New Visualization Model for Large Scale Biosignals Analysis

Catarina Cavaco

, Ricardo Gomes

, Hugo Gamboa

1,2

and Ricardo Matias

3,4

CEFITEC, Physics Department, Faculdade de Ci

encias e Tecnologias - Universidade Nova de Lisboa, Lisbon, Portugal

PLUX Wireless Biosignals, Lisbon, Portugal

School of Health Care, Polytechnic Institute of Set

ubal, Set

ubal, Portugal

Neuromechanics Research Group, Interdisciplinary Centre for the Study of Human Performance (CIPER),

Faculdade de Motricidade Humana - Universidade de Lisboa, Lisbon, Portugal

Keywords:

Long-term Biosignals, Big Data, Biosignals Visualization, Biosignals Annotation, Medical Monitoring.

Abstract:

The development of new resources in the medical ﬁeld, such as wearable sensors, allowed the improvement of

biosignals monitoring. Acquired data is then an important source of information to clinicians and researchers.

Thus, extracting useful information from data is a task of the greatest importance that involves a variety of

concepts and methods, from which stands out data visualization. However, these methods present several

limitations mainly when dealing with big data. In this paper we present an innovative web-based application

for biosignals visualization and exploration in a fast and user friendly way overcoming the detected limitations.

Three case studies are presented and a usability study supports the reliability of the implemented work.

1 INTRODUCTION

The technological innovation in medical systems has

been of the utmost importance in the monitoring im-

provement of human body signals, so-called biosig-

nals. There are several types of biosignals resulting

of the electrical, magnetic, chemical or mechanical

activity during biological events such as heart beat

or muscle activity. They can also be classiﬁed con-

sidering their nature, application or their characteris-

tics (Kaniusas, 2012). Biosignals monitoring can be

done through the use of non-invasive wearable sen-

sors which combined with systems allow the storage

of the data acquired. Relevant information can then

be extracted from this data to support clinicians and

researchers decision-making, as well as to inform pa-

tients. Therefore, to achieve the goal of extracting rel-

evant information from the data, a variety of concepts

and methods, such as data visualization, are involved.

Since humans main input sense is visual, data vi-

sualization is considered essential in signal analysis

(Aigner et al., 2011). The integration of the human

visual perception with the current massive computa-

tional capacities results in this concept which supports

the examining, understanding and transmitting of the

vital information carried by a signal (Iliinsky and

Steele, 2011). However, data visualization presents

some limitations that have to be considered. Besides

the computation capacity limited by the memory and

time to run an algorithm, the display is restricted

by the number of pixels available to show the data.

On the human side, the limitations comprise the hu-

man perceptual and cognitive capacities which can re-

sult in incorrect data interpretations both in time and

space (too fast or too dense for the correct perception)

(Munzner, 2009).

The demand for studies that result in large

amounts of data, such as sleep analysis or neuro-

muscular diseases monitoring, is increasing. In this

case, difﬁculties become evident. Dealing with mas-

sive sources of data, which can be considered big

data, increase the complexity of the problems de-

scribed before and its processing with traditional ap-

plications presents several limitations (Hudson and

Cohen, 2006).

Considering the extreme importance of biosig-

nals analysis and the outlined hurdles, the aim of

this work was to present an innovating solution for

large dataset analysis, what involves visualization and

annotation, in the context of biosignals. This was

achieved through the better conjugation between pro-

cessing and storage capabilities of computers and the

visual, creative and knowledge capabilities of hu-

mans. In this study we present a novel web-based ap-

plication for biosignals visualization and exploration

in a fast and user friendly way.

190

Cavaco C., Gomes R., Gamboa H. and Matias R..

New Visualization Model for Large Scale Biosignals Analysis.

DOI: 10.5220/0005207201900197

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2015), pages 190-197

ISBN: 978-989-758-069-7

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

The remainder of the paper is organised as fol-

lows. Section 2 reviews related work on visualization

and annotation of large datasets of biosignals. In sec-

tion 3, it is presented the application framework. Sec-

tion 4 refers to the developed visualization model and

Section 5 presents the case studies considered. Sec-

tion 6 presents the usability study carried out. Finally,

in section 7 the main conclusions are drawn and di-

rections for further research are given.

