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Department of Biomedical Engineering
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Biomedical Signal Processing Lab.
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Yaniv Zigel, Ph.D.
Head,
BSP Lab.
Lecturer,
Department of Biomedical Eng.
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Email: yaniv@bgu.ac.il
Tel.: +972-8-6428372
Fax.: +972-8-6428371
Research
Biomedical signal processing has become a necessary tool
for extracting clinically significant information hidden in physiologic
signals. This information can be the physiological state of a patient or even
their psychological state in some cases.
The vision of our research is to advance the development
of computer based diagnostic and monitoring systems which offer fully automated
analysis. These diagnostic systems can help the physician in making well
founded decisions and reduce the subjectivity of manual measurements and
decision making process. Automated monitoring systems can save life in
real-time situations in cases such as apneas and cardiac failures.
The research activity at the Biomedical Signal Processing Research Lab. is concentrate on bioelectrical signals such as the
electrocardiograms (ECG) and electroencephalogram (EEG), and physiological
acoustic signals such as snore sounds, cough sounds, phonocardiogram (PCG) and
voice/speech signals. In these research activities, new
methods of signal processing and pattern recognition that extract useful
information from the physiological signals are being
developed.
The research activities in the last four years can be divided into four main research areas:
1. Sleep
research: ¨
Recognition and classification of snoring sounds for obstructive sleep apnea
(OSA) diagnosis.
¨ Identifying sleep apnea patients from their
speech signals,
¨ Analyzing EEG signals from OSA patients.
2. Heart
research: ¨ Identifying atrial electrical activity in
arrhythmias using ECG signals.
3. Smart
medical home: ¨ Fall
detection system of elderly using floor vibrations and
sound,
¨ Development of a cough detection monitoring
device,
4. Speech
research: ¨ Age estimation from speech signals,
¨ Robust
speaker recognition to reverberant speech,
¨ Room volume estimation from speech signals.
Sleep Research – Analysis and Diagnosis of Obstructive Sleep Apnea
Obstructive sleep apnea
(OSA) is a highly prevalent disease in
which upper airways collapse during sleep causing a complete or partial breath
cessation. One of the foremost consequences of these breathing pauses is a
major fragmentation of sleep, causing a marked deterioration of sleep quality.
The main complaints of OSA patients are snoring and excessive daytime
sleepiness, which result in a substantial decrease in life quality. Untreated
OSA is a major risk factor for both chronic and acute conditions such as
cardiovascular events and even sudden death.
The gold standard for OSA assessment is the
polysomnography (PSG) in which patients are connected to numerous sensors. The
cost of PSG and the complexity of the study motivate further development of
alternative approaches for screening subjects at risk for OSA. OSA severity is
defined by the number of obstructive apnea and hypopnea events per hour of
sleep (apnea hypopnea index – AHI).
Snoring, caused by the vibration of soft tissues in
the upper airways (UA) is one of the common symptoms of OSA. Snoring has long
been viewed as a potential indicator for monitoring OSA, and it is especially
advantageous when using a non-contact microphone
as the screening tool. However, as of yet, it has not been properly exploited
in diagnosis. Similar to the vocal tract in speech production, the upper
airways acts as a variable acoustic filter in the generation of snoring sounds.
Patients with OSA commonly have narrower and more collapsible UA then non-OSA
subjects. Therefore, it is expected that the
acoustic characteristics of snores from OSA and non-OSA subjects will be
different. In this research we hypothesize that the snore signal carries
important information about the OSA condition.
Our group has suggested a system that analyses the
entire nocturnal audio signal and combines several developed acoustic features
with a classifier in order to classify subjects as either OSA snorers or benign
(non-OSA) snorers [Ben-Israel, Tarasiuk, and Zigel, 2010]. OSA patients were
recruited prospectively and consecutively at the Sleep-Wake Disorders Unit,
Soroka Medical Center (Head of the unit: Prof. Ariel Tarasiuk). The
results that were achieved using 60 subjects with different OSA severities were
very good; the obtained sensitivity (specificity) was 96.5% (90.6%) for
resubstitution and 87.5% (82.1%) for cross validation (CV), where sensitivity
and specificity stands for correct detection of OSA and non-OSA subjects,
respectively. A patent for this system has been submitted [Zigel, Tarasiuk, and
Ben-Israel, 2010]. Recently, our research was granted by the Israeli chief
scientist, Ministry of Industry, Trade & Labor [KAMIN, 2011-2013].
