IMPLEMENTATION OF AN AUTOMATED ECG-BASED DIAGNOSIS

ALGORITHM FOR A WIRELESS BODY SENSOR PLATFORM

Francisco J. Rinc´on

, Laura Guti´errez

, M´onica Jim´enez

, V´ıctor D´ıaz

, Nadia Khaled

David Atienza

1, 4

, Marcos S´anchez-

Elez

, Joaqu´ın Recas

and Giovanni De Micheli

DACYA/UCM, Madrid, Spain

Depto. Salud y Rendimiento Humano/UPM, Madrid, Spain

LSI/EPFL, Lausanne, Switzerland

ESL/EPFL, Lausanne, Switzerland

Keywords:

Wireless Body Sensor Networks, Biomedical signal processing, Electrocardiogram.

Abstract:

Wireless Body Sensor Networks (WBSN) are poised to become a key enabling technology of personal systems

for pervasive healthcare. Recent results have however shown that the conventional approach to their design,

which consists in continuous wireless streaming of the sensed data to a central data collector, is unsustainable

in terms of network lifetime and autonomy. Furthermore, it was established that wireless data communica-

tion is responsible for most of the energy consumption. To address the energy inefﬁciency of conventional

WBSNs, we advocate an advanced WBSN concept where sensor nodes exploit their available, yet limited

processing and storage resources to deploy advanced embedded intelligence and processing, to reduce the

amount of wireless data communication and consequently energy consumption. More speciﬁcally, this paper

addresses the design and optimization of an automated real-time electrocardiogram (ECG) signal analysis and

cardiovascular arrhythmia diagnosis application for a prototype sensor node called Wireless 25 EEG/ECG sys-

tem. The satifactory accuracy of this on-line automated ECG-based analysis and diagnosis system is assessed

and compared to the salient off-line automated ECG analysis algorithms. More importantly, our results show

an energy consumption reduction of 80% to 100% with respect to conventional WBSNs, when our analysis

and diagnosis algorithm is used to process the sensed ECG data to extract its relevant features, which are

then wirelessly reported to the WBSN central data collector, after the node can automatically determine the

potential cardiovascular pathology without human monitoring.

1 INTRODUCTION

AND RELATED WORK

Wearable personal health systems for pervasive mon-

itoring and healthcare are widely recognized to

be a key enabling integration technology for next-

generation advanced citizen-centric eHealth delivery

solutions (Lo and Yang, 2005). Through enabling

continuous biomedical monitoring and care, they hold

the promise of improved personalization and qual-

ity of care, increased ability of prevention and early

∗

This work is partially supported by the Spanish Gov-

ernment Research Grant TIC 2005-5619 and the Integrated

Systems Center of EPFL under the project ”Performance

monitoring for professional and recreation sports using

Wireless Sensor Networks” (2007-2008).

diagnosis, and enhanced patient autonomy. Further-

more, wearable personal health systems can help the

eHealth sector realize rapid sustained market growth,

reduction of healthcare and public costs.

To provide the necessary accurate, integrated and

long-term assessment and feedback, these wearable

personal health systems must sense, acquire, moni-

tor and analyze a large number of physiological and

metabolic parameters, both during physical activity

and rest. The number as well as the nature of the pa-

rameters of interest depends on the actual biomedical

application/scenario and target population. Neverthe-

less, it is largely accepted that Wireless Body Sen-

sor Networks (WBSN) will be the underlying com-

mon architecture and technology of these personal

health systems. More speciﬁcally, the WBSN will

Rincón F., Gutiérrez L., Jiménez M., Díaz V., Khaled N., Atienza D., Sánchez-Élez M., Recas J. and De Micheli G. (2009).

IMPLEMENTATION OF AN AUTOMATED ECG-BASED DIAGNOSIS ALGORITHM FOR A WIRELESS BODY SENSOR PLATAFORM.

In Proceedings of the International Conference on Biomedical Electronics and Devices, pages 88-96

DOI: 10.5220/0001549000880096

 SciTePress

consist of a number of sensor nodes attached to the

patient body, each sensor node potentially compris-

ing of 5 components (Lazzer et al., 2002; Culler

et al., 2004): sensors, actuators, a microprocessor,

a wireless transceiver and an energy source. Each

WBSN node ensures the accurate sensing and capture

of its target physiological data, its (pre-) processing

and wireless communication to the other nodes and

the wearable Personal Digital Assistant (PDA). This

PDA will be responsible for the storage, organization,

complementary analysis and fusion of the collected

information, its user-friendly representation, and its

dissemination to the relevant medical staff or cen-

tral monitoring service through private and/or public

wireless access networks (Lo and Yang, 2005).

