identification of sudden cardiac death using spectral ......searching method based on the detected...

Identification Of Sudden Cardiac Death UsingSpectral Domain Analysis Of Electrocardiogram

Saurabh Rastogi, SCE

Supervisors

Assoc Prof Lin Feng, SCE

Asst Prof Mark Chavez, ADM23/02/2010

Student

Seminar Two

Introduction & ObjectivesProblems with the current ECG systemsProposed MPSoC ECG MonitorWireless Data TransferSpectral Domain AnalysisPlanned Research

A leading cause of human deaths is heart diseases.

According to the World Health Report, 29.2% of total global deaths aredue to Cardiovascular Diseases ( CVD).

Many of these diseases are preventable by proper monitoring.

ECG - regular rhythmic electrical signal generated by the heart.

Recorded electrical pattern - Electrocardiogram (ECG).

Instrument for recording electrocardiogram –Electrocardiograph (ECG).

Example of a typical ECG Signal. Clear depiction of complete heart activity.

Typical Values for an ECGsignal.

ECG bandwidth for generaldiagnosis applications -0.05Hz and 100Hz.

ECG bandwidth for patientmonitoring or healthcarepurposes - 0.05Hz and 35Hz.

Each lead measures different potential. 6 chest electrodes V1 to V6 and LA, RA and LL electrodes. All electrodes - body surface electrodes.

For the measurement of Heartactivity 12 leads are used.

Under life critical conditionsECG needs to be constantlymonitored for a patient.

For mobile patients or duringhome care or emergencymonitoring.

To present an ambient, home based health and wellness measurementand monitoring architecture.

Especially targeting the elderly and chronic patients.

Development of a wireless monitoring system capable of acquiring 12leads of ECG.

Transmission of ECG to a consultation unit.

Development of a lightweight unobtrusive, belt-like wearable device.

To enable patients to be monitored daily and at the same time allowthem to perform their regular daily activities.

To design an intelligent, energy efficient, reusable and computationallycapable MPSoC system.

Intentions to improve the old measurement techniques like the classicalPan-Tompkins algorithm.

Attempt to make a more flexible system like measurement of otherbiopotentials by the same MPSoC with only electrodes being replaced bythe proper biopotential electrodes.

Real time wireless monitoring of the patients with suspected cardiacarrhythmias.

Measurement of some other intermediate signal variables like PQ, RS orST time intervals.


Table of Contents

•ECG data is always huge due to repetition and long monitoringdurations.

•High computational demands arise for this huge chunk of ECG Data.

•Transmitted and analyzed to provide large scale analysis and remote,real time computation at the patient’s location.

•The wireless connection must be a 100% functional always ON.

•Life threatening to loose even a few heartbeats.

•Should be sampled at relatively high sampling frequencies fortransmission.

The expert visualization upstairs

The normal visualization downstairs

Wearable Measurement andTelemetry Technology

Medical technologies in the past focused predominantly on earlydetection and treatment of disorders.

Recent developments aim more on constant monitoring of one’s healthas to facilitate sustained wellness at all times.

Fast rise of ubiquitous technologies and emergence of ubiquitoushealthcare solutions.

Ubiquitous technologies have resulted in active and wide-rangingresearch into constant health monitoring-based healthcare methods.

These methods free people from the space and time restrictions ofconventional institution-oriented medical treatments.

State-of-the-art biomedical equipments lack the ability to provide large-scale analysis and remote, real-time computation at the patient’slocation.

Growing demand for telemedicine services.

Reduction of the nurse’s and doctor’s work-load.

Improvement in the quality of care for patients in both emergency

situations and long-term treatments.

Old bulky medical monitors still used in ambulances.

Cost reduction.

Need of user friendly devices.

Need for devices that patient can operate at his own.

Wearable devices need of the day.

Using the new advanced technologies for healthcare applications.

Need for monitoring more than one ECG parameters simultaneously.

Possibility of measurement of more than one physiological variables likepulse oxymetry and temperature by just one chip device.

Need of measurement from more than one lead simultaneously like 3leads at a time.

Prediction about the diseases like sudden cardiac death.

Enabling doctors to detect chances of myocardial infarction.

Possibility to analyze and predict the susceptibility to cardiac arrest.

Advances in embedded systemsand multiprocessor systems.

Now possible to developwearable multi processor basedsingle-chip solutions (MPSoC).

Able to perform all operationslike measurement, parallelprocessing and analysis of thedata and necessary action in caseof cardiac problems.

Able to maintain a perfect transmissionwith strict control over the variables to betransmitted.

Able to decide which component of ECGsignal is to be transmitted.

Also able to trigger a demand modepacemaker in case when heart looses itsability to generate pulses

ECG reflects:▪ Atrium activity▪ SA (Sino Atrial) Node Potentials▪ Ventricular activity▪ AV (Atrio Ventricular) Node Potentials

We are trying to measure from ECG:▪ Blood Volume in circulation▪ Possibility of Heart Disorders (Cardiac Diseases) in advance

Other than this:▪ MPSOC System▪ Communication Improvement

TMS320C32 micro-controller.

TX-2 for transmitter module, AD620 forinstrumentation amplifier.

40Hz high pass and 60Hz notch filter usingOP amp.

Three channels, each channel is selectedby manual operating.

Sampling rate 250Hz and A/D resolution is16bit.

Available distance of transmitter module is70m in building and 300m in open ground.

Communication with PC performedthrough serial port.

Receiver module connected in PCconsisted of RX-2 receiver module and RS-232 port.

