Automatic Ballistocardiogram (BCG) Beat DetectionUsing a Template Matching Approach

Adviser: Ji-Jer Huang

Presenter: Zhe-Lin Cai

Date:2014/12/24

30th Annual International IEEE EMBS ConferenceVancouver, British Columbia, Canada, August 20-24, 2008

J. H. Shin, B. H. Choi, Y. G. Lim, D. U. Jeong and K. S. Park

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Outline

Introduction

Method

Result

Discussion

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Introduction

Ballistocardiography (BCG) ―Cardiac and respiratory evaluation―Non-invasive method―Strength of myocardial contraction―Condition of the heart

Photo Source : http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6421181

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Introduction

Ventricle contraction―ECG Q-R-S complex

―BCG I-J-K complex

ECG, BCG principles―ECG records the heart in nerve conduction arising from potential

changes graphics

―BCG defined as a method by which body vibrations caused by heart activity are recorded

Photo Source : http://abrc.snu.ac.kr/korean/viewforum.php?f=172

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Introduction

BCG:―Advantages:• Non-contact• Non-conscious• Security

―Disadvantages: • Motion artifact signal

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Introduction

This paper suggests a beat detection method for ballistocardiogram (BCG) from an unconstrained cardiac signal monitoring devices

―The goal of the method is extraction of J peak without ECG synchronization

Photo Source : http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6421181

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Introduction

The analyzed systems were Chee et al (2005) method using balancing tube and air-mattress for unconstrained measurement system loadcell type BCG measurement system and EMFi-film measurement system were chosen

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Introduction

We applied template matching approach for BCG beat detection algorithm to the three different types of BCG measurement system

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Method

The detection method is based on a “template matching” rule evaluated using a correlation function in a local moving-window procedure

Beat detection algorithm operates in two stages― BCG template modeling― Beat detection by Template Matching

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Method

BCG template modeling

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Method

BCG template modeling

―Bandpass filterThe bandpass filter which has 0.5~20Hz cutoff frequency was

applied to remove a respiration signal from raw signal

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Method

BCG template modeling

―The template bases were selected by the following criteria Clear to identify the I-J-K complexes Includes at least 10 BCG cycles Free from respiration effort signal and motion artifact signal

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Method

BCG template modeling

―Segmentation & verificationIn the segmentation step, the selected template bases were split to

several BCG cycles and each cycle was verified by the expert

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Method

BCG template modeling

―Ensemble AverageAnd the cycles were normalized between -1 to 1. Finally, BCG

template was constructed by an ensemble averaging of the valid BCG cycles centered at J peak points

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Method

Constructed BCG templates :(a) Air-mattress system (b) Loadcell system (c) EMFi-film system

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Method

Beat detection by Template Matching

―Template matching was performed by local moving window function which generates correlation coefficient between the constructed template in previous modeling procedure and BCG signal

Template matching illustration

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Method

𝑟 𝑓 ,𝑔=𝑐𝑜𝑣 ( 𝑓 ,𝑔)𝜎 𝑓 𝜎𝑔

𝑐𝑜𝑣 ( 𝑓 ,𝑔)❑⇒∫0

𝑇

𝑓 (𝑡 )𝑔 (𝑡+𝜏 )𝑑𝜏

𝜎 𝑓=√𝑐𝑜𝑣( 𝑓 , 𝑓 )  

Photo Source : http://en.wikipedia.org/wiki/Convolution

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Result

Synchronized ECG was measured simultaneously for a convenience of expert’s manual scoring of BCG J peaks

Photo Source : http://abrc.snu.ac.kr/korean/viewforum.php?f=172

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Result

Sampling rate:―Loadcell type BCG system: 200Hz―Air-mattress type BCG systems:1KHz ―EMFi-film type BCG systems:1KHz

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Result

The template matching approach was exercised in BCG signals to detect the J-peak events

Detected J peaks marking with reversed triangle in three BCG systems

―Air-mattress (upper) ―Loadcell (middle) ―EMFi-film (lower)

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Result

The sensitivity is the detection screening probability of the method

The positive predictivity value determines the capacity to identify a true event

𝑺ensitivity   =  𝑻𝑷

(𝑻𝑷+𝑭𝑵 )

P ositive   predictivity   value  = 𝑻𝑷

(𝑻𝑷+𝑭𝑷 )

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Result

We analyzed 10 subjects recorded during resting and sleep. All subjects have normal health condition and signal was acquired in supine position with normal breath. The subjects were recorded for 30 seconds and 5 records were analyzed in each system

Result from air-mattress system and the loadcell system shows a high sensitivities and positive predictivity values.

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Discussion

The template matching has an advantage with a simple and fast algorithm, and be able a real-time process

Various template types can be useful in different measurement systems and it is possible to register and classify multi-templates with patient disease condition

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Discussion

Fixed template is not a universal set with wide variation of BCG shape according to the change of measurement situations

In the further studies, we will apply the template matching approach to classification of the beats with a cardiac disease and an automatic template updating method to overcome the limitation of the fixed template

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Thanks for your attention