State-of-the-art commercial products and experi-

mental prototypes of personal health monitoring sys-

tems merely apply on-board analog ﬁltering to the

sampled sensed data, before it is either logged on a

bulky patient unit for off-line analysis, or wirelessly

transmitted to a remote monitoring system (Jovanov

and et al., 2005; LifeShirt, ; SmartShirt, ). The ob-

trusiveness and off-line nature of the analysis of the

ﬁrst approach compromises its acceptance and appli-

cability to pervasive healthcare, whereas the second

approach is not sustainable in free-living conditions in

terms of autonomy. Indeed, current results in WBSN,

e.g., prosthesis processing (Kemere and et al., 2004)

or Electroencephalogram (EEG) / Electrocardiogram

(ECG) monitoring (L¨ofgren and et al., 2007)) indi-

cate that an unaffordable amount of energy would be

spent in the wireless communication, if no local sig-

nal processing is present and most of the acquired

data is wirelessly streamed to the PDA. Moreover,

similar conclusions can be derived from the Code-

Blue project (Project, ), which is a WBSN that targets

biomedical monitoring by including a set of devices

to collect ECG and oxigen saturation data, which can

be transmitted through a wireless network to a wide

range of receiving devices that can display the data

in real time. The conclusions of this project outline

that the largest proportion of energy is consumed in

the wireless data transmission, and requires monitor-

ing of the received data by a doctor or biomedical

specialist; thus, the WBSN nodes are not able to re-

port any physical anomaly. Therefore, we advocate

in this paper an advanced WBSN concept where sen-

sor nodes exploit their available processing and stor-

age resources to deploy advanced embedded intelli-

gence and processing, which will be optimized for

enhanced functionality and autonomy. More partic-

ularly, in this paper, we investigate the feasibility and

beneﬁts of such an advanced WBSN for an automated

electrocardiogram (ECG) signal analysis and cardio-

vascular arrhythmia recognition application, using a

prototype sensor node called Wireless 25 EEG/ECG

system (Penders et al., 2007).

A signiﬁcant amount of research effort has been

devoted to the automated analysis of ECG signals.

Some of the proposed methods are able to classify

a set of Arrhythmias depending on special correlated

characteristics of the ECG signal, for instance, using

Multicategory Support Vector Machines (Khadtare

and Sahambi, 2004). Other methods are based on

the underlying detection of the major ECG charac-

teristic waves, namely the QRS complex, P and T

waves (S¨ornmo and Laguna, 2005). As a matter of

fact, the performance of an automated ECG analy-

sis system using the second approach critically de-

pends on the reliable detection of these ﬁducial waves.

The most salient methods proposed for the auto-

mated detection of the ECG ﬁducial waves belong to

three categories: ﬁltering or adaptive thresholding,

wavelet transform-based and (nonlinear) multiscale

transform-based (S¨ornmo and Laguna, 2005). The

latter approach was evidenced to have less noise sen-

sitivity than adaptive thresholding, and to avoid the

problem of position deviation exhibited by wavelet-

based techniques. Therefore, in this paper, we con-

sider a multiscale morphological derivative (MMD)

transform-based algorithm to realize automated ECG

characteristic wave detection.

While the retained MMD transform-based algo-

rithm was validated by simulation (Sun et al., 2005),

its translation into a robust, efﬁcient and reliable au-

tomated diagnosis capability embedded in our wear-

able sensor node calls for the porting and (non-

straightforward) optimization of this algorithm to

adapt it to the sensor node’s limited processing re-

sources. In general, this porting and optimization

effort is key to translate the recent biomedical sig-

nal processing advances into autodiagnosis tools, and

hence to enable pervasive healthcare. As a result, the

main contributions of the paper are:

• The design of a real-time ECG-based diagnosisal-

gorithm, including a new run-time ECG signal re-

construction module, based on the off-line MMD

algorithm, and a diagnosis module able to identify

various anomalies in the cardiovascular function.

• The porting and optimization of the new real-time

ECG-based diagnosis algorithm on the Wireless

25-channel EEG/ECG sensor node platform.

