Multiple Biopotentials Acquisition System for Wearable Applications

S. Benatti

, B. Milosevic

, M. Tomasini

, E. Farella

, P. Sch

onle

, P. Bunjaku

, G. Rovere

, S. Fateh

Q. Huang

and L. Benini

1,3

DEI, University of Bologna, Bologna, Italy

E3DA - Fondazione Bruno Kessler, Trento, Italy

Integrated System Laboratory, ETHZ, Zurich, Switzerland

Keywords:

PLI removal, EEG, ECG, Wearable computing, Biomedical monitoring, Sensor node.

Abstract:

Wearable devices for monitoring vital signs such as heart-rate, respiratory rate and blood pressure are demon-

strating to have an increasing role in improving quality of life and in allowing prevention for chronic cardiac

diseases. However, the design of a wearable system without reference to ground potential requires multi-level

strategies to remove noise caused from power lines. This paper describes a bio-potential acquisition embedded

system designed with an innovative analog front-end, showing the performance in EEG and ECG applications

and the comparison between different noise reduction algorithms. We demonstrate that the proposed system is

able to acquire bio-potentials with a signal quality equivalent to state-of-the-art bench-top biomedical devices

and can be therefore used for monitoring purpose, with the advantages of a low-cost low-power wearable

devices.

1 INTRODUCTION

Every year in EU and US over 4 million deaths are

caused by cardiac diseases. More than 100 millions

of people conduct their life after a heart attack and

there are 30 million of people suffering from arrhyth-

mia and other cardiac and cardiovascular disorders

(WHF , 2012). On the other hand, there are more

than 1 Billion people suffering from neurological dis-

eases, even if the international community was seri-

ously underestimating in traditional epidemiological

and health statistical methods its effects. The health

cost for treatments associated with these diseases is

about 450.5 B$ per year (WHO, 2012).

The monitoring of hearth rate, blood pressure,

oxygen saturation, brain activity and other physiolog-

ical parameters can help minimize this cost and en-

hance the quality of life for a signiﬁcant part of the

world’s population. During hospitalization, monitor-

ing these parameters is relatively simple and can be

obtained with high-end bench-top diagnostic systems.

To extend the control and the diagnostic capabilities

out of hospitals it is necessary to provide unobtru-

sive and low cost systems, which should be equipped

with adequate sensor interfaces, sufﬁcient computa-

tional resources and with optimized power manage-

ment strategies.

One open challenge in this ﬁeld is the design of

unobtrusive systems that can be used in different ap-

plications of human biosignal analysis. Their goal is

to be able to run the needed algorithms for monitoring

and diagnosing the life parameters, while providing

adequate communication interfaces and a prolonged

battery life. The design of a wearable system for

biosignal monitoring presents many challenges, since

it involves integrated circuit design, mixed analog-

digital signal acquisition techniques, digital signal

processing, low power algorithms and adaptive ﬁlter-

ing techniques.

Given the nature of the signals to be acquired,

which lie in the 1 µV - 10 mV range, with a frequency

band of 0 − 1 kHz, analog acquisition and robustness

to noise is crucial. Noise interference is caused by

the ﬂoating reference potential of the human body

and by the 50/60 Hz power-line interference (PLI).

To achieve a robust design of a wearable system for

biosignal measurement, we need to properly address

the minimization of noise sources.

In this paper, we present a wearable platform

based on a low power Cortex M4 microcontroller

and a high performance Analog Front End (AFE)

(Schonle et al., 2013). The AFE is equipped with

a scalable SPI-interface, allowing accurate acquisi-

tion of bio-potentials. The computing performance of

260

Benatti S., Milosevic B., Tomasini M., Farella E., Schoenle P., Bunjaku P., Rovere G., Fateh S., Huang Q. and Benini L..

Multiple Biopotentials Acquisition System for Wearable Applications.

DOI: 10.5220/0005320302600268

In Proceedings of the International Conference on Biomedical Electronics and Devices (SmartMedDev-2015), pages 260-268

ISBN: 978-989-758-071-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

the microcontroller allows advanced signal process-

ing to ﬁlter the noise and apply processing techniques

to achieve robust biosignal monitoring.

This work demonstrates the performance of our

platform with two kinds of vital signs: the acquisi-

tion of an Electrocardiogram (ECG), based on a 3

leads conﬁguration, and of an Electroencephalogam

(EEG), using 2 fully differential channels. We imple-

mented, proﬁled and compared four approaches for

the PLI noise ﬁltering. Furthermore, we show the per-

formance of the acquisition system and we compare

the results with a commercial state-of-the-art AFE

(AD7194) for the ECG and a non-portable hospital

device for multichannel bio-potential acquisition for

the EEG.