Since there are studies that have confirmed that OSA
is associated with anatomical and functional abnormalities of the upper airway,
and since speech signal properties are also influenced by the anatomical
structure of the upper airway, we investigated the correlation between OSA and
speech signals [Zigel, Tarasiuk, and Goldshtein, 2008]. Using speech signal
processing techniques, we explored acoustic speech features of 93 subjects who were
recorded using a text-dependent speech protocol and a digital audio recorder
immediately prior to polysomnography study. Following analysis of the study,
subjects were divided into OSA (n=67) and non-OSA (n=26) groups. A Gaussian
mixture model (GMM)-based system was developed to model and distinguish between
the groups; discriminative features such as vocal tract length (VTL) and linear
prediction coefficients (LPC) were selected using feature selection technique.
Specificity and sensitivity of 83% and 79% were achieved for the male OSA and
86% and 84% for the female OSA patients, respectively [Goldshtein, Tarasiuk,
and Zigel, 2011].
Another
study was performed by our group in this area of OSA. We analyzed EEG signals
in order to estimate slow-wave activity (SWA), a marker of sleep homeostasis,
in children with OSA before and after adenotonsillectomy (TA) compared with
untreated OSA children (comparison group) [Ben-Israel, Zigel, et al., 2010].
The conclusions were:
1) sleep
homeostasis is considerably impaired in pre-pubescent children with OSA, and
2) adenotonsillectomy restores more physiological sleep homeostasis in children
with OSA.
Heart research - Identifying atrial electrical activity in
arrhythmias using ECG signals
In many arrhythmias,
extraction of atrial activity or cancellation of ventricular activity can be a
great help for the physician who tries to identify the type of arrhythmia from
ECG signals. In many cases, the P wave is hidden in a QRS complex or in a T
wave, and cannot be observed. Our group (in cooperation with Prof. Amos Katz)
developed an algorithm for ECG signal separation and p-wave detection that can
make the arrhythmia-type decision more convenient [Weissman, Katz, and Zigel,
2009]. A patent for this algorithm has
been submitted [Katz, Weissman, and Zigel, 2010].
In
another research, performed in cooperation with Dr. Yoram Etzion, we designed an algorithm to
detect atrial electrical activity and refractoriness of rodents based on the
surface ECG signal [Zigel, Mor, et al.,
2009]; this algorithm can advance the tachycardia research
and clinical applications in animal models and human.
Speech Research
- Speaker Age Estimation from Speech Signals
Human age (speaker age) is a part of non-verbal
information of a speech session that recently gained increasing importance in
improving speech-based applications. For interactive voice response (IVR) systems,
age information helps to adapt it to the user and can give more natural
human-machine interaction; this can help children as well as elderly in various
applications. Our research was granted by the Israeli chief scientist, Ministry
of Industry, Trade & Labor [MAGNETON, 2008-2009] for cooperation with
PuddingMedia LTD. We analyzed speech signals from different subjects in order
to develop an automatic system that can estimate the speaker's age. Different
aspects were examined and developed, acoustic features, statistical classifiers
[Porat, Lange, and Zigel, 2010], optimization techniques [Dobry, Hecht, Avigal,
and Zigel, 2009] [Dobry, Hecht, Avigal, and Zigel, 2010] and hybrid approaches
[Hecht, Hezroni, Manna, Aloni-Lavi,
Dobry, Alfandary, and Zigel, 2009].
Speech
Research – Robust Speaker Recognition and Room-Volume Estimation
This research was performed in cooperation with Prof.
Boaz Rafaely from the department of Electrical and Computer Eng, BGU. The
influence of acoustic reverberation was researched in order to develop a robust
speaker recognition system and a system for estimation of the volume of a room.