Using Slope Detection Method Slope change represented by + or - sign

After the detection of QRS complex, P and T wave detected using similarsearching method based on the detected QRS complex.

PQ segment level is the beat baseline. Therefore the detection of P wave is important but extremely problematic. It is a

problem which is not resolved yet, and many researches is being worked. Detection of PQ segment level by finding the minimum slope point in the middle

part of P and Q wave.

Transmit only secure remote-alarm signals.

Reports on the results of the analysis.

Result reports much smaller in size (a few bytes) than the ECG data (inMegabytes).

Can be retransmitted until reception acknowledgement in case oftransmission failure.

Saved on an off-chip memory for every analyzed ECG data chunk.

ObjectivesProblems with the current ECG systemsProposed MPSoC ECG MonitorWireless Data TransferSpectral Domain AnalysisPlanned Research

24 hour long ECG data difficult to diagnose.

Transforms like Laplace and Fourier provide integration of any data overa long period of time.

Fast Fourier Transform (FFT) on QRS complex can be used to extractinformation from the ECG signals.

Signals first normalized in frequency domain.

Plotting of the parameters using two dimensional plots to showsignificant difference in the desired and pathological parameters.

Will provide the basis of difference of a normal signal to the ECG signal ofthe patient to suffer a cardiac arrest.

In this way, instead of waiting for over 24 hrs, 4-5 min. of ECG of anypatient is enough to detect possibility of Sudden Cardiac Death.

Possibility to analyze and predict the susceptibility to cardiac arrest.

Wavelet transforms can be used to distinguish between various ECGcomponents.

The onset of ventricular tachycardia(annotated by v).

Tachycardia - fast heart rhythm, thatoriginates in one of the ventricles ofthe heart.

Frequency - the rate of change. Change in activity of heart means that

the significant spectrum of the signalshould be affected as well.

Fourier Transform for the significantlobe of spectrum, and parametersplotting will give us the results.

The software interface will provide the following details:

Real-time analysis and data extraction of ECG parameters

PQRST amplitudes

Time intervals such as RR, PR, JT, QT and QTc

Analysis of ECG parameters in real-time or offline

Automated detection and averaging of ECG cycles

Automated tabulation and data extraction of ECG parameters

Automated real-time or offline ECG Plots

Graphical QT vs RR, QT vs Time & RR vs Time plots

It will also provide:▪ Detection and analysis of R waves & RR interval variation in ECG real-time or

offline recordings

▪ Inclusion or exclusion of ectopic beats from analysis

▪ Addition of R waves or removal short artifacts from analysis

▪ Exports data analysis

▪ RR Intervals, Spectrum Intervals & Report

▪ Automated HRV Analysis Windows

▪ Spectrum Plots, Period Histogram & Delta Histogram

▪ Cardiac Axis Extension: Automation of the calculation of frontal plane ECGs andvector cardiograms and display of the instantaneous cardiac vector.

▪ SAECG (signal averaged ECG) Extension: Calculation of the average cycle of ECGsignals and automatically identification specification waveforms and cardiacindices.

▪ Peak Parameters Extension: Determination of a number of parameters for anindividual peak

This software will be ideal for analyzing cardiac action potentials.

Can be extended to Mobile real-time processing

Finalized the requirements for the measurements.

Use of digital signal processors.

Blue Silver/Silver Chloride electrodes.

Complete 12 lead ECG measurement.

Measurement of heart rhythmic period and all the peaks P,Q,R,S,T, and Uand their inter peak intervals.

Help of some simulation environment software like MPSIM.

Use of other transforms like Fast Fourier Transform or Discrete FourierTransform for Spectral Domain Analysis

More Detailed and Accurate Measurement of Heart

▪ 12 Leads

▪9 sensors

▪Microprocessor Texas Instruments - MSP430F1611IRTDT

▪RF Solutions Alpha-TX433s Module, Transmitter, TX433s

▪RF Solutions Alpha-RX433s Module, Receiver, RX433s

▪ Integrated Silicon Solution IS42S16400D-7TLI SDRAM, 64m, 3.3v,143mhz

▪Burr-brown INA118P Amp Instrumentation, DIP8

▪MPSIM

MPSOC System

Derivatives of ECG

▪First Order vs Second Order

▪Discrete vs Continuous Correlation with ECG Signal (Auto vs ?) Percentage of Signal to be Correlated ( 25%, 30, 40% or ?) Shift or Time Delay for Correlation ( 25%, 30, 40% or ?) Need vs no need to correlate the whole signal with itself? An algorithm for finding the peaks Comparison with

▪Eyeballing Techniques

▪Pan-Tompkins Algorithm

Key Parts: Extended sensor front-end having actual signal acquisition devices.

Analog to digital sensor interface▪ Also responsible for amplifying and filtering the acquired biosignals and driving

them to the A/D converter.

Processor core.

Wireless module responsible for the transmission of the biosignals.

Key points

▪ To breakup functions into parallel operations

▪ Thus speeding-up execution

▪ Allowing individual cores to run at a lower frequency with respect totraditional monolithic processor cores

Using Master Slave Components.

Transmission directly from the chip itself.

Wheel MountedElectrocardiography

Armrest-mounted electrodes.

The system can obtain preciseECG only when driver grips rightpositions of wheel

May provide wrong warningswhen the user holds unexpectedpart of wheel.

Blood Pressure Meter on Toilet Seat

The system requires measuringspatial relation between heartand thigh.

Sensor Fusion

Computerized Serial Comparisonof ECGs

Triggering pacemaker

Simulating and transferringenergy of defibrillators

identification of sudden cardiac death using spectral ......searching method based on the detected...

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