ECG data acquired with our system and with the

AD7194 chip exhibit similar characteristics, having a

signal-to-noise ratio (SNR) of 13.4 dB in our case and

12.3 dB for the AD7194. By applying PLI ﬁltering

techniques we were able to improve the performance

of our system and to achieve a SNR of up to 30 dB.

The acquired EEG data was instead compared against

a state-of-the-art non-portable device, used in clini-

cal environments. We veriﬁed that data acquired by

the two systems has the same temporal evolution and

frequency spectrum.

The reminder of the paper is organized as follows:

Section 2 introduces related works; Section 3 illus-

trates the system setup and the nature of the signals

we are considering, while Section 4 describes the pro-

cessing techniques applied. Section 5 presents exper-

imental results. Finally, in Section 6 we draw the con-

clusions and discuss some future developments.

2 RELATED WORKS

In recent years, there have been numerous research

and commercial efforts in the design of wearable

biopotentials measurement systems. There are al-

ready several low-cost devices on the market, like

the ones from OMRON (Omron R7, 2004), Philips

(Philips MX40, 2011) and VIVAGO (Vivago 8005,

2012). All these devices offer limited computational

resources and are not designed for applications re-

quiring ﬂexibility e.g. in terms of multi-modality

or number of channels. Their counterparts is repre-

sented by novel wireless portable and quite compact

systems, such as the g.MOBIlab+ by G.TEC (GTEC

g.MOBIlab, ), which at one side are more ﬂexible,

enabling multi-modal multi-channel biosignal record-

ing, however being expensive and often requiring the

use of proprietary software.

At research level, physiological signals monitor-

ing systems have appeared since the MIThrill2003

prototype (DeVaul et al., 2003), which represents a

milestone in wearable computing platforms. It is

based on a PDA connected with a sensor board for

biopotential acquisition. The embedded sensor board

is equipped with a 3-axes accelerometer, temperature

sensor and an analog front-end for EMG-ECG acqui-

sition. Sampled data is transmitted from the sensor

board to the PDA for processing. Another important

similar project is the AMON platform (Anliker et al.,

2004), a monitoring system composed by a wrist-

worn device capable of measuring blood pressure, O

saturation, ECG and body temperature, which sends

acquired data to a base station for remote storage,

processing and support functionalities. Furthermore,

there is a 2-axis accelerometer to correlate vital pa-

rameters to user activity. The project (Mundt et al.,

2005) proposes a system to measure and collect bi-

ological data for up to 9 hours in extreme environ-

ments. The board is based on a PIC microcontroller

and can collect ECG, heart rate, blood pressure and

saturation, body temperature and movements. The

user can interact with the acquisition system using a

PDA, thus obtaining an unobtrusive system. How-

ever, the main limitation of all these architectures is

the need to transmit data from sensors to a PDA or

a base station. The sensor nodes are not equipped

with sufﬁcient computational resources, thus the data

transmission limits the bandwidth of the processed

signals and has a signiﬁcant impact on power con-

sumption. The recent advances in embedded sys-

tem integration enable on-board signal processing re-

quired to improve the quality of the signals and pos-

sibly to perform part of the signal processing, thus

opening the possibility to optimize the power con-

sumption. These are the opportunities we want to ex-

ploit in this work.

A wireless system performing on-board process-

ing is presented in (Buxi et al., 2012). The system is

equipped with a custom DSP and a TI MSP430 micro-

controller. The DSP is used to perform Indipendent

Component Analysis (ICA) and adaptive ﬁltering to

detect heart rates and cardiac arrhythmia. Although

the system is well optimized for low power operation,

it is based on a custom DSP independent from the low

power commercial microcontroller, therefore limiting

scalability in more complex and diverse applications.

(Penders et al., 2011) presents a neck-band system

for cardiac activity monitoring, which implements a

CWT based BPM algorithm and an ECG derived res-

piration rate monitor. The data can be stored in an SD

card or transmitted by a low power radio. The digi-

tal platform is an MSP430 and the ASIC analog front

end offers great performance in terms of power con-

MultipleBiopotentialsAcquisitionSystemforWearableApplications

261

sumption (21µV), but it is connected to the microcon-

troller with an analog interface. An analog back-end

interface can be affected by additional noise and re-

duces the system scalability, when compared to digi-

tal interfaces. Recently, wearable systems started also

to cope with EEG signal acquisition and monitoring.