The research was granted, partially, by the Israeli chief scientist, Ministry
of Industry, Trade & Labor [MAGNETON, 2006-2007] for cooperation with NICE
Systems LTD (Zigel was a PI in the first year of the MAGNETON from NICE systems
side). Different aspects were examined and developed. We investigated the
effect of reverberation on the optimal GMM order of speaker recognition systems
[Shabtai, Zigel, and Rafaely, 2008a], and on feature normalization [Shabtai,
Zigel, and Rafaely, 2008b] [Shabtai, Rafaely, and Zigel, 2011] [Shabtai,
Rafaely, and Zigel, 2010]. A robust speaker recognition system was developed
[Peer, Rafaely, and Zigel, 2008a] [Peer, Rafaely, and Zigel, 2008b] [Peer,
Rafaely, and Zigel, 2008c]. A novel system for room-volume estimation was
developed; a system based on room impulse response (RIR) parameters [Shabtai,
Zigel, and Rafaely, 2009a] [Shabtai, Zigel, and Rafaely, 2009b] [Shabtai,
Zigel, and Rafaely, 2010a], and a system based on speech features [Shabtai,
Zigel, and Rafaely, 2010b].
Smart Medical
Home - Fall Detection System of Elderly
Falls are a major risk for elderly people living
independently. Rapid detection of fall events can reduce the rate of mortality
and raise the chances to survive the event and return to independent living. In
the last two decades, several technological solutions for detection of falls
were published, but most of them suffer from critical limitations. This
research was performed in cooperation with Prof. Israel Gannot from the
department of Biomedical Eng, TAU. In this research, we presented a proof of
concept to an automatic fall detection system for elderly people [Litvak,
Zigel, and Gannot, 2008a] [Litvak, Zigel, and Gannot, 2008b]. The system is
based on floor vibration and sound sensing, and uses signal processing and
pattern recognition algorithm to distinguish between fall events and other
events. The classification is based on special features like shock response
spectrum and mel frequency cepstral coefficients. For the simulation of human
falls we have used a human mimicking doll: “Rescue Randy.” The proposed
solution is unique, reliable, and does not require the person to wear special
devices. It is designed to detect fall events in critical cases in which the
person is unconscious or in a stressful condition. After testing the proposed
system [Zigel, Litvak, and Gannot, 2009], we concluded that it can detect human
mimicking doll falls with a sensitivity of 97.5% and specificity of 98.6%. A
patent for this system has been submitted [Gannot, Litvak, and Zigel, 2009].
To Yaniv Zigel's homepage | To BME homepage
People
Dr. Yaniv Zigel,
Head
Ilan
Levy, B.Sc., Lab.
Eng.
Eliran
Dafna, M.Sc. student
Oren
Elisha, M.Sc. student
Or
Perlman, M.Sc.
student
Yaniv
Tocker, M.Sc. student
Jenny
Goldshtein, M.Sc.
Noam
Weissman, M.Sc.
Roy
Porat, M.Sc.
Dan
Lange, M.Sc.
Nir
Ben-Israel, M.Sc.
Ahmad
Erew, B.Sc.
Marwan
Abu Leil, B.Sc.
Sara
Seidel, B.Sc.
Mirit
Levy, B.Sc.
Dana
Weiss, B.Sc.
Elinor
Tzvi, B.Sc.
Haim
Cohen, B.Sc.
Hila
Beharav, B.Sc.
Nadav Yaniv, B.Sc.
Vadim Zviling, B.Sc.
Ofer Gil, B.Sc.
Hagar Shushan, B.Sc.
Hen Barades, B.Sc.
Research students from other labs under Yaniv Zigel supervision:
Noam
Shabtay, Ph.D. (with
Dr.
Itai
Peer, M.Sc. (with Dr.
Dima
Litvak, M.Sc. (with
Dr.
Gil
Dobri, M.Sc. (The
Open-University)
Past and Present Cooperation
Prof.
Dr.
Yoram Etzion, Cardiac
Arrhythmia Research Laboratory,
Prof.
Prof.
Boaz Rafaely,
Electrical and Computer
Prof.
Dr.
Jacob Urkin, Clalit
Health Services.
Dr.
Gallya Gannot,
Opticul Diagnostics Ltd.
Dr.
Moshe Ben-David,
Opticul Diagnostics Ltd.
Links
Physiobank: http://www.physionet.org/physiobank/database/
Conferences: http://www.cinc.org/
To Yaniv Zigel's homepage | To BME homepage