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Respiratory monitoring

Respiratory monitoring. DSP II – Final presentation. Some background…. Some background…. Project integrated in master thesis: “ Textile-integrated data-acquisition system” Prof. Dr. Ir. R. Puers Optional course in Biomedical technology

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Respiratory monitoring

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  1. Respiratorymonitoring DSP II – Finalpresentation Hans De Clercq & Rogier Corthout

  2. Some background… Hans De Clercq & Rogier Corthout

  3. Some background… • Project integrated in master thesis: • “Textile-integrateddata-acquisition system” • Prof. Dr. Ir. R. Puers • Optionalcourse in Biomedicaltechnology • Applicationformonitoringbreathing disorders (e.g. SIDS) during sleep forbabies • Accelerometer-based design measuringmovementsduringbreathing • Startedfrom scratch  shapedourownDSP-project… Hans De Clercq & Rogier Corthout

  4. Some background… • Non-uniform chest/abdomen expansion measurementvariation of inclinationusingaccelerometersplacedsidewayson the chest/abdomen • XY-plane  modulus & angle Respiration g Hans De Clercq & Rogier Corthout

  5. Rawsignalvs gold standard Time (s) Time (samples @ 20 Hz) Accelerometer (angle) Spirometer Hans De Clercq & Rogier Corthout

  6. Objectives project DSP II Hans De Clercq & Rogier Corthout

  7. Block scheme Accelerometersignal Offset & noisecancellation Adaptive BPF Spirometer signal Nishimuraor N-points FFT Extraction dominant frequency RMS Decisionmaking (fuzzylogic) Breathingrate Breathingpattern Phase shift Errorrate Hans De Clercq & Rogier Corthout

  8. Signalconditioning • Noisecancellationfromlow-costaccelerometersoutsideusefulbreathing BW • Noiselimitsaccelerometerresolution: 1000μg/sqrt(Hz) • Analog filtering (simple RC)  anti-aliasing: sample frequency ADC ~ 10 Hz • Offset cancellation • Simple, butsteephigh-passIIR-filtering (cut-off ~ 0.01 Hz) • E.g. third order Chebychev • Normalizationwithreferencesignal Hans De Clercq & Rogier Corthout

  9. Adaptive filtering • Objectives: • Cancel the noiseinside the usefulbreathing BW, obtainingonly the “sine wave”-likesignal • Obtain the dominant frequency as a parameter for the pattern detector • Comparisonwith gold standard… Hans De Clercq & Rogier Corthout

  10. Adaptive filtering • Twomethodsexplored: • Nishimuramethod • STFT-method • Algorithmstestedon 3 different signals: • Sine wavewithGaussiannoise • 4 different frequencies/amplitudes • SNR < 2dB • Spirometermeasurement (gold standard) • Accelerometermeasurement (test signal) • … and thosetwocombined … Hans De Clercq & Rogier Corthout

  11. Nishimuramethod • Concept: adaptiveband-pass filter (IIR!) • Filter is tuned in real-time to maximize the output of the bandpass filter: • Basically, input is a “sine wave” with a variablefrequencycovered in noise… • Bandpass filtering for maximum output = lookingfor the frequency band with maximum power! Hans De Clercq & Rogier Corthout

  12. Input signal Time (s) Frequency (Hertz) Hans De Clercq & Rogier Corthout

  13. Nishimuramethod [2] • 2nd order IIR-filter: • α0determines the selectivity of the filter… Increasingα0 Hans De Clercq & Rogier Corthout

  14. Nishimuramethod [2] • 2nd order IIR-filter: • α0determines the selectivity of the filter • α1determines the center frequency of the pass-band: • α1 is iterativelytuned in real-time to maximize the output of the bandpass filter… Hans De Clercq & Rogier Corthout

  15. Nishimuramethod [3] • Iterative updating schemeforα1: • Gradientalgorithmtowards maximum output power • μdetermines the convergence speed, and is heavilyrelatedwithstability… μ Δ + x HB(z) G(z) u(k) y(k) Hans De Clercq & Rogier Corthout

  16. Nishimuramethod: results Hans De Clercq & Rogier Corthout

  17. Nishimuramethod: results • Update filter foreverynew input value • Computational efficiency  real-timeimplementationpossible μ Δ + x HB(z) G(z) u(k) y(k) Hans De Clercq & Rogier Corthout

  18. Nishimuramethod: results Hans De Clercq & Rogier Corthout

  19. Nishimuramethod: results • Limitedconvergence speed ifvaryingfrequency… • E.g. onsine wave signalwithvaryingfrequency and amplitude… Detected dominant frequency (Hz) Amplitude input signal Time (s) Time (s) Hans De Clercq & Rogier Corthout