Several wireless and portable EEG monitoring sys-

tems have been published so far in literature. Some

implementations exploit discrete components (Sulli-

van et al., 2008) and (Chen and Wang, 2011), while

others rely on fully integrated systems, especially for

the implementation of low-noise analog front-end cir-

cuitries. An example of a fully implemented wireless

EEG sensor node is presented in (Brown et al., 2010)

which uses the analog front-end published in (Yazi-

cioglu et al., 2008). All these systems are strongly ori-

ented to a single application scenario therefore lack-

ing in ﬂexibility. Furthermore, they cannot be used in

applications requiring multimodality, where data fu-

sion from heterogeneous sensors is required.

The lesson learned by these inspiring approaches

is that we must join the design of a high perfor-

mance AFE to allow the acquisition of the principal

biopotentials (EEG, ECG and EMG) with an efﬁcient

microcontroller with integrated DSP functions. The

AFE must have a digital back-end with SPI or I2C to

provide a faster communication with the microcon-

troller, which must have sufﬁcient computational re-

sources to locally execute algorithms for ﬁltering and

information extraction, without data transmissions to

a base station.

3 SYSTEM DESCRIPTION

3.1 System Architecture

The Cerebro wearable device is a smart sensor node

designed for medical and ﬁtness applications and its

high-level functional block diagram is shown in Fig.

1. This node consists basically of a multichannel

analog-front-end (AFE) with a digital interface. The

AFE is called Cerebro ASIC (Schonle et al., 2013),

which is responsible for the biomedical signal acqui-

sition. An ARM microcontroller with a FPU DSP

instruction set is used for noise ﬁltering and further

feature extraction. Additional inertial and pressure

sensors have been added in order to collect data on

the patients’ motor activity. After biomedical signals

have been acquired and elaborated, they can be lo-

cally stored on a SD card or wirelessly transmitted by

a Bluetooth module to a nearby smart phone or tablet.

The supply of the system is handled by a dedi-

cated IC equipped with an internal switching voltage

I2C

JTAG

SPI

ELECTRODES

CONNECTOR

JTAG/SWD

USB

BATTERY

IMU

PRESSURE

SENSOR

CARD

BLUETOOTH

MODULE

SENSORS

REGULATOR

MCU

REGULATOR

USART

SPI

ANALOG

DIGITAL

CEREBRO

ARM Cortex M4

MCU

POWER

MANAGEMENT

BLUETOOTH

REGULATOR

CEREBRO DIGITAL

REGULATOR

CEREBRO ANALOG

REGULATOR

Figure 1: Overview of the Cerebro wearable device.

regulator. This power management circuitry automat-

ically detects the power source in use (battery or USB

connector) and manages the recharging of the battery

while providing low-dropout voltage regulators to the

inertial and pressure sensors as well as to the Blue-

tooth module. This ﬂexible solution for controlling

the power management allows us to switch-off sub-

modules of the board that are not required for a tar-

geted biomedical application and thus enhancing bat-

tery lifetime.

The board is designed on a 6 layers printed-

circuit-board (PCB) with a single ground plane, a split

power plane (between analog and digital) and 2 sig-

nal layers (top and bottom). Discrete components

are placed on both top and bottom layers in order

to further reduce the resulting board size, which is

85 × 50 mm.

The Cerebro AFE has already been tested with

ECG signals (Schonle et al., 2013) and for the recog-

nition of hand gestures using EMG signals for pros-

thesis control in (Benatti et al., 2014). In this work,

we aim at a more general scenario where the devel-

oped platform can be used in a large variety of differ-

ent biomedical applications including EMG, ECG and

EEG signal acquisition, or combinations of them. We

further analyze the nature and requirements of ECG

and EEG signals, which are used to prove the capa-

bilities of the biomedical platform.

3.2 ECG Signal

The ECG signal is one of the most important biosig-

nals that can provide a great amount of information in

medical and ﬁtness applications. It senses the electri-

cal activity of the heart during its muscular contrac-

tions. During the heartbeat, the muscular cells on the

hearth surface depolarize their membrane. The result-

ing potential differences can be detected using sur-

face electrodes placed in a proper conﬁguration and a

low noise signal ampliﬁer. The typical frequencies of

ECG signals go from 0.01 to 250 Hz and the ampli-

tude is lower than 5 mV.

BIODEVICES2015-InternationalConferenceonBiomedicalElectronicsandDevices

262

As all the biosignals, the ECG is difﬁcult to man-

age because it is a low amplitude signal affected by

different sources of noise (e.g. power line interfer-

ence, baseline wander, ground loop noise, muscular

contraction and respiration artifacts). For these rea-

sons, in the design of an ECG signal detection system

and, in general, for the design of a wearable device

for biopotentials measurement, the system level hard-

ware and software design is extremely important.