• The application of the new diagnosis algorithm

for autodiagnosis on-board the sensor node to sig-

niﬁcantly reduce the amount of data to be wire-

less transmitted, and consequently, dramatically

reduce the sensor node’s energy consumption and

extend its battery life.

IMPLEMENTATION OF AN AUTOMATED ECG-BASED DIAGNOSIS ALGORITHM FOR A WIRELESS BODY

SENSOR PLATAFORM

The rest of the paper is organized as follows. In

Section 2, we overview the software and hardware

architecture of the WBSN node used in this work.

Then, in Section 3, we present the ECG-based diag-

nosis algorithm ported to our WBSN node. Next, in

Section 4, we describe the performance and energy

consumption trade-offs between node processing and

the communication tasks. Finally, in Section 5, we

summarize the main conclusions of this work.

2 WIRELESS BODY SENSOR

NODE ARCHITECTURE

As previously mentioned, this work uses as target

platform a Wireless 25-channel EEG/ECG sensor

node (Penders et al., 2007). This small sensor node

can monitor up to 25 different bio-potential signals

and transmit in real-time wirelessly the sensed infor-

mation to a data collector device. Furthermore, it

can be combined with other instances of the same

node to deﬁne complex WBSN topologies. The node

has a very elaborated hardware/software architecture,

which enables porting different types of biomedical

signal processing applications, use different hardware

components, and customize the communication stack

for various wireless communication protocols. In this

section, we summarize the main features of the har-

ware/software architecture of this node.

2.1 Hardware Architecture

The hardware architecture of the Wireless 25-channel

EEG/ECG sensor node is partitioned in three main

blocks, which relate to the three main tasks in WSNs,

namely, sensing, processing and wireless transmis-

sion of the acquired informationto a base station (e.g.,

a PC, PDA or a mobile phone).

The sensing task is performed by a 25-channel

ultra-low-power Application-Speciﬁc Integrated Cir-

cuit (ASIC) that is able to extractthe bio-potentialsig-

nals. The core of the processing part is a TI MSP430

ultra-low-power microcontroller (Lutz Bierl, 2000).

This small 16-bit microcontroller has a low active

power (0.6 nJ/instruction), as well as a a very low

stand-by power (2µW). In addition, it features a fast

wakeup from stand-by to active mode (6µs) and an

on-chip 12-bit analog-to-digital converter, suitable for

biomedical signal processing. Finally, it also includes

60kB of ﬂash program memory and 2kB of RAM to

upload applications and store processed data from the

different sensors.

Then, the wireless communication task is carried

out using a Nordic nRF2401 transceiver (Semicon-

Radio

nRF2401

µC

MSP430

25-ch

ASIC

Radio

driver

µC

driver

ASIC

driver

MAC

TinyOSkernel

Application

Figure 1: OS-Based EEG/ECG 25-ch sensor node architec-

ture overview.

ductor, 2000), which is an ultra-low-power 2.4 GHz

communication chip. This component has a very low

power consumption, with only 10.5mA at an output

power of -5dBm and 18mA in receive mode. Also,

it includes built-in power-down modes that allow to

switch-off the radio when not used, and special trans-

mission modes to reduce the processing needed by the

microcontroller for every new packet sent or received.

Overall, this radio is a low-power solution for WBSN,

which has relatively low duty cycle rates.

2.2 Software Architecture

The software architecture of the platform follows a

layered modular approach in which each hardware

component (sensors, microcontroller and radio) is a

separate software module. This modular multi-layer

structure, depicted in Figure 1, is supported thanks

to the inclusion of TinyOS (Culler, 2006), a light

event-based operating system specially designed for

WSNs. It has been written using the nesC (Gay et al.,

2003) programming language, which is an extension

of the C language optimized for the memory restric-

tions of sensor networks. Thus, it is possible to eas-

ily port new biomedical signal processing applica-

tions using the nesC language, and to use the under-

lying hardware driver support provided by TinyOS

to access different hardware blocks in the architec-

ture. In addition, the abstraction from the hardware

blocks makes possible to modify/replacethe hardware

blocks included in each instance of the node, with-

out modifying the remaining blocks or the upper lay-

ers (communication protocols, applications, etc.). In

particular, in this research work, while a porting for

the TI MSP430 microcontroller was already available

within the TinyOS hardware support, we had to de-

velop the new drivers to support the Nordic nRF2401

radio module and the 25-channel EEG/ECG ASIC in

the operating system, to be able to use them from the

ECG-based diagnosis application layer.

