A Mechanism for Data Quality Estimation of On-Body Cardiac Sensor Networks Sunghoon Ivan Lee *...

Copyright: UCLA Wireless Health Institute

A Mechanism for Data Quality Estimation of On-Body Cardiac Sensor Networks

Sunghoon Ivan Lee*

Charles Ling*

Ani Nahapetian*†

Majid Sarrafzadeh*

*Computer Science, UCLA

†Computer Science, CSUN

Wireless Health Institute (WHI) - UCLA

• Campus Community– School of Medicine– Medical Center– School of Engineering– School of Nursing – School of Public Health– College of Letters & Science– Anderson School of Management

• Unique approach– End-to-end integration from

sensing to medical informatics to call center

– Develop and verify new healthcare methods and services

– Establish standards for efficacy, reliability, interoperability, and security

3

Cardiac-Monitoring Sensors

• BANs or PANs involve various types of wearable and non-invasive sensors on body– Cardiac Sensor, EMG, EEG, Glucose sensor, Motion sensor, etc.

• Cardiac monitoring sensors are the most common and widely used sensors– For example, in the survey on wearable sensor-based systems for

health monitoring [17], 90% of the introduced systems involve cardiac monitoring sensors.


4

Problems with Wearable Cardiac Sensors

• Sensors often suffer from high level of noise• Types of possible noise include

i. Channel noise produced by human body [11, 19]ii. Noise caused by environments [5]iii. Noise from loose physical contact of the sensor node to the

human body

• Motion artifacts have the most significant degradation effect on the quality of sensor data, especially when the subject is highly mobile (e.g., at-home remote health care applications).

5

Motivation Example

• This signal was obtained from one of our participants wearing an on-body ECG sensor (Alive Heart Monitor [1]) at wrist.


6

Motivation Example

• Therefore, multiple sensors are often mounted at different parts of the body to provide higher data quality in a highly mobile environment.


7

Data Quality Information

• For continuous and pervasive health monitoring environment, data quality information can be very useful– Automatically detecting sensors producing clean data

• In the field of medicine, extra manpower is required to manually filter out polluted data [20]

– Improving continuous monitoring of patients– The data quality information can be also used to

optimize the resources the pervasive system.• This is a very important issue for pervasive systems


8

Objective

• Our work introduces a mechanism for data quality estimation of a BAN composed of cardiac sensors specifically considering resource scarceness


9

Considered BAN Structure

• The mechanism considers a BAN structure that all cardiac sensors transmit data to a single aggregator


Sensor node #1

Sensor node #2Aggregator

Body Sensor Network

10

Summary of the Proposed Mechanism

• STEP 1: Local Data Quality Estimation

• Individual sensors filter out most of the normal events and recognize any abnormal events (motion artifact noise + health hazardous events)


Sensor node #1


Body Sensor Network

11

Summary of the Proposed Mechanism

• STEP 2: Global Data Quality Estimation

• Aggregates information about the local data quality and fuses the information to estimate the data quality of the overall BAN.


Sensor node #1


Body Sensor Network

12

Local Data Quality Estimation

• Detect any abnormal events in data generated from a single cardiac sensor

• This method is based on a well known fact that amplitude of cardiac signal and inter-pulse interval (IPI) variability are effect bedside measurements to detect any abnormal events [4], [8], [9].


13

The overview of the local data quality estimation

• Digital filters include [16]i. An integer coefficient band-pass filterii. A derivative filter combined with an amplitude square processiii. Moving window integrator

• Peak detection logic locates cardiac cycles [18]


14


• Using the location of cardiac cycles, we extract the time length of each cardiac cycle (i.e., IPI)

• We define this high pass filtered IPI time series as IPI variation, and denote it as v[n]


15


• The time series of average amplitude of cardiac cycle is denoted as


][na

16

Irregular Fluctuation

• Now we have new time series and• The proposed mechanism focuses on discarding

most of normal cardiac cycles based on observing irregular fluctuation– Irregular fluctuation describes erratic movements in a

time series that follow no recognizable or regular patter [5].

– Health hazardous events carry irregular fluctuation in and/or . [14]

– Our observation verifies that motion artifact noise also carries irregular fluctuation in and/or


][nv ][na

][nv ][na

][na][nv

17

Pattern of normal cycles

• We define the pattern of normal cycles as the degree of variation in normal v[n] and a[n] within a window size of N assuming Gaussian distribution– It is actually known that the distribution of normal

cardiac cycles are usually skewed rather than Gaussian [12].

– However, these models are very complicated, and usually evolve over time.