  20. Nishimuramethod: results Hans De Clercq & Rogier Corthout

  21. Nishimuramethod: conclusion • Works “fine” onartificialsine wave signal… • Biomedicalsignalsaren’tdeterministic at all  limitedconvergence speed = bottleneck in detecting the dominant frequency • E.g. onsimple spirometer signal… Detected dominant frequency (Hz) Amplitude input signal Estimation of realfrequency Time (s) Time (s) Unstableworking regime… Hans De Clercq & Rogier Corthout

  22. STFT-method • After paper Hung & Bonnet… • Dividesignal in 51.2s (1024 samples) segmentswith 1/6th overlap betweenwindowsforedgecontinuity • Assumption: continuity over time window • Calculatespectrum and detectmaximum freq. of accelerometersignalf0 ∈ 0.1-1Hz • Filter the signal in pass-band:using a 4th order Butterworth filter… Hans De Clercq & Rogier Corthout

  23. STFT-method [2] Hans De Clercq & Rogier Corthout

  24. STFT-method [3] • E.g. on spirometersignal (trivialonsine wave) frequency time lesssmearing out of low breathingfrequencies Hans De Clercq & Rogier Corthout

  25. STFT-method [4] • E.g. appliedon spirometer signal… • Limitedfrequencyresolution • Butstill more reliablethanNishimurafor long term monitoring… Filteredsignal Detectedfrequency Detected dominant frequency (Hz) Amplitude signal Time (s) Time (s) Hans De Clercq & Rogier Corthout

  26. Comparisonusingaccelerometers • Similarresults: • STFT more reliable and intuitive • Nishimura hard real-timepotential… •  STFT seems to be the wisestchoiceforlong-termapplications!  Original signal Amplitude signal  Filtered (STFT method) Detected dominant frequency (Hz) Amplitude signal Time (s) Time (s)  Filtered (Nishimura) Detected dominant frequency (Hz) Amplitude signal Time (s) Time (s) Hans De Clercq & Rogier Corthout

  27. Comparison of performance onspiro & accelero Accelerometersignal Spirometer signal FilteredsignalusingSTFT DetectedfrequencyusingSTFT Consistent detectiononbothsignals FilteredsignalusingNishimura DetectedfrequencyusingNishimura Inconsistent detectiondue to unstablebehaviour… Hans De Clercq & Rogier Corthout

  28. Information processing • Dominant frequency (supra) • RMS-value of BP-filteredsignals • Phase shift with gold standard • Normallyconsistentlysmallduringquietbreathing [GOLLEE] • Exception: transient/fastmovement, e.g. forcedexpiration (coughing) • Implemented, butnotyetusedforthisapplication… Hans De Clercq & Rogier Corthout

  29. Decisionmaking • Determination of breathingpatternfromprocessedinformation • Clustering of sampledvaluesfor all parameters usingfuzzytechniques  Advantage: clustering techniquescanrevealstructures in data without relyingonassumptionscommon to conventionalstatisticalmethods, such as the underlyingstatistical distribution Hans De Clercq & Rogier Corthout

  30. Decisionmaking [2] • Data set to beclustered: • Every time sample has itsmembershipfunctionμik: Cluster variables 1…n Time samples k=1…N Hans De Clercq & Rogier Corthout

  31. Decisionmaking [3] • Objective is to minimize the fuzzyc-meansfunctional: • Implementation of Gustaf-Kesselsonalgorithm in Matlab • Complex iterativealgebraicproblem, furthermathematical details omitted… • Only input parameter: expectednumber of clusters c Membershipfunction of sample k to cluster i Distance of sample k to center of cluster i Hans De Clercq & Rogier Corthout

  32. Decisionmaking [4] • Output of algorithm: • Partition matrix with allmembershipfunctionsU • Cluster prototype matrix V with cluster centers • Cluster covariance matrix F Cluster number 1…c Time samples k=1…N Cluster number 1…c Cluster variables k=1…n Cluster number 1…c Cluster variables k=1…n Hans De Clercq & Rogier Corthout

  33. Results and discussion • Detectionmean amplitude or average power per breathingpattern • Applicationonartificial test signalsobtain low errorratesduring clustering: • ε < 15% usingNishimura • ε < 5% using STFT-BPF! Hans De Clercq & Rogier Corthout

  34. Results and discussion [2] Long-termmeasurementusingonlyaccelerometers… Cluster number boxplot dom. freq. point cloud Heavy breathing Quietbreathing RMS STFT-BPF output boxplot RMS No breathing Cluster number Dominant frequency (STFT) Hans De Clercq & Rogier Corthout

  35. Futurework & potential… • Gustaf-Kesselson starts with random cluster centers  usestatisticalinformation to initialize clustering more reliably • Expandfrequencydetection & fuzzy clustering to othersignals (ECG, oximetry,…) in order to: • Obtain redundant measurements  increasereliability • Examine the correlationbetweenthosesignalsduringapnoea Hans De Clercq & Rogier Corthout

  36. Respiratorymonitoring DSP II – Intermediatepresentation Hans De Clercq & Rogier Corthout

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