3.3 EEG Signal

EEG represents a collection of electrical voltages

recorded at different locations on the scalp of patients.

Electrical characteristics of these signals show a typ-

ical bandwidth range from 0.5 to 100 Hz with a peak

amplitude of about 100 µV. Such signals are gener-

ated by millions of underlying neurons that ﬁre asyn-

chronously and are responsible for the brain activity.

Hence, the EEG recording does not contain the ac-

tivity of single neurons but the averaged activity of

millions of neurons. For this reason, raw (unpro-

cessed) EEG signals do not show any kind of reg-

ularity in the time-domain. However, after proper

band-pass ﬁltering of the EEG signals, e.g. extract-

ing delta, theta, alpha and beta frequency bands, more

regular patterns can be identiﬁed, especially in the

lower frequency bands. These ﬁltered EEG bands

are of high interest because they are strictly correlated

with the states in the brain such as wakefulness, sleep,

or even with some severe diseases including epilepsy

and neoplasms (Moore and Lopes, 1998), (Buzaki,

2006) and (Nunez and Srinivasan, 2006).

EEG evaluation is thus an important tool to learn

about brain functioning. The understanding of brain

functions, however, is currently limited to clinical en-

vironments and may not accurately reﬂect brain activ-

ities in the real world. Furthermore, long recordings

are feasible only during sleep as the EEG ampliﬁers

are large, inconvenient for patients, heavy, and need to

be plugged in, making it unable for patients to move

more than a few meters.

On the contrary, a wearable EEG device is not re-

stricted to these limitations, exploiting paradigms of

integration, low power operation, and small device

size (Casson et al., 2010). This increased degree of

freedom for the wearable EEG device allows to record

biological signals also outside of the clinical labora-

tory, increases the interests in the research ﬁeld that

is currently restricted to medical use cases. Such a

portable device has countless applications with high

market potential, ranging from early detection of dis-

eases to the monitoring of well-being habits and cog-

nitive behavior.

To provide a satisfactory wearable EEG device,

it is essential to build it such that its performance is

comparable to those obtained in the state-of-the-art

clinical devices. We therefore compare our system

with the commonly used bench-top clinical device. It

is shown that we obtain similar performance in ﬁeld

tests.

3.4 Data Processing

All applications relying on the acquisition of biopo-

tential signals share the common need to reduce noise

and interferences by digital post-processing. The

most common sources of interference are: power-

line interference (PLI), baseline wander (drift), move-

ment and breathing artefacts, changes in the electrode

impedance and intra-channel interference. In particu-

lar, PLI introduces a 50/60 Hz sine interference to the

signal. It is always noticeable, even when the system

is battery-powered (Serrano, 2003), that its accurate

removal is a critical but required task.

Different approaches have been presented in lit-

erature so far for the removal of PLI. The simplest

approach is a notch ﬁlter, which is a stop-band ﬁl-

ter that allows to attenuate the frequency of a narrow

band. The rejected band depends on the quality factor

Q of the ﬁlter: with a Q = 50 the notch ﬁlter provides

10 dB attenuation at the frequency f

pli

± 0.5 Hz. This

approach has the advantage to be easily implemented

and to have low computational requirements, but it

introduces distortion in the signal power spectrum.

More advanced approaches have been developed

in literature to overcome the limitations of the notch

ﬁlter and to accurately separate the PLI from the

EEG signal. In particular, there are methods based

on time-domain subtraction (Levkov et al., 2005), re-

gression subtraction (Bazhyna et al., 2003) and sinu-

soidal modeling (Zivanovic and Gonz

alez-Izal, 2013).

These methods all share the basic approach, which

consists in the estimation of the sinusoidal interfer-

ence and its removal from the acquired signal. Brief

summaries of these technique is listed below.

The time-domain subtraction method ﬁrst divides

the signal in linear and non-linear segments which

is performed by setting a threshold on the second

derivate of the signal. Then, in the linear segments,

the signal is averaged and the PLI is estimated, which

is then also removed from non-linear segments.

Regression-subtraction or time-correlated power-

line interference subtraction estimates the amplitude

and phase of the PLI and then subtracts it from sub-

sequent samples. This approach models the interfer-

ence as two quadrature sinusoids with the same fre-

quency and uses blocks of data to estimate it with a

MultipleBiopotentialsAcquisitionSystemforWearableApplications

263

(a) (b)

Figure 2: Raw ECG signals (top) and their FFTs (bottom) for the Cerebro platform (a) and for the AD7194 acquisition board

(b).

least-squares ﬁt.