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

3 ECG-BASED DIAGNOSIS

ALGORITHM

The design of an ECG-based diagnosis algorithm to

detect possible cardiovascular diseases is a very com-

plex process that can be divided in two main phases.

First, a real-time detection of the characteristic waves

of ECG signals (Sun et al., 2005; Sun et al., 2002)

(e.g., complex QRS, P and T wave, etc.) is an es-

sential module for the quality of the detection of the

cardiovascular diseases; thus, we have developed a

run-time ECG wave identiﬁcation module, which is

described in Section 3.1. Second, once the funda-

mental features of the input ECG signal are identi-

ﬁed, they can be used to detect if the heart is suffer-

ing an anomalousbehaviorof the cardiovascularfunc-

tion and diagnose corresponding pathologies. This

last phase of the proposed ECG-based diagnosis al-

gorithm is described in Section 3.2.

3.1 Run-Time ECG Signal

Reconstruction

Our run-time ECG signal reconstruction phase is

based on a multiscale morphological derivative

(MMD) transform-based singularity detector (Sun

et al., 2005). This initial detector is an off-line al-

gorithm able to reliably identify the Q wave, R peak,

S wave and the onsets and offsets of the P wave and

T wave, which are fundamental blocks of any ECG

signal, as shown in Figure 2.

Although the ECG signal reconstruction using the

MMD algorithm is quite accurate (Sun et al., 2005),

the fundamental issue to directly apply for WBSN is

the fact that MMD requires as input data the com-

plete wave read from a patient during a large amount

of time, which is subsequently processed to tune the

thresholds to apply for each concrete sample. Unfor-

tunately, the memory resources of a WBSN sensor

node are very limited (only few kilobytes) to min-

imize its size and power consumption; thus, taking

into account the high sampling frequency needed for

a reliable ECG signal processing, only a few seconds

of information can be stored, which is insufﬁcient to

accurately tune the thresholds for MMD. Moreover, if

all the data is streamed from the sensor node to a de-

vice with larger capacity (e.g., a base station) to pro-

cess them, the sensor node would run out of battery

in a short period of time, because of the elevated data

transmission rate that is needed and the high energy

consumption of the radio. As a result, WBSN require

an on-line approach for ECG signal reconstruction, in

which the complete ECG signal is processed directly

in the sensor nodes and only the relevant results are

(a)

(b)

(c)

Figure 2: Results obtained using the MMD detector. (a)

Original ECG input signal (b) Multiscale morphological

transform of the original signal (c) Points and waves de-

tected by the algorithm.

A=onset of the P wave, B=P peak, C=offset of the P wave, D=onset

of the Q wave, E=onset of the R wave, F=R peak, G=offset of the

R wave, H=offset of the S peak, I=onset of the T wave, J=T peak,

K=offset of the T wave.

sent to the data collector device or base station.

In the following we propose a run-time process-

ing of the ECG signal, based on a MMD transform-

based singularity detector, which can be directly ex-

ecuted in a WBSN sensor node. In the ﬁrst phase of

our run-time ECG ﬁducial wave detection algoritm,

the received input signal is ﬁltered for noise reduction

and baseline wander correction (S¨ornmo and Laguna,

2005). To this end, we have implemented a morpho-

logical ﬁlter on the input signal (Sun et al., 2002),

where at all instances of time during the execution,

a window of the last 300 samples are stored in a cir-

cular buffer. Hence, since the sampling frequency of

the 25-channel EEG/ECG node is 200Hz, the current

implementation of the algorithm uses the last 1.5s of

the input signal as history of processed signal.

Once each sample is ﬁltered, the following step in

an MMD detector requires the application of a mul-

tiscale morphological transform on the ﬁltered data

to detect the ﬁducial points in the original ECG sig-

nal. However, due to the scarce memory resources

in the node, the transformed signal cannot be stored

in the node; thus, the morphological transform is dy-

namically recalculated when needed during the fol-

lowing steps of the run-time algorithm. Then, the lo-

cal maxima and minima of the transformed signal can

be selected, based on some characteristics of the mor-

IMPLEMENTATION OF AN AUTOMATED ECG-BASED DIAGNOSIS ALGORITHM FOR A WIRELESS BODY

SENSOR PLATAFORM

Table 1: Steps followed by the proposed run-time ECG sig-

nal reconstruction algorithm. All the operations are per-

formed over the transformed data.