2 RELATED WORK

The high concern in clinical systems for improving

the physical and psychological wellness has resulted

in the advent of crucial systems speciﬁcs for biosig-

nals. The PhysioNet (Goldberger et al., 2000) of-

fers free web access to a large biosignals databases

that can include ground-truth information, and it also

comprises a wide collection of software for viewing

and analysing biosignals. The OpenSignals software

enables the visualization and analysis of the biosig-

nals acquired by a wearable hub that along with this

software constitute the biosignalsplux system (PLUX,

2014), an advanced biosignal monitoring platform for

sports and biomedical research. The ActiLife6 is the

visualization software that integrates the ActiGraph

system (ActiGraph, 2012), the most used actigraphy

monitoring system in research and clinical trials in-

volving physical activity and sleep assessment. In

cardiology, one of the most common examinations is

the Holter, an ambulatory Electrocardiography (ECG)

for a minimum 24-hour period, conducted with the

purpose of screening for ECG disturbances. The

Welch Allyn Holter System (Welch Allyn, 2007) is

one of the available systems to perform Holter exam-

inations. Notwithstanding the high development of

visualization tools, they face yet some issues, partic-

ularly when dealing with big data. The possible inte-

gration of applications in real life platforms to mon-

itoring diseases increases the demand for novel solu-

tions.

3 APPLICATION FRAMEWORK

3.1 System Requirements

The development of the visualization application took

into account some base requirements. Therefore, the

developed application had to:

• be applied to all type of biosignals

• enable the possibility to explore up to 10 days of

continuous acquisitions

• show the time lapses where the signal acquisition

was interrupted

• allow the handling of annotations

• present a fast and user friendly interface

Lastly, the proposed model had to represent a

commitment between usability and performance, al-

lowing the user to analyse a biosignal without having

to deal with signal processing algorithms directly.

3.2 System Architecture

The implemented visualization tool is a web-based

platform. This decision is justiﬁed by the fact that

the web standards currently available provide some

of the best tools for the creation of rich graphical user

interfaces and it also eliminates complex installation

and conﬁguration procedures. A client-server model

was developed. The local server was implemented in

Python language (Beazley, 2009) and the communica-

tion between the visualization platform and the server

is done with WebSockets. The ﬂow of information is

schematically represented in Figure 1.

Visualization

Platform

request

response

Processing

Algorithms

server

Figure 1: Information Flow. The client sends request mes-

sages to the server which responds by serving a message

containing the requested information. The generated re-

sponses are then interpreted by a series of JavaScript li-

braries and the results dynamically displayed on the page.

Considering the possible client’s requests, it was

implemented different Python Application Program-

ming Interfaces (APIs). Through the use of Python

language to access and manipulate the data and

JavaScript language (Duckett, 2014) to present the

data and deal with the user-interface tasks, a highest

performance is reached.

3.3 Data Architecture

The manipulation of very large amounts of data must

involve suitable data format for storage and access

this data. Although presenting some limitations, the

Hierarchical Data Format 5 (HDF5) has proven to be

the best option for the intended task (Gomes et al.,

2012). Before submitting a record ﬁle to the visual-

ization tool it should be previously processed. The

processing API involves ﬁltering, subsampling, and

NewVisualizationModelforLargeScaleBiosignalsAnalysis

191

events detection. As a result, different groups of data

are created in the HDF5 ﬁle. The previous compu-

tation of some variables will speed up the visualiza-

tion since the APIs previously referred only access the

data instead of computing it.

4 VISUALIZATION MODEL

A generic model for visualization is proposed in

(Van Wijk, 2005) and schematically represented in

Figure 2.

Data

Image

Visualization

Specification

Visualization

Perception

Exploration

Knowledge

User

Figure 2: Simple model of visualization. In this model

the central process is the visualization where data is trans-

formed into a time varying image according to a speciﬁ-

cation. Then, the resulting image is perceived by a user,

leading to an increase of users knowledge. Finally, the in-

teractive exploration of the image enables to adapt the pre-

vious speciﬁcation based on the current knowledge in order

to further explore the data. Adapted from (Van Wijk, 2005).

4.1 Visualization Process

4.1.1 Domain Problem Characterization

As already stated, the beneﬁts of long-term monitor-

ing (more than 24 hours of acquisition) have drawn

considerable attention in healthcare. In (Jung et al.,

2012) and (Goya-Esteban et al., 2009) are presented

two examples of 7 days of continuous measurements

of electrical biosignals such as ECG and electromyo-

graphy (EMG).