The sinusoidal modeling approach models the in-

terference by a set of harmonically related sinusoids

modulated by low order time polynomials. The poly-

nomial coefﬁcients can be estimated by minimizing

the quadratic error between the signal and the model

in a given interval. Then the estimated PLI is sub-

tracted from the original signal to obtain a noise free

signal.

Time domain method relies on the separation of

linear and non-linear segments, which is easily ap-

plicable to ECG signals, but is not useful in other

cases. Regression-subtraction and sinusoidal model-

ing, on the other hand, use short intervals of signals

(0.5 − 1.5 s) to estimate the sinusoidal source, which

is then effectively removed from the original signals,

preserving their other characteristics.

4 EXPERIMENTAL RESULTS

4.1 ECG Signal Acquisition

To test the capabilities of our system and to com-

pare the different de-noising approaches, we used a

3-lead ECG acquisition scenario. In this set-up, we

used one differential channel of the AFE to acquire

the ECG signal. We placed two disposable electrodes

on the wrists of the user and an additional electrode

was placed on the ankle as the reference potential (pa-

tient ground).

The ECG signal was acquired with our platform

and with a reference state-of-the-art AFE, which is the

Analog Devices AD7194 chip. We used the develop-

ment board provided for this chip, which is equipped

with 8 analog channels and can be connected to a PC

via USB for the data acquisition. Our device sent the

sampled data to a PC via Bluetooth and all the col-

lected data was stored and processed on the PC. The

two systems have been conﬁgured in the most simi-

lar way possible, setting the acquisition frequency at

1 kHz and the gain at 8 for our device, while the ADC

was set to sample at 960 Hz with a gain of 128.

The signals acquired from the two devices are

plotted in Fig. 2, along with their frequency spec-

trums. From the plots we can observe that the two sys-

tems provide signals of comparable quality. The fre-

quency spectrum of the two acquired signals is very

similar. The Cerebro ASIC acquired signal exhibits a

strong PLI component at 50 Hz (the exact measured

frequency of the PLI was 55 Hz). The ADC has an in-

ternal ﬁlter, which reduces the PLI contribution. Us-

ing the data acquired with Cerebro, we implemented

and compared the PLI ﬁltering techniques described

in the previous Section. Ideally, the perfect ﬁlter-

ing technique should remove the PLI component and

leave the rest of the signal frequency spectrum as it is.

The result of the PLI removal for the considered

approaches is shown in Fig. 3, where we plotted the

frequency spectrum for the raw and the ﬁltered sig-

nals acquired with our system. From the plots we can

see that all the considered algorithms remove the PLI

component, but they alter the rest of the frequency

spectrum in different ways. We can note that the notch

ﬁlter removes also frequency components close to the

PLI, while the time-domain approach alters consider-

BIODEVICES2015-InternationalConferenceonBiomedicalElectronicsandDevices

264

Figure 3: Comparison of the FFT of the PLI ﬁltering tech-

niques (top to bottom): notch ﬁlter, time domain subtrac-

tion, regression subtraction and sinusoidal modeling.

ably the spectrum of the output signal, for both lower

and higher frequencies.

The regression-subtraction method is the one that

alters less the frequency spectrum of the signal, only

reducing the PLI component. Also the sinusoidal

modeling approach is very precise and removes cor-

rectly the PLI without additional changes in the sig-

nal. It is worth noting that the signals also present

interferences at frequencies multiple of the main PLI

component (e.g. 100 Hz), which can be removed ap-

plying the considered approaches also for those fre-

quencies.

To summarize the results of this comparison,

we computed the SNR of the ﬁltered signals, ob-

taining 21.9 dB for the notch ﬁlter, 28.3dB for the

10 20 30 40 50 60 70

100

120

epoch

frequency [Hz]

−20

−15

−10

−5

Figure 4: Color-coded power spectra of consecutive 90-s

epochs (4 s window) with Hanning window. Data was sam-

pled with 250 Hz. Spectra are color coded on a logarithmic

scale, 30 dB corresponds to an input signal with an ampli-

tude of 100µV.

time-domain subtraction, 29.5 dB for the regression-

subtraction and 30.4 dB for the sinusoidal modeling

approach. The latter two methods deliver consider-

ably better signal quality and are the ones chosen to

be used in our system. In particular, the regression

subtraction method has been implemented and used

for the ﬁnal ECG and EEG applications, since it is

the one that preserves better the frequency response

of the system.