1. The thresholds Th

and Th

are selected based

on an adaptive thresholding from the histogram

of the transformed signal

2. r

peak

= local minima with absolute amplitude

larger than Th

If r

peak

was not detected, go to 1, otherwise

continue

3. r

= ﬁrst local maxima on the left of r

peak

with

absolute amplitude larger than Th

of f

= ﬁrst local maxima on the right of r

peak

with absolute amplitude larger than Th

4. q

wave

= ﬁrst local minima on the left of r

with

absolute amplitude larger than Th

5. s

wave

= ﬁrst local minima on the right of r

of f

with absolute amplitude larger than Th

6. p

of f

= ﬁrst local maxima on the left of q

wave

with absolute amplitude larger than Th

= ﬁrst local maxima on the left of p

of f

with

absolute amplitude larger than Th

7. t

= ﬁrst local maxima on the right of s

wave

with absolute amplitude larger than Th

of f

= ﬁrst local maxima on the right of t

with

absolute amplitude larger than Th

phological trasform and a dynamic thesholding, up-

dated at run-time using a window of the last received

samples. These detected local maxima and minima

are then subsequently used to locate the characteristic

points of the wave. In this regard, since the sampling

frequency is 200Hz, the microcontroller only has 5ms

between two consecutive samples to perform all the

calculations on the data to ﬁnd the local maxima and

minima. As this time interval is too short to execute

the complete detection of maxima and minima points,

we have divided the algorithm into 7 phases, which

are listed in Table 1. Each of these phases can be ex-

ecuted in the interval between two consecutive sam-

ples. Thus, when a new data is sampled, ﬁltered and

included in the circular buffer, one of the 7 phases is

executed. For example, at the beginning of the pro-

cess, when no R peak is yet present in the considered

window, steps 1 and 2 are repeated continuously be-

tween data samples. Then, when a R peak is detected

in step 2, steps 3 to 7 are applied in sequence, such

that, each of them is used in the time slot between

two consecutive samples. After the complete identiﬁ-

cation of the current input ECG wave is ﬁnished, the

process restarts from step 1 for the next wave.

For illustration purposes, one of the results ob-

tained using the proposed run-time ECG signal re-

construction algorithm in an input wave is depicted

in Figure 2. This ﬁgure shows three different waves,

the ﬁrst one (or a) is the original ECG signal given

as an input to the algorithm. The second wave (or

b) is the original signal after applying the multiscale

morphological transform, all the points detected by

the algorithm are marked. The last one (or c) shows

the detected waves and peaks over the original signal.

This last wave illustrates where the Q wave, R peak,

S wave and the onsets and offsets of the P and T wave

are found using our run-time algorithm.

3.2 Arrhythmia Detection Phase

After processing the original ECG signal and obtain-

ing the ﬁducial points of the wave (Figure 2), our

diagnosis module is applied to check if the signal

presents any anomalous behavior,which may hint that

the patient is suffering a cardiovascular pathology.

The diagnosis module checks iteratively ﬁve dif-

ferent conditions based in the points detected in the

ECG signal, according to the valid ranges reported

in (Schamroth, 1971; P´erez-G´omez, 1985), namely:

1. The time from D to H must be less or equal to

0.10s.

2. The time interval from A to D must be in the range

from 0.12s to 0.20s.

3. The amplitude of J must always be positive.

4. The time from D to F must not be longer than

0.03s.

5. The QT interval rule, which establishes a relation

between the interval from D to K. This rule indi-

cates the valid interval between the heart beat rate,

and the last RR interval (i.e., the interval from the

last R peak to the current one). To ﬁnd the valid

QT interval, Bazet’s formula (1) is used to calcu-

late the QT coefﬁcient for an input signal (QTc)

as shown in Equation 1. Then, the valid values of

the QTc are reported in Table 2.

QTc =

time interval f rom D to K

√

previous RR interval

(1)

As a result, in the current version of our diagnosis

algorithm, according to the results of checking these

previous ﬁve conditions, the sensor node reports to

the base station that the heart of the monitored patient

can be suffering one of the pathologies of Table 3.

4 EXPERIMENTAL RESULTS

The complete proposed ECG-based diagnosis algo-

rithm has been ported and optimized to be executed

in the 25-channel EEG/ECG node. The statistics

and energy values shown in the rest of the paper

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

Table 2: Normal values of the QTc. HBR=Heart Beat Rate

(per minute), RR=RR interval. Values in last two columns

are in seconds.