– Since our objective is to discard most of normal cardiac cycle rather than accurately model the distribution, Gaussian is a good approximation


18

Pattern Learning Process

• The pattern learning process involves computing the mean and the std. dev. of normal cycles for a window size of N.– Learning process bounded by O(N).– It requires local memory to store N numbers.

• The proposed mechanism determines that the newest cardiac cycle of index n has high data quality if

note that δ can be adaptively chosen as a result of the training process


aaaaaavvvvvv nanv ][&][

19

Local Data Quality Estimation

• The output of the local estimation, which is then transmitted to the aggregator is

• In our experiment, we asked participants to sit on a chair without any movements to acquire N=20 normal cycles.

• Then, δ is chosen such that all N normal cycles satisfy the above inequality.


,0

,1][nQ

aaaaaavvvvvv nanv ][&][if

otherwise

20

Global Data Quality Estimation

• The data fusion process is performed at the aggregator side.STEP 1: Temporal synchronization based on the minimum sampling rate for real-time purpose.(sensors may have different sampling rate)


Synchronization

t1[m]

t2[m]

t3[m]

21


• Then, tk[m] is defined as

tk[m] = Qk[n]

for m within each cardiac cycle. (mathematical details provided in the paper)

• Intuitively, it is a time series for the kth sensor that shows the quality of a cardiac cycle in a synchronized sampling rate


22


• STEP 2: we fuse the synchronized tk[m] to estimate the data quality t[m] of the overall BAN using a majority voting.


23

Experimental & Simulation Results

• Experiment– Conducted on 4 participants to show that the

proposed mechanism recognizes the noise created by motion artifacts.

• Simulation– Simulation on multi-variable cardiac data (from

PhysioNet) to address that the mechanism can recognize the noise created by health hazardous events such as hearth arrhythmia.


24

Experiment

• 4 Participants• 3 off-the-shelf cardiac sensors (i.e., K = 3)– CHEST: Alive Heart Monitor

– LEG: Vernier EKG Sensor

– FINGER: Nonin Onyx 9560 SpO2 Sensors


25

Experiment• Set of actions that simulates the average daily activity of a person based

on the American Time User Survey (ATUS) [21].– Walking– Sitting down with no movement– Sitting down while moving upper limbs– Bending down to pick up an object– Standing up while moving upper limbs

• Participants performed the actions for 10 seconds and rest for another 10 seconds. Then, this combination of action and rest is repeated 3 times– clearly distinguishing the noise from normal signal

• We manually annotated all cardiac cycles to be either normal or abnormal (noise), and compared the detection results (i.e., tk[m]) against this ground truth annotation for each sensor data.


26

Illustrative Example of Experimental Results


27

Experimental Results

rd: the overall detection rate

rfa: false abnormal rate

rfn: false normal rate


• In average, the detection rate for the abnormal cycles can be detected at the rate of 90.56% when the data was fused. The false abnormal and false normal rates were 3.89% and 25.11%, respectively.

28

Simulation

• The database used in this simulation is the MGH/MF Waveform Database from PhysioNet [10].– Due to the limitation and safety issues in recruiting participants

with severe cardiac ailments who are likely to undergo a health hazardous cardiac problem during the experiment

• Includes– Three ECGs– An arterial pressure– A pulmonary arterial pressure– A central venous pressure signal.


29

Simulation

• Two interesting observations1. All local signals had the same IPI time series

since none of the sensors is locally distorted due to motion artifacts.

2. The average ratio of the number of normal to abnormal cardiac cycles is 99.1%.• We investigate detection rate for normal and abnormal

cycles separately.


30

Simulation Results


• In average, the detection rate for the abnormal cycles is 100% and the detection rate for the normal cycles is 87.8%.

N’a: # detected abnormal

Na: # actual abnormal

rfn: false normal rate

N’n: # detected normal

N’n: # actual normal

rfa: false abnormal rate

31

Summary• We introduced an efficient mechanism for estimating data quality of a

BAN composed of cardiac sensors.– Low complexity

• The proposed method employs – Local data quality estimation– Global data quality estimation

• We presented experimental results based on three off-the-shelf cardiac sensor devices in order to detect motion artifact noise.

• We also presented simulation results to detect health hazardous events using the proposed mechanism.


32

Thank you

• Questions?

• Please feel free to reach me at [email protected]


A Mechanism for Data Quality Estimation of On-Body Cardiac Sensor Networks Sunghoon Ivan Lee *...

Documents

Transcript of A Mechanism for Data Quality Estimation of On-Body Cardiac Sensor Networks Sunghoon Ivan Lee *...