4.2 EEG Signal Acquisition

A spectrogram is shown in Fig. 4, which gives a bet-

ter insight on the overall performance of the wearable

EEG device. The color map on the right hand-side

has the dimension decibel and indicates the power

of the recorded signal. A 100µV sinusoidal signal

is sampled with the device at a sampling frequency

of 250 Hz while the input sinusoidal signal frequency

is increased over time from 0 to 250 Hz. Each EEG

channel of the device is low-pass ﬁltered at a cut-

off frequency of 66 Hz. The dots on the illustration

represents the sinusoidal amplitude changes (from

grey to white) indicating the signal attenuation by the

low-pass ﬁlter. The spectrogram also shows signals

aliased back in the Nyquist band which were gener-

ated by feeding the EEG device with sinusoidal sig-

nals with frequencies higher than half of the sampling

frequency. The horizontal gray line at 50 Hz is the

residual of the mains interference being successfully

suppressed to a certain degree and does not signif-

icantly disturb the EEG signal. This spectrogram il-

lustrates that the Cerebro AFE is well-suited to record

EEG signals with amplitudes below 100µV. In the

frequency band of interest, i.e., from 0.5 to 100 Hz, a

signal-to-noise ratio of more than 35 dB is observed.

In order to provide a useful wearable EEG device,

MultipleBiopotentialsAcquisitionSystemforWearableApplications

265

Figure 5: Illustration of the electrodes arrangement on the

head of the patient with the denoted patient ground G. The

pairs of electrodes are split in colors where the gray ones

are referenced to the right ear (R2) and the the white ones

to the left ear (R1).

it is essential to build it such that its performance is

comparable to those obtained in the state-of-the-art in

clinical use. For this reason, a direct comparison with

the commonly used state-of-the-art device in hospitals

is performed in this paper to show that the wearable

EEG device achieves similar performance.

The speciﬁcation of the wearable EEG device is

performed taking the recorded EEG signals from both

devices, i.e., our wearable EEG device with limited

hardware resources and the state-of-the-art record-

ing device in operation at the University Hospital of

Zurich (USZ) which has no limitations in power con-

sumption and hardware resources. The EEG signals

are simultaneously acquired from the same patient at

a sampling frequency of 250 Hz with the wearable

EEG device and at 256 Hz with the USZ device.

Fig. 5 illustrates the placement of electrodes on

the scalp of the patient. During the recording, a test

subject is connected to different EEG channels, where

two out of four channels are connected to the wear-

able EEG device (numbered gray electrodes), while

the remaining two are connected to the state-of-the-

art device used in USZ (numbered white electrodes).

The pairs of electrodes connected to both devices are

placed next to each other in order to record EEG sig-

nals from the common source.

The current measurement setup in use at USZ con-

sists of polygraphic ampliﬁers provided by Artisan

(Micromed, Mogliano Veneto, Italy) and the record-

ing is performed using the Rembrandt Datalab (Em-

bla System, Broomfeld, CO, USA). Before analog-to-

0 1 2 3 4 5 6 7 8 9 10

−40

−20

time [sec]

amplitude [µV]

state-of-the-art in USZ

wearable EEG device

0 1 2 3 4 5 6 7 8

−20

−10

time [sec]

amplitude [µV]

0 1 2 3 4 5 6

−10

−5

time [sec]

amplitude [µV]

0 0.5 1 1.5 2 2.5 3 3.5 4

−10

−5

time [sec]

amplitude [µV]

zoom

delta band

theta band

alpha band

beta band

Figure 6: First pair of EEG channels comparing the wear-

able EEG device with the state-of-the-art in USZ. Brain

waves are obtained after proper ﬁltering and decomposing

of the EEG signals in delta, theta, alpha, and beta frequency

bands.

digital quantization is done, the analog EEG signals

are high-pass (-3 dB at 0.16 Hz) and low-pass ﬁltered

(-3 dB at 67.2 Hz), as indicated in (Moore and Lopes,

1998).

In the wearable EEG device, the analog front-end

(Schonle et al., 2013) samples the analog signals at

250 Hz. After the analog-to-digital quantization, dig-

ital high-pass (-3 dB at 0.16 Hz) and low-pass ﬁltering

(-3 dB at 66 Hz) is performed on the digitized signals.

After acquiring the EEG signals from the two ac-

quisition systems, a post-processing ﬁlter has been

applied in order to highlight major cerebral waves of

the patient. As illustrated in Fig. 6, the EEG Channel-

1 of both devices deliver similar EEG signal patterns

for the different EEG bands of interest (Channel-2

shows very similar behaviour, not shown). For both

devices, waves are obtained according to the follow-

ing scheme: delta low-pass ﬁlter at 4 Hz, theta band-

pass ﬁlter between 4 and 7 Hz, alpha band-pass ﬁlter

between 7 and 15 Hz while beta is band-pass ﬁlter be-

tween 15 to 30 Hz.