HBR RR QTc and normal limits

40 1.5 0.46 (0.41 - 0.51)

50 1.2 0.42 (0.38 - 0.46)

60 1 0.39 (0.35 - 0.43)

70 0.86 0.37 (0.33 - 0.41)

80 0.75 0.35 (0.32 - 0.39)

90 0.67 0.33 (0.30 - 0.36)

100 0.60 0.31 (0.28 - 0.34)

120 0.50 0.29 (0.26 - 0.32)

150 0.40 0.25 (0.23 - 0.28)

180 0.33 0.23 (0.21 - 0.25)

200 0.30 0.22 (0.20 - 0.24)

were obtained using the model of the 25-channel

EEG/ECG system for PowerTOSSIM, which was val-

idated in (Rinc´on et al., 2008), showing variations of

less than 4% between the simulation framework and

the measurements of the ﬁnal platform.

4.1 Validation of the ECG-based

Diagnosis Algorithm

The accuracy of the algorithm running on the 25-

channel EEG/ECG system was tested with several in-

put signals taken from the QT database (Laguna et al.,

1997). This database was specially created for eval-

uation of algorithms that detect waveform boundaries

in ECG signals. It consist of 105 ﬁfteen-minute ex-

cerpts of two-channel ECG recordings. It also con-

tains a subset of beats (at least 30 in each recording)

that have been manually annotated by cardiovascular

analysis experts.

In our ﬁrst set of experiments, we have com-

pared the performance of the proposed algorithm

with MMD and other state-of-the-art ECG detec-

tion algorithms from the literature for detection of

waveform boundaries in ECG signals: the original

MMD off-line algorithm (Sun et al., 2005), an adap-

tive thresholding-based detector (TD) (Daskalov and

Christov, 1999) and a wavelet transform-based detec-

tor (WD) (Li et al., 1995).

We report three parameters in these comparisons,

namely, their mean error (m), standard deviation (σ)

and Sensitivity (Se). We have used these parameters

because they give a complete overview of the overall

behavior of each algorithm, as m shows how close the

detection results using an algorithm are to the manu-

ally annotated results, σ represents the stability of the

detection and Se (deﬁned in 2) measures the detection

sensitivity (where TP is the number of true detections

and FN is the number of manual annotations that are

not detected by the algorithm).

Table 3: Pathologies detected by diagnosis module.

Problem Possible pathology

Time from D to H

longer than 0.10s

(it could even reach

0.12s).

Block of the His bundle.

Supraventricular rhythm with

aberrant conduction.

Abnormal conduction over acce-

sory pathways.

Ventricular rhythm or pacemaker

rhythm.

The time interval

from A to D is

longer than 0.20s

(long PR interval).

Disorder in the conduction be-

tween atriums and ventricles at

the atrioventricular node level,

His bundle (or its branches) or

Purkinje system.

The time interval

from A to D is

shorter than 0.12s

(short PR interval).

Presence of an anomalous ac-

cesory pathway that produces a

faster conduction or the presence

of a rhythm with origin in the

atrioventricular union, in the left

atrium or in the lower part of

the right atrium. Generally, this

anomaly is due to a ventricular

preexcitation.

The amplitude of J

is negative (T wave

is negative).

Primary alterations of the repo-

larization phase (due to ischemia

or myocardial infarction, suba-

cute pericarditis or myocarditis).

Secondary alterations of the re-

polarization phase (due to alter-

ations of the ventricular repolar-

ization).

The time from D

to F is longer than

0.03s.

Delay in the ventricular activa-

tion time.

QT interval longer

than the values

speciﬁed in Table

The ventricular repolarization

has slowed down, which can

be due to acquired or congen-

ital causes. It is related to the

appearance of arrhythmias.

QT interval shorter

than the values

speciﬁed in Table

This problem is usually related to

the use of some medicines, hy-

percalcemia or hyperpotassemia.

Se =

TP+ FN

×100 (2)

Table 4 shows the results obtained from these

comparisons and the accepted standard deviation tol-

erances given by the Common Standards for Electro-

cardiography (CSE) committee. As this table illus-

trates, the values of the mean error are quite low and

can be compared to the values obtained with MMD,

TD and WD. Only in the case of T

, the mean er-

ror is very high, but this is not a problem, since T

is not relevant for the cardiovascular pathologies tar-

getted by the diagnosis module. Then, the compari-

son between the proposed run-time ECG signal recon-

struction algorithm and the MMD detector in terms of

IMPLEMENTATION OF AN AUTOMATED ECG-BASED DIAGNOSIS ALGORITHM FOR A WIRELESS BODY

SENSOR PLATAFORM

Table 4: Comparative results of our run-time ECG charac-

teristic wave detection and other state-of-the-art ECG de-

tection algorithms.