However, dealing with the analysis of massive

sources of information remains a challenge that has

to be overcome. For example, displaying the 24 hours

of an ECG signal recorded with a modern Holter

recorder, which uses sampling rates of 1000Hz or

higher (Hilbel et al., 2008), results in a falling attempt

of drawing more than 80 million data points. Since a

standard computer’s screen has only some thousands

of available pixels (Pakhira, 2010), displaying the pre-

viously mentioned signals not only exceeds the capa-

bilities of the visualization device, but also results in

a massive time and memory consuming rate. Even

if the visualization is possible, the representation of

such a large amount of data points will surpass the

human perceptual capacity resulting in incorrect in-

terpretations of the data both in time and space.

4.1.2 Abstraction Design

After a speciﬁc domain problem has been identiﬁed,

it has to be abstracted into a more generic represen-

tation. Despite not always being performed, it is an

essential process when dealing with massive data sets.

The created abstraction took into account that

biosignals only contain crucial information in speciﬁc

time intervals - in cyclic or sporadic events. There-

fore, considering only these events, it is possible to

highlight the key information in the biosignal and to

suppress the irrelevant details. Furthermore, this will

support the variability analysis in a physiological net-

work. (West, 2013) states its importance. As a result,

considering only the portions of the signal which con-

tain important information, instead of the whole sig-

nal, considerably reduces the number of data points to

plot.

4.1.3 Visualization in Layers

The proposed model comprises a multi-level visu-

alization architecture. This architecture enables the

easy search and focus in the interest regions through

a simple navigation in the biosignal. While the ﬁrst

layer displayed gives the user a global overview of the

whole biosignal, the others provide a more detailed

visualization of the selected interval of the above

layer (by default the selected interval is the ﬁrst one).

In the application there are seven standard infor-

mation layers divided into a deﬁned number of inter-

vals. The choice of the standard information layers

had in consideration the system requirements and the

fact that each layer should be divided into an integer

number of intervals with the size of the layer imme-

diately below. The chosen layers, their number of in-

tervals and type of data representation that each layer

represents are described in Table 1.

The total number of information layers displayed

in the platform depends on the total duration of the

biosignal that is being analysed. The ﬁrst layer to be

displayed corresponds to the lowest layer that can rep-

resent the whole signal. All the layers below this one

will also be displayed. Figure 7 ilustrates this case;

the signal considered only has 20 hours of record,

then the ﬁrst layer to be displayed was the 1 day layer.

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Table 1: Model Layers.

Layer

Number of

Intervals

Data

Representation

10 days 10 Events

1 day 24 Events

1 hour 4 Events

15 minutes 3 Events

5 minutes 5 Subsampled

1 minute 60 Subsampled

1 second 1 Raw

4.1.4 Visualization Techniques

Besides line and bar plots, two standard techniques

in data visualization, horizon plots, were also con-

sidered. An horizon plot is a stacked graph that en-

ables the performance comparison of a large num-

ber of time-dependent variables. It is built by adding

color bands to a line graph and mirroring the negative

values respecting the x-axis. Thereafter, using a tech-

nique called two-tone pseudo coloring the color bands

are overlaid in the graph. This technique is then inte-

grated with the small multiples technique which en-

ables the display of a series of small graphs stacked

one above the other (Heer et al., 2009). The main in-

terface combines only the line and bar plots to show

the data selected. While the bar plots are used for the

1 hour layers, the line plots are used for the remain-

ing ones (Figure 7). The horizon plots are used in

the analysis page, later discussed, enabling the com-

parison of a variety of features in a restricted space

(Figure 4).

The visualization elements are rendered in Scal-

able Vector Graphics (SVG) which are more reliable

and ﬂexible than the HTML canvas elements (Murray,

2013). The Web Graphics Library (WebGL) elements

were also considered. Despite providing a similar ren-

dering functionality in a faster way due to its interac-

tion with the graphics processing unit, the toolboxes

currently available to perform this type of rendering

are still too complex or not stable enough to provide

a suitable visualization.

4.2 The Perception and Cognition

Process

The graphical perception refers to the ability to de-

code the information encoded on graphs (Heer et al.,

2009). Therefore, in order to enhance the graph-

ical perception some variables, such as positions,

shapes and colours, were carefully assessed. The plat-

form also includes a fast and responsive design and

thoughtful default colours.