FFT plots of the two acquired channels are shown

in Fig. 7, which provides a different perspective of the

measured EEG. Frequency responses of both devices

overlap over the whole Nyquist frequency band. The

BIODEVICES2015-InternationalConferenceonBiomedicalElectronicsandDevices

266

state-of-the-art in USZ

wearable EEG device

frequency [Hz]

power density [µV/√Hz]

100101

100

-2

-1

-2

-4

frequency [Hz]

power density [µV/√Hz ]

100101

100

-2

-1

-2

-4

state-of-the-art in USZ

wearable EEG device

1 pair of electrodes

2 pair of electrodes

Figure 7: Frequency response of the two electrode pairs.

FFTs are very similar in amplitude except for the notch ﬁlter

at 50 Hz of the USZ acquisition system.

only noticeable difference is that the USZ device uses

a notch ﬁlter to cancel mains interference while the

wearable EEG device handle this problem by apply-

ing the regression subtraction approach and subtract-

ing the estimated interference signals (Schonle et al.,

2013).

5 CONCLUSION

In this paper, we presented the design and evaluation

of a wearable node able to acquire heterogeneous vi-

tal signs and having on-board ﬁltering and processing

capabilities. In particular, we considered the acqui-

sition of EEG and ECG signals, comparing our sys-

tem with state-of-the-art solutions. We also described

the implementation and the results of different de-

noising algorithms that can be executed in real-time

with our platform. We demonstrated that the output

of the Cerebro node for EEG and ECG signal ﬁlters

accurately the power-line noise reaching a SNR of

30dB, which is comparable with state-of-the-art de-

vices, while represents a much less expensive solu-

tion.

Furthermore, Cerebro offers higher scalability

w.r.t. comparable commercial devices or other re-

search prototypes and higher ﬂexibility in terms of

multi-modality. Therefore, we think that the proposed

wearable platform has high potential to be used not

only for the monitoring of vital signs, but also for

biomedical real-time signal processing. The Blue-

tooth interface allows to connect the Cerebro board

to mobile devices paving the way to the development

of efﬁcient user interfaces for clinician and patients.

Future work will explore the use of Cerebro out of the

lab, exploiting the scalability of the system and chal-

lenging the ability to perform complex algorithms on-

board.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the support of T.

Kleier, M. Lanz, F. Schulthess, A. Malafeev, and T.

Burger. Further thanks go to the medical research

partners, particularly P. Achermann. This work is

funded by the Nano-Tera.ch RTD Project WearMe-

Soc, which is ﬁnanced by the Swiss Confederation

and scientiﬁcally evaluated by SNSF. It is also funded

by INAIL Prosthetics Center in Vigorso di Budrio

(Italy) and by EU project PHIDIAS (g.a. 318013).

REFERENCES

Anliker, U., Ward, J., Lukowicz, P., Troster, G., Dolveck,

F., Baer, M., Keita, F., Schenker, E., Catarsi, F., Coluc-

cini, L., Belardinelli, A., Shklarski, D., Alon, M., Hirt,

E., Schmid, R., and Vuskovic, M. (2004). AMON:

a wearable multiparameter medical monitoring and

alert system. Information Technology in Biomedicine,

IEEE Transactions on, 8(4):415–427.

Bazhyna, A., Christov, I., Gotchev, A., Daskalov, I., and

Egiazarian, K. (2003). Powerline interference sup-

pression in high-resolution ECG. In Computers in

Cardiology, 2003, pages 561–564. IEEE.

Benatti, S., Milosevic, B., Casamassima, F., Schonle, P.,

Bunjaku, P., Fateh, S., Huang, Q., and Benini, L.

(2014). EMG-based hand gesture recognition with

ﬂexible analog front end. In proceedings of the 2014

IEEE Biomedical Circuits and Systems Conference

(BioCAS).

Brown, L., van de Molengraft, J., Yazicioglu, R., Torfs, T.,

Penders, J., and Van Hoof, C. (2010). A low-power,

wireless, 8-channel EEG monitoring headset. In En-

gineering in Medicine and Biology Society (EMBC),

2010 Annual International Conference of the IEEE,

pages 4197–4200.