Technique Parameter P

of f

QRS

of f

Ours Se(%) 92.4 92.4 100 100 96.6 91.7

m(ms) 1.4 14.9 -7.8 8.2 53.6 12.8

σ(ms) 15.6 13.3 22.6 16.8 21.6 20.9

MMD Se(%) 97.2 94.8 100 100 99.8 99.6

m(ms) 9.0 12.8 3.5 2.4 7.9 8.3

σ(ms) 9.4 13.2 6.1 10.3 15.8 12.4

TD Se(%) 96.2 97.0 99.9 99.9 98.8 98.9

m(ms) 10.3 -5.7 -7.3 -3.6 23.3 18.7

σ(ms) 14.1 13.6 10.9 10.7 28.3 29.8

WD Se(%) 89.9 89.9 100 100 99.1 99.2

m(ms) 13.0 5.4 4.5 0.8 -4.8 -8.9

σ(ms) 12.7 11.9 7.7 8.7 13.5 18.8

CSE σ(ms) 10.2 12.7 6.5 11.6 - 30.6

σ is very important, since the proposed algorithm is

based on the original off-line MMD detector. As ex-

pected, the values of the standard deviation achieved

by the MMD method with the complete signal anal-

ysis instead of the limited run-time history are better.

Nonetheless, the degradation of standard deviation in

the case of the new run-time approach is very limited.

In particular, the reasons for this limited degradation

are the following:

• The proposed run-time algorithm is optimized to

work in real-time in a sensor node platform with a

simple microcontroller. Floating point operations

are very costly in terms of time for such a sim-

ple microcontroller, and they had to be converted

into integer operations, with the resultant loss of

precision in the ﬁltering and the multiscale mor-

phological transform operations.

• Dynamic thresholding based on the histogram of

the signal in the new approach is more difﬁcult

than with the off-line approach, since only a small

window of data (the last 1.5s of signal) is available

to choose the correct thresholds. Sometimes this

is also a source of errors in the detections.

However, it is very important the fact that results

using the proposed run-time approach are not far from

the original algorithm, especially considering that, in

exchange of a bit of accuracy, it is possible to process

the input data in real-time and the patient can be in-

formed of a possible cardiac pathology in real-time.

Therefore, the time to react and attend the patient in

case a problem occurs is minimal, and then the conse-

quences of the problem and the probability of the ap-

pearance of a more serious pathology derivated from a

late identiﬁcation of the problem are greatly reduced.

4.2 Energy Features of Diagnosis

Sensor Node Platform in WBSN

One of the main problems in WBSNs is energy con-

sumption, mainly due to the high sampling frequency

required by these applications. Sensors generate lots

Table 5: Typical ranges of heart beat rate per minute at rest

and practising aerobic and anaerobic exercise for different

kind of adult people.

Sedentary Fit Sportperson

At rest 70 - 90 60 - 80 40 - 60

Aerobic 110 - 130 120 - 140 140 - 160

Anaerobic 130 - 150 140 - 160 160 - 200

Table 6: Energy savings in the radio due to transmission

using the run-time ECG detector instead of data streaming.

Sedentary Fit Sportperson

At rest 91 - 93% 92 - 94% 94 - 96%

Aerobic 87 - 89% 86 - 88% 84 - 86%

Anaerobic 85 - 87% 84 - 86% 80 - 84%

of data that need to be transmitted to the base station,

for that reason, the time that sensors can be in stand-

by mode is shorter than in other kind of applications.

In these networks, the nodes are attached to the body,

therefore batteries must be very small, to allow the

person to do a normal life. These small batteries have

a short lifetime and after some time they need to be

replaced. If the sensors are implanted in the body, the

replacement of the batteries is not feasible. A possible

alternativecould be energyscavengers, but in general,

energy scavengers are not able to supply the node con-

tinuously, since the average power generated by one

of these devices is usually much lower than the av-

erage power consumed by the nodes. Normally, they

are coupled to batteries and recharge them, extending

their lifetime.