4.3 The Interactive Exploration Process

The direct interaction with the data enables the user

to focus on some details according to his objectives.

Basic interaction techniques are often used to im-

prove the visualization exploration and for this reason

they are familiar to the majority of the users. The

choice of these techniques comprised the zoom and

pan techniques, the use of tooltips and the presence

of modal dialogs to alert or guide the user. It also

enables the saving of the exploration carried out.

4.3.1 Annotations

The annotation of the biosignals is a demanding task

that depends on human subjective intervention and re-

quires speciﬁc knowledge. Therefore, the developed

platform enables the annotations handling, thereby

improving the biosignal interpretation through the

notes made by the user in the relevant biosignal re-

gions. Figure 3 illustrates the presence of annotations

in the platform.

4.3.2 Further Analysis

The choice of key signal features can be a fundamen-

tal method to discover important information hidden

on the signal. Therefore, in the developed application,

it is possible to request for a more detailed analysis of

some layers (Figure 4). The features selected to anal-

yse a speciﬁc layer depend on the biosignal that is be-

ing analysed, however they are grouped in the same

domains: temporal, statistical and spectral.

5 CASE STUDIES

5.1 Data Acquisition and Processing

The acquisition system consisted in a set of biomedi-

cal sensors, a wireless acquisition unit and a smart-

phone. A surface EMG sensor at the gastrocne-

mius muscles of the right leg, a triaxial accelerometry

(ACC) sensor at the right hip, and a 3 lead ECG sen-

sor at the left side of the chest were used along with

the bioPLUX research system. The biosignals were

acquired with a sampling frequency of 1000 Hz and

with a 12 bit resolution and the data was sent via Blue-

tooth to the smartphone, saved in a text ﬁle and then

converted to an HDF5 ﬁle. Three acquisitions were

carried out in a home atmosphere performing daily

living activities. In order to provide large ﬁles to use

in the developed tool, the last signals were replicated

or combined. The ECG and the ACC signals were

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Figure 3: Navigation and Annotations. The selection of the sixth interval in the 1 day layer, i.e. the sixth hour of that day, will

switch the data in the 1 hour layer in order to correspond to the data of the sixth hour. Annotations are visually represented

in the platform by a timestamp placed at the beginning of the annotation time frame in a division below the x axis. When the

timestamp is clicked, it shows its message and a rectangular grey region highlighting the time frame of the annotation arises

in the graph. Therefore, the 1 day layer presents three annotations (one highlighted), however only the second one belongs to

the interval displayed at the 1 hour layer.

replicated until achieve a duration of 10 days, in the

ECG case, and 2 days 9 hours 32 minutes and 55 sec-

onds, in the ACC case. The EMG signal consisted

in the combination of the two EMG acquisitions with

intervals where the acquisition was interrupted, what

resulted in 6, 4 and 6 hours of acquisition intercalated

with 3 hours and 30 minutes without acquisition.

5.2 Electrocardiography

The ECG signal is a cyclic signal that consists in the

recording of the electrical activity of the heart. There-

fore, it provides a fundamental way of cardiac moni-

toring allowing the detection of cardiac abnormalities

(Kaniusas, 2012).

The abstraction for the ECG signal was performed

through the consideration of the R peak in the QRS

complex, one of the most important to analyse in an

ECG waveform (Neophytou et al., 2012). Although

the main event to consider is the positions where these

peaks occur, it was also computed another event, the

number of peaks that occur per minute (the heart rate).

5.2.1 Features Selection

The selection of features to provide a further analysis

of the ECG signal was done considering a derivation

of the main event above considered. The Heart Rate

Variability (HRV) represents the variation of the in-

tervals between heartbeats (RR intervals) and its stan-

dard features provide a vital source of signal informa-

tion.

5.2.2 Results

Figures 5 and 6 represent the result of one possible

analysis of the ECG signal considered. Through the

zoom, pan, the creation of annotations and the use

of tooltips was possible to ﬁnd a probably artefact

caused by the chest muscles contraction. The dou-

ble clicking in the graphs allow to switch the data in

the layers below until the desired event be displayed.

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Figure 4: Further Analysis Result of the 15 minutes layer of an EMG signal.

Figure 5: ECG signal analysis result.

