSC 2018
- 55. Translating Multidimensional Sensor Data to Guide Self-management for Patients with Parkinson Disease Talk @WDSC 201855 1 2 Deep Brain Stimulator/ Drug Infusion Pump Body-worn Inertial Sensors Developed Algorithm Before medication (OFF) UPDRS-49; mAIMS=5 1 hour after medication (partial ON) UPDRS=30; mAIMS=7 2 hours after medication (ON with dyskinesia) UPDRS=17; mAIMS=16 3 hours after medication (OFF again) UPDRS=42; mAIMS=4 ON OFF 40 sec Classification Certainty RTI Status Report Existing Devices/Algorithms Motor Impairment Report Clinical Visits For Physician Interpretation 3 a b c d Clinically Actionable Information Front Back
- 56. Dataset (Cont.) Talk @WDSC 201856 KinetiSense motion sensor unit
- 57. Detection of Response to Intervention Talk @WDSC 201857
- 58. Classification Report Talk @WDSC 201858 M. Hssayeni, M. Burack , J.Adams, B. Ghoraani “Personalized Assessment of Response to Therapeutic Intervention in Individuals with Parkinson's Disease”, under preparation to the IEEETransactions on Biomedical and Health Informatics.
- 59. Infantile Spasms — Infantile Spasms Talk @WDSC 201859
- 60. Infantile Spasms — Quantification of Hypsarrhythmia in Infantile Spasms Talk @WDSC 2018 !"#$!(%$&)! $$%! ! &'! ! (! )! *+,-.,/01!(23)! ! *+,-.,/01!(23)! ! 456,!(7,0)! !"#$!(%$&)! a)! b)! c)! d)! e)! Traitruengsakuly S, Seltzerz LE, Paciorkowskiz AR, Ghoraani B. Automatic Localization of Epileptic Spikes in EEGs of Children with Infantile Spasms, 37th IEEE Engineering In Medicine and Biology Proceedings: 6194- 6197.August 2015, Milan, Italy.60
- 61. TF Feature Extraction Talk @WDSC 2018 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 False Positive Rate (1−specificity) TruePositiveRate(sensitivity) (b) ROC based on F2 AUC = 98.56% ( = 10) AUC = 93.14% ( = 0.5) Classification Performance Using SupportVector Machine S.Traitruengsakul, L. E. Seltzer,A. R. Paciorkowski, and B. Ghoraani, “Developing A Novel Epileptic Discharge LocalizationAlgorithm for Electroencephalogram Infantile Spasms During Hypsarrhythmia”, Medical & Biology Engineering and Computing Journal, January 2017. 61
- 62. Some of the members of BSIA Lab Talk @WDSC 201862
- 63. q Dr. Moussa Mansour, Massachusetts General Hospital q Dr. Jamie Adams, University of Rochester Medical Center q Dr. Michelle Burack, University of Rochester Medical Center q Dr. David Huang, University of Rochester Medical Center q Dr. Lendra Friesen, University of Connecticut q Dr. Laurie Seltzer, Neurology, University of Rochester Medical Center q Dr. Elizabeth M. Cherry, School of Mathematical Sciences, RIT q Dr. Arkady M. Pertsov, SUNY Upstate Medical University School of Mathematical Sciences RIT q National Institutes of Health (NIH) - 1R15HL127663-01 q NSF Advance - HRD-1209115. q I-SENSE Seed Funding 63 Talk @WDSC 2018