REAL-TIME CARDIAC ARRHYTHMIAS MONITORING FOR PERVASIVE HEALTH CARE RTLAB YuJin Park

Pervasive Health Care (PHC) New health care model that enables patient mobility Continuous health monitoring Timely detection of anomalies Pervasive Cardiac Care(PCC) System

Overview of PCC System Real-Time Pervasive Cardiac Monitoring Service Remote Health Monitoring Adaptive Communication Mechanism QRS detection : AED Algorithm

PCC System Architecture Wireless ECG Sensor Outdoor Care Home Care Clinic Care Local Access Server Mobile Phone PDA Laptop PC Access Point Textual Message Internet Real-Time ECG Local Access Server DB Server APP Server Web Server Fire wall Fire wall Internet LAN Local Access Server Mobile Phone PDA Laptop Diagnosis Terminal

Wireless ECG Sensor 4-lead ECG HOLTER CMMR(min) : 120 dB Programmable sample frequency more than 500 Hz 100 Hz ~ 2000 Hz Analogue to digital converter (ADC) : 12 bit TI MSP430

Local/Remote access Server Local access server Reliable data transmission service between local server and remote server ECG signals compression algorithm Remote access server Database service Medical record storage Web service Remote surveillance server Interactive visualization graphical user interface Alarm service

PCC Operation Modes Real-time continuous ECG signal Remote real-time display and diagnosis Not fit to monitor a large number of patient : limitations of network bandwidth ECG signal sequence Cardiac arrhythmia event is detected -> Sending a sequence of ECG signal Textual emergency message Cardiac arrhythmia event is detected -> Sending a short textual emergency message Diagnosis report Periodical report mode

Key Technologies of PCC System Lossless ECG Signal Compression Adaptive Communication Mechanism PCC data frame PCC communication mechanisms PCC system control Signal retransmission mechanism Data competition mechanism AED Algorithm Signal preprocessing and conditioning QRS detection

Lossless ECG Signal Compression The high sampling frequency(500Hz) is necessary to guarantee the accuracy of the AED algorithm But, ECG signals with the low sampling rate(often 128 Hz) are acceptable for the remote visualization system. -> Sub-sampling 500Hz -> 128Hz Data format of signal compression algorithm +/-typedata bit2 bit 1 or 5 or 9 or 13 bit

Adaptive Communication Mechanism UDP protocol -> Reliable data transmission mechanism (application layer) is needed Real-time data transmission Control data, ECG signal, image data PCC communication mechanism PCC system control Signal retransmission mechanism Data competition mechanism

PCC data frame type Sequence number Signal number QRS number Sampling frequency QRS result…… QRS resultECG Signal type Sequence number Images typeControl codeControl information ECG frame Image frame Control frame QRS position QRS length QRS state

PCC Communication Mechanisms PCC system control REQ_Connect / ACK_Connect, REQ_Terminate / ACK_Terminate REQ_Configure / ACK_Configure, REQ_Restra ACK_5Frames, ACK_Image Signal retransmission Hold Queue(local access server) / Wait Queue(remote surveillance server) Sequence number Data competition mechanism Control frame (Highest priority) ECG frame(Higher than Image frame) Image frame(lowest priority)

Automatic ECG Diagnosis(AED) Algorithm Implementation Issues Interference and noise Baseline Drift, electrical noises and muscle tremor interferences Resource consumption Signal preprocessing and conditioning Adaptive filter QRS complex detection Diagnostic segment window(DSW) Self-adaptive threshold Geometric analysis method

Signal preprocessing and conditioning Classic filters : notch filter, low-pass filter, and high-pass filter Adaptive filter Aecg(0) = R(0) 0< α <1, t = 1...N Aecg(t) = α * Aecg(t-1) + (1 - α)*R(t) a: R(t) = raw signal, b: RC(t) when filtering the R(t) by traditional filters c: A(t) when filtering the R(t) by adaptive filter d: A(t) when filtering the RC(t) by adaptive filter

QRS complex detection Diagnostic segment window Self-adaptive threshold QRS location : State transition recognition Feature extraction : geometric analysis method PT : Positive threshold NT : Negative threshold