5.3 Electromyography

An EMG signal is an electrical signal generated dur-

ing a muscle contraction. Therefore, it enables the

quantiﬁcation of the neuromuscular function and for

this reason these signals are mostly used to measure

the degree of muscle activation and to access the neu-

Figure 6: Continuation of the previous Figure.

rophysiologic mechanisms of fatigue (Arjunan et al.,

2011).

The event with most importance in the EMG anal-

ysis is therefore the signal activation (onset and off-

set times). However, the EMG signal is also usually

analysed by the root mean square value (Soares et al.,

2013).

5.3.1 Features Selection

The further analysis of the EMG signal is performed

by the zero crossing rate, the short and long diame-

ters of a Poincar

e plot, the standard deviation and the

median frequency features.

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5.3.2 Results

Figure 7 presents the ﬁrst layer of the EMG signal

considered. The analysis of this layer enables to get

the general intervals where there was or not acqui-

sition and the inactivity hours where the muscle in

study did not perform any contraction.

Figure 7: EMG signal analysis result (ﬁrst layer).

5.4 Accelerometry

The ACC signal provides the measurement of the

applied acceleration acting along a reference axis.

Therefore, its analysis provides crucial information

that can be used in functional status and monitor

falling studies (Lan et al., 2012), sleep analysis (Acti-

Graph, 2012) and in neuromuscular diseases diagno-

sis (Machado et al., 2014).

The abstraction for the ACC signals was settled

considering that the signal represent different activ-

ities performed by the subject. These activities can

be classiﬁed as static states and dynamic states (Acti-

Graph, 2012). These two states are usually distin-

guish by the Signal Magnitude Area (SMA) feature.

When the SMA value exceeded a preset threshold

thus the subject is classiﬁed as being in a dynamic

state (Lan et al., 2012; Carus et al., 2013).

5.4.1 Features Selection

The selected features to perform the further analysis

of the ACC signal were the autocorrelation, the root

mean square, the mean, the standard deviation and the

median and fundamental frequencies (Machado et al.,

2014). These features were computed with the total

acceleration signal.

5.4.2 Results

The result of the ACC signal analysis is similar to the

EMG signal, however instead of activations, here we

present the idea of static or dynamic states.

6 USABILITY EVALUATION

In order to assess the usability of the developed

work, a study with the System Usability Scale (SUS)

(Brooke, 2013) was carried out.

To the date the usability study performed included

10 participants with ages between 23 and 28 years

who had no experience with the application. All the

participants belonged to the ﬁnal target of users of

the developed tool - health science professionals. The

participants were instructed to use freely the system,

opening a ﬁle and exploring it with the available in-

teraction tools. The average score of the performed

tests was 80/100 what indicates a good system with

a high perceived usability. In the end of the test the

majority of the participants agreed that some basic

concepts were needed in order to perform a suitable

analysis, however, these concepts should already be

known by the ﬁnal user. They also agreed that a nec-

essary guidance should be provided at the ﬁrst use of

the system, still, it would not be necessary after this

ﬁrst use. Nevertheless, the participants stated that the

system is intuitive, useful and suitable, they also indi-

cated that they were likely to recommend it.

7 CONCLUSIONS AND FUTURE

WORK

This work introduces a novel visualization inter-

face which allows the professionals, who work with

biosignals, to get insight into the large data sets ac-

quired by the enhanced monitoring systems that have

been introduced in the last years. The visualization

model followed includes a web-server, a multi-level

layer layout, an abstraction approach and the imple-

mentation of different visualization techniques. Dif-

ferent explorative interaction techniques were also de-

veloped in which stands out the handling of annota-

tions. Three case studies were presented and several

performance tests were done. Finally, an usability

evaluation was carried out in which an average score

of 80 was achieved, which indicates a good usability

performance.

Despite the developed application has proven to

be a suitable approach, further research can be per-

formed.

We propose the use of more case studies. De-

spite of the considered signals represent the two pos-

sible types of biosignals events, cyclic and sporadic,

the consideration of another set of biosignals such as

electroencephalogram or respiration signals will fur-

ther validate the developed work enabling its use in

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more research studies. The search for WebGL ren-

dering tools should be carrying on and when possible

these elements should replace the implemented SVG

elements. Despite the SUS provides a conﬁdent mea-

sure of the perceived usability with a small group of

participants, it was carried out only with biomedical

engineers. Hence, this study should also be performed

by doctors. The integration of the processing in the

acquisition system would provide a real time process-

ing which would reduce the time spent in processing.

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