Buxi, D., Berset, T., Hijdra, M., Tutelaers, M., Geng, D.,

Hulzink, J., van Noorloos, M., Romero, I., Torfs, T.,

and Van Helleputte, N. (2012). Wireless 3-lead ECG

system with on-board digital signal processing for am-

bulatory monitoring. In Biomedical Circuits and Sys-

tems Conference (BioCAS), 2012 IEEE, pages 308–

311.

MultipleBiopotentialsAcquisitionSystemforWearableApplications

267

Buzaki, G. (2006). Rhythms of the Brain. Oxford University

press, London, 2nd edition.

Casson, A., Yates, D., Smith, S., Duncan, J., and Rodriguez-

Villegas, E. (2010). Wearable electroencephalogra-

phy. Engineering in Medicine and Biology Magazine,

IEEE, 29(3):44–56.

Chen, X. and Wang, J. (2011). Design and implementa-

tion of a wearable, wireless EEG recording system. In

Bioinformatics and Biomedical Engineering, (iCBBE)

2011 5th International Conference on, pages 1–4.

DeVaul, R., Sung, M., Gips, J., and Pentland, A. (2003).

MIThril 2003: applications and architecture. In Wear-

able Computers, 2003. Proceedings. Seventh IEEE In-

ternational Symposium on, pages 4–11.

GTEC g.MOBIlab. http://www.gtec.at/Products/Hardware-

and-Accessories/g.MOBIlab-Specs-Features.

Levkov, C., Mihov, G., Ivanov, R., Daskalov, I., Christov,

I., and Dotsinsky, I. (2005). Removal of power-line

interference from the ECG: a review of the subtraction

procedure. BioMedical Engineering OnLine, 4(1):50.

Moore, R. and Lopes, J. (1998). IFCN standards for digital

recording of clinical EEG. the international federation

of clinical neurophysiology. In Electroencephalogr

Clin Neurophysiol Suppl. PubMED.

Mundt, C., Montgomery, K., Udoh, U., Barker, V., Thonier,

G., Tellier, A., Ricks, R., Darling, B., Cagle, Y.,

Cabrol, N., Ruoss, S., Swain, J., Hines, J., and Ko-

vacs, G. (2005). A multiparameter wearable physio-

logic monitoring system for space and terrestrial ap-

plications. Information Technology in Biomedicine,

IEEE Transactions on, 9(3):382–391.

Nunez, P. and Srinivasan, R. (2006). Electric ﬁelds of the

brain: the neurophysics of EEG. Oxford University

Press, London, 2nd edition.

Omron R7 (2004). http://www.omron-healthcare.com/eu/

en/our-products/blood-pressure-monitoring/r7.

Penders, J., van de Molengraft, J., Altini, M., Yazicioglu,

F., and Van Hoof, C. (2011). A low-power wireless

ECG necklace for reliable cardiac activity monitoring

on-the-move. Proc. of the Intl. Conf. of the IEEE En-

gineering in Medicine and Biology Society.

Philips MX40 (2011). http://www.usa.philips.com/healthcare-

products/HC862115/intellivue-mx40-wearable-

patient-monitor.

Schonle, P., Schulthess, F., Fateh, S., Ulrich, R., Huang, F.,

Burger, T., and Huang, Q. (2013). A DC-connectable

multi-channel biomedical data acquisition ASIC with

mains frequency cancellation. In ESSCIRC (ESS-

CIRC), 2013 Proceedings of the, pages 149–152.

Serrano, Roberto E., e. a. (2003). Power line interference

in ambulatory biopotential measurements. Proceed-

ings of the 25th Annual International Conference of

the IEEE, 4.

Sullivan, T., Deiss, S., Jung, T.-P., and Cauwenberghs, G.

(2008). A brain-machine interface using dry-contact,

low-noise EEG sensors. In Circuits and Systems,

2008. ISCAS 2008. IEEE International Symposium

on.

Vivago 8005 (2012). http://www.vivago.com/products-and-

services/products/care-8005/.

WHF(2012). http://www.world-heart-federation.org/cardio

vascular-health/global-facts-map/.

WHO (2012). http://www.who.int/mental

health/neurology/

neurological disorders report web.pdf.

Yazicioglu, R., Merken, P., Puers, R., and Van Hoof, C.

(2008). A 200 uw eight-channel EEG acquisition

ASIC for ambulatory EEG systems. Solid-State Cir-

cuits, IEEE Journal of, 43(12):3025–3038.

Zivanovic, M. and Gonz

alez-Izal, M. (2013). Simultaneous

powerline interference and baseline wander removal

from ECG and EMG signals by sinusoidal modeling.

Medical engineering & physics, 35(10):1431–1441.

BIODEVICES2015-InternationalConferenceonBiomedicalElectronicsandDevices

268