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OSA Screening Test at the Patient’s Home



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LEEP apnea (SA) in the form of Obstructive sleep apnea
(OSA) is becoming the most common respiratory
disorder during sleep, which is characterized by cessations
of airflow to the lungs. These cessations in breathing must
last more than 10 seconds to be considered an apnea event.
Apnea events may occur 5 to 30 times an hour and may
occur up to four hundred times per night in those with severe
SA [1]. Nowadays, polysomnography (PSG) is a standard
testing procedure to diagnose OSA which includes the
monitoring of the breath airflow, respiratory movement, and
oxygen saturation (SpO2), body position,
electroencephalography (EEG), electromyography (EMG),
electrooculography (EOG), and electrocardiography (ECG).
Therefore, a final diagnosis decision is obtained by means of
medical examination of these recordings [2]. However, new
simplified diagnostic methods and continuous screening of
OSA is needed in order to have a major benefit of the
treatment on OSA outcomes.
In this work, we propose to precisely monitor the patient
at home in terms of breathing movements and alert the
user/patient whenever the acoustic signal of respiration is
paused. The overall design involves acquiring sound from a
microphone. This signal is then processed to detect
abnormalities in breathing or breathing cessations.
Moreover, as a secondary system, image data is continually
collected from the environment through webcam. When an
apnea event occurs, that is, if the patient is without breath
for longer than 15 seconds, and the conditions of
surrounding environment is changing, an alarm will be
raised immediately.
The overall prototype of our apnea detection system
follows. Breathing sounds are recorded using a small
microphone integrated with a large stethoscope-like
diaphragm in place with a soft band that is fastened gently
around the patient’s neck. During the initialization phase,
three main modules are initialized simultaneously; the peak
programme meter (PPM), the analyzer and the detector.
The initial sound data is recorded through a signal recorder
using the peak programme meter (PPM). This allows us to
have the first raw information about the patient’s breath
cycles and the peak threshold values for an apnea attack.
The PPM values are then sent to a filter to remove the noise.
Manuscript received July 21, 2012. Authors are with the Department of
Computer Science and Engineering, University of Bridgeport (UB),
Bridgeport, CT 06604, USA (e-mails: {lalmazay, elleithy, vpande,
mfaezipo} M. Faezipour (corresponding author) is the
director of the Digital/Biomedical Embedded Systems and Technology Lab
at UB; e-mail:; phone: 203-576-4702.
Another copy is also sent to the detector as a secondary data
for detection (system fail trigger) of sleep apnea. The
breathing sounds from the microphone are then passed
through the analyzer to develop a breathing classifier. Here,
a graphical user interface of the sound wave can be used to
adjust the patient’s breath cycle, setting a pattern for regular
breathing and a pattern for irregular breathing. Both these
patterns are sent through a filter to remove the noise. The
final step and the third simultaneous process is the detector
module. This module receives values from either the PPM in
case a classifier for a patient is not set (our system fail
trigger), or receives the primary value from the analyzer
module. If the threshold values are exceeded, an alarm is
raised. A video monitoring tool that judges the patient’s
presence is also used to avoid false positives. The detection
process would check for apnea attack for a time period of
thirty seconds, then the whole detection process will run
again with the same classified data values.
Fig. 1. The SA detection framework graphical screenshot.
The complete apnea detection system was implemented in
Visual C#. The Olympus ME-52W noise canceling
microphone to capture breath and a standard web-camera
with 60/sec to capture video frames were used. We also
developed a graphical user interface for the sound analyzer
module, where the user can study and classify the breathing
cycle of the patient. The graphical results screen illustrates
the results of SA detection from the abnormal breathing and
movement detection in real time (Fig. 1). This allows for a
comfortable, continuous and real time monitoring of sleep
apnea disorder at home, which can trigger an alarm if the
apnea is detected.
[1] Sleep Disorders Guide.
[2] L. Almazaydeh, K. Elleithy, and M. Faezipour, “Detection of
obstructive sleep apnea through ECG signal features,” in Proc. of the
IEEE EIT Conference, pp. 1-6, May. 2012.
OSA Screening Test at the Patient’s Home
Laiali Almazaydeh, Khaled Elleithy, Varun Pande, and Miad Faezipour, Member, IEEE
1st Annual IEEE EMBS Micro and Nanotechnology in Medicine Conference
Ka’anapali, Hawaii USA, 3 - 7 December, 2012
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Conference Paper
Full-text available
Obstructive sleep apnea (OSA) is a common disorder in which individuals stop breathing during their sleep. Most of sleep apnea cases are currently undiagnosed because of expenses and practicality limitations of overnight polysomnography (PSG) at sleep labs, where an expert human observer is needed to work over night. New techniques for sleep apnea classification are being developed by bioengineers for most comfortable and timely detection. In this paper, an automated classification algorithm is presented which processes short duration epochs of the electrocardiogram (ECG) data. The automated classification algorithm is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA. The results show that our automated classification system can recognize epochs of sleep disorders with a high degree of accuracy, approximately 96.5%. Moreover, the system we developed can be used as a basis for future development of a tool for OSA screening.