Analyzing the energy consumption of the different

hardware blocks of a node using typical applications

for WBSNs, we can extract that the main cause of en-

ergy waste is the transmission and reception of data

via radio. The radio is responsible for between 70%

and 90% of the total amount of energy consumed by

the whole sensor node. Then, one of the main chal-

lenges in WBSNs is trying to reduce as much as possi-

ble the energy consumption of the node, therefore we

need to minimize the energy consumed by the radio,

since the radio is the main source of energy waste.

In this section, we consider and analyze three dif-

ferent approaches: data streaming, the use of the pro-

posed run-time ECG signal reconstruction algorithm

and the complete embedded diagnosis system, cou-

pling the diagnosis module introduced in 3.2 to the

basic run-time ECG algorithm.

First, we analyze the most basic application, that

simply performs data streaming, which means that all

the data read by the sensors is transmitted to the base

station without any intermediatte operation. All the

information will be processed off-line in the base sta-

tion. In order to perform a reliable ECG streaming, a

sampling frequency of about 200Hz is required. Tak-

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

ing into account that the packet payload is 18 bytes

and the size of each sample is 12 bits, we can con-

clude that 1000 packets are sent in 60s.

Using the run-time ECG signal reconstruction al-

gorithm previously described, the sampled data are

preprocessed before being sent. In this way, a packet

is sent only when a R peak is detected. The sent

packet contains when the R peak has been detected,

the position of the rest of detected points respect the

R peak and the amplitude of P peak, R peak and T

peak. The number of packets sent in 60s using this

algorithm would be equal to the heart beat rate per

minute. Typical values of heart beat rate per minute

are shown in Table 5. Table 6 shows the energy sav-

ings in the radio due to transmission of packets ob-

tained with the use of the proposed algorithm. In

order to verify these theoretical results, some exper-

iments were run in the simulator. The result of one

of these simulations is presented in Figure 3. This

plot shows that the energy consumption due to packet

transmission in the case of the ECG streaming appli-

cation is 50.30mJ in a simulation of 60 real seconds.

If the run-time ECG algorithm is used, the energy

consumption drops to 3.73mJ for an input signal of

76 heart beats per minute, which means a reduction

of 92.6%, as it was predicted in Table 6.

As previously mentioned, a diagnosis module has

been implemented and successfully tested. This mod-

ule evaluates, taking as input the points detected by

the run-time ECG signal reconstruction algorithm, if

the ECG signal of the patient is normal or it presents

any anomalous behavior. When a problem is de-

tected, a packet is sent, containint the code of the di-

agnosed pathology and the points of the wave where

the pathology was detected. In the worst case, when

a problem is detected in every heart beat, the energy

savings with regard to the data streaming case are the

same as if we do not use the diagnosis module. In the

best case, when no pathology is detected because the

ECG signal is normal, no transmission is produced

and therefore the reduction in the number of trans-

missions reaches 100% improvement.

5 CONCLUSIONS

Energy consumption minimization is one of the ma-

jor design challenges to enable WBSN-based solu-

tions for personal healthcare systems. In this paper,

we have demonstrated the feasibility of exploiting

the limited processing and storage resources to de-

ploy a novel real-time automated ECG analysis and

arrhythmia diagnosis algorithm on an actual sensor

node platform. This goal was achieved through judi-

0,00

10,00

20,00

30,00

40,00

50,00

60,00

ECG streaming Run-time ECG detector

E (mJ)

Figure 3: Energy consumption due to packet transmission

for the ECG streaming application and the run-time ECG

signal reconstruction algorithm during a period of 60s.

cious application-level optimizations, taking into ac-

count the underlying hardware architecture and the

real-time constraints of the automated diagnosis ap-

plication. The satisfactory accuracy of both the auto-

mated ECG analysis (ECG characteristics wave de-

tection) and ECG-based arrhythmia diagnosis algo-

rithms was assessed. Finally, we also demonstrated

that the deployment of these advanced algorithms for

ECG analysis and autodiagnosis enables a dramatic

reduction of the energy consumption (between 80%

and 100%, depending on the sensed signal), com-

pared to the conventional WBSN approach that sim-

ply wirelessly streams the sensed data to the central

node. These excellent results show the potential of

embedding advanced processing and intelligence in

sensor nodes for extending the lifetime of WBSNs,

and motivates further research in characterizing the

optimal trade-off between embedded signal process-

ing and wireless communications for these energy-

constrained wireless networks.

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