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OUTCOME OBJECTIVES:
Mobile computer assisted, beside
incentive spirometry can be used to
detect and calculate the frequency,
amplitude, and duration of inspiration.
METHODS
A clinical patient-centered model in an
academic research setting was used to
demonstrate that computer assisted
incentive spirometry could analyze the
frequency, amplitude, duration, and
calculated volume of inspiration. Sixty
inspiratory cycles were recorded from a
smartphone (n=30) and laptop computer
(n=30) utilizing an external microphone
and traditional incentive spirometer
(Voldyne 5000, Hudson RCI, Temecula,
CA). Multiple microphone-spirometer
positions were investigated. Recordings
were processed and analyzed utilizing
Matlab (R2012a, The Mathworks, Inc.,
Natick, MA, USA).
RESULTS:
All inspiratory cycles were captured and
recorded successfully. All smartphone
recorded inspiratory cycles did not
exceed 1.5kHz in broadband noise.
Increasing amplitude of inspiratory
sounds did not produce a significantly
increased flow over microphone. No
significant power difference was found
between the smartphone and computer
recordings when investigated 2-cm in
front of lips and immediately behind the
open-ended spirometry mouthpiece.
CONCLUSIONS:
Mobile, computer assisted recording and
analysis of inspiratory cycles is a low-
cost, and reproducible method for
detecting detailed characteristics of
inspiration. Future investigations of
portable and automatically implemented
signal processing may improve patient
care and post-operative recovery.
Utility of Mobile Computer Assisted Incentive SpirometryKaram W Badran, MD1; Chloe C. Krasnoff, BSc2; Zachary Taylor, PhD3; Maie A. St. John, MD PhD1,2,3
1Department of Head and Neck Surgery, UCLA Medical Center, Los Angeles, CA2David Geffen School of Medicine at UCLA, Los Angeles, CA
3Department of Bioengineering, UCLA, Los Angeles, CA
Sixty inspiratory cycles were recorded using the system
illustrated in Figure 1. Through an external microphone
(3.5mm EarPod) thirty samples were recorded using a
smartphone (iPhone 6s, Apple Inc., Cupertino, CA) and
the remainder using a laptop computer (MacBook Pro
2013). For each computing device, ten samples were
recorded at three locations from the user. Figure 1 (A)
illustrates a microphone 2cm away from the user’s lips,
(B) immediately behind the incentive spirometry, and (C)
within the spirometer’s chamber.
The dataset of audio samples were analyzed utilizing
Matlab (R2012a, The Mathworks, Inc., Natick, MA,
USA). The frequency domain representation of the
original signal was associated to a function of time by
use of Fourier transformation. The data graphed
represents noise amplitude, frequency, and duration.
INTRODUCTION
METHODS
RESULTS
ABSTRACT
Karam W Badran, MD
Department of Head & Neck Surgery
UCLA Medical Center
Email: [email protected]
CONTACT
DISCUSSION
Utilizing a microphone and headphone jack all
inspiratory cycles were successfully recorded and
analyzed (Figures 2-4). The frequency, amplitude, and
duration of inspiration were recorded. The maximum
recorded amplitude of noise identified through Fourier
transformation on iPhone (x=0.18 ± 0.02) was on
average less and differed significantly from those
recorded by computer (Figure 2, x=0.25 ± 0.02,
p=0.027) when recorded in front of the lips and when
placed inside the spirometer chamber (p<0.01). The
range of frequencies captured by computer (range: 0-
2kHz) at all microphone positions was wider than that
recorded by iPhone (Figure 3, range: 0-1.5kHz), however
all frequencies captured were less within the chamber
than when outside of the spirometer or within the
mouthpiece (Figure 4, p<0.01). Representative
measurements of air flow at 500 Hz, 1000 Hz, and 1500
Hz demonstrated variable amplitude at each microphone
position and method of recording.
REFERENCES
1. Overend TJ, Anderson CM, Lucy SD, Bhatia C, Jonsson BI, Timmermans C. The effect of
incentive spirometry on post- operative pulmonary complications: a systematic review.
Chest. 2001;120:971-978.
Smartphone computing allows for the efficient storage of
datasets readily available for computer-assisted analysis.
Our study demonstrates a low-cost and easily
reproducible system for recording inspiratory cycles.
Future work is required prior to clinical application.
Improvements include a frequency threshold and range
restriction in order to exclude extraneous noise (ie.
bedside alarms). Additionally, we expect that power and
flow would correlate with noise amplitude, a made more
significant in a reduced frequency range. With the
appropriate user interface, the smartphone incentive
spirometer has the ability to increase patient compliance,
reduce hospital costs, increase accessibility, and improve
patient care in the post operative setting.
Incentive spirometry is a near universal component of
medical and post-operative care. The goal of this
technique is to encourage patients to take deep and slow
breaths to assist in expansion of the lungs after surgery.
This is crucial as many individuals are at risk of atelectasis
secondary to pain and resultant shallow breathing.1
Conventional incentive spirometry is accomplished by the
use of a large device that provides the patient with a visual
feedback when they inhale for a minimum of 3 to 5
seconds for 5 to 10 consecutive cycles.1 The self-
administered device however, suffers from low patient
compliance. It is bulky, not user-intuitive, may induce
pain, and does not monitor patient use or progression.
Recently, advances in mobile and smartphone computing
have penetrated everyday personal and clinical practice.
With the increasing sophistication of software for these
devices, mobile, point-of-care detection of patient focused
goals are possible. Our study aims to assess the utility of a
smartphone assisted incentive spirometry system.
Figure 1. Workflow demonstrating a mobile and smart method to capture
inspiratory flow. A, B, and C indicate the three areas where flow past a
microphone are recorded
Figure 2. Recording from a MacBook Pro computer. The graphs above illustrate a single inspiration directly in front of the mouth. From left to right, the duration of
inspiration is graphed as a function of amplitude, frequency, and both.
Figure 3. Recording from an iPhone 6s. The orientation from left to right is the same as Figure 2. The middle graph illustrates frequency v. time. The hue increases in
intensity as amplitude increases for each frequency identified.
Figure 4. Recording on MacBook from within the incentive spirometry chamber demonstrates high amplitude though not in consistent frequency bands.