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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 12, December 2015)
Heart-Sound Monitoring Using a Smartphone
Divya Kundra1, Prerna Juneja2
1
Assistant Professor, Deen Dayal Upadhyaya College, Delhi, India
2
Software Engineer, EMC Corporation, Bangalore, India
As the mobile device technologies are developed into the
state of the art, the typical features set in a smartphone
include a CPU, a non-volatile storage, Wi-Fi and 3G
networking and a touch-screen-based interface. The
smartphone can record a variety of audio signals, including
heart sounds using microphone or electronic stethoscope.
Therefore, we develop a smartphone-based stethoscope
system that can record heart sound and calculate heart rate
from the sound. A smartphone is used in this system which
is portable, easy to use and accessible. So, it can be used in
various environments such as home health checkups,
education settings, operating rooms, ambulances, etc.
Abstract— Heart diseases associated with living conditions,
such as coronary heart disease and hypertension are
becoming more and more complex. If heart condition didn't
get long-term and real-time monitoring and diagnosis, it may
induce sudden heart disease. As the heart auscultation is one
of the most fundamental ways to evaluate the heart functions,
a stethoscope can be used to auscultate respiratory sounds
and heart sounds, and diagnose most of the cardiopulmonary
disorders and other diseases. Therefore, we develop a
smartphone-based stethoscope system for Android platform
that can record heart sound and calculate heart rate from the
sound. The application records heart sound using a
stethoscope with an embedded microphone in it on a real time
basis and displays the FFT (Fast Fourier Transform) of the
recorded heart sound in a graph. Also, the peak of the heart
sounds is identified on a real-time basis to determine the heart
rate, thereby quantifying the information related to the
cardiovascular system.
II. RELATED WORK
Due to smartphone’s mobility, connectivity and
processing capabilities it is uses in many areas of
healthcare [2]. Work has already been developed that
facilitates smartphone to allow patients to record and
monitor personal behavior related to asthma [3],
hypertension [4], diabetes [5]. Smartphone are even used as
sensors for physiological monitoring [6-8]. In addition, it
can record a variety of audio signals including heart sounds
with the help of microphone or electronic stethoscope.
Keywords—Auscultation, Cardiac Cycle, Fast Fourier
Transform (FFT)
I. INTRODUCTION
Heart disease is a major health problem and a leading
cause of fatality throughout the world. Treatment can be
easier and cheaper if the condition is detected early.
Auscultation is listening to internal sounds of body with a
stethoscope. Cardiac disorders that are valve related can be
detected efficiently and cheaply using auscultation [1].
Frequent and noninvasive cardiovascular monitoring is
important in the surveillance for cardiovascular
catastrophes and treatment of chronic disease. Heart
disease associated with living conditions, such as coronary
heart disease and hypertension is becoming more and more
complex. Heart diseases may occur if there is no long term
monitoring and diagnosis. When treatment is delayed and
the moment for optimal treatment is missed, it may even
result in unpredictable and grave consequences. As the
heart auscultation is one of the most fundamental ways to
evaluate the heart functions, a stethoscope can be used to
auscultate respiratory sounds and heart sounds, and
diagnose most of the cardiopulmonary disorders and other
diseases.
III. METHEDOLOGY
For cardiac examination, auscultation is considered the
most effective method due to its requirement of minimal
equipment [9]. Thus for small scaled primary health care
units, auscultation remains the primary and only means of
cardiac examination. Auscultation consists of two phases.
First phase is heart sound acquisition and second is heart
sound analysis [9]. Heart sound acquisition involves
obtaining the heart sound by putting the stethoscope at the
appropriate location on a patient’s chest with the right
amount of force. Determining whether the captured sound
belongs to a healthy or diseased heart is done in heart
sound analysis phase. The normal heart rate of a human
being lies between 60-100 bpm.
90
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 12, December 2015)
There are two events related to heart sounds in healthy
adults. The first type of heart sound is S1 and the second
type of sound is S2 as shown in Figure 1. S1 and S2 are
produced by the closing of the two valves namely
atrioventricular and semilunar valves respectively. S1 is
caused by the sudden block of reverse blood flow due to
closure of the atrioventricular valves. S2 is caused by the
sudden block of reversing blood flow due to closure of the
semilunar valve. Together S1 and S2 are referred to as
fundamental heart sound (FHS). A cardiac cycle or a single
heartbeat is defined as the interval between the beginnings
of S1 to the beginning of the next S1. The interval between
the end of S1 to the beginning of the same cycle’s S2 is
called systole and the interval between the end of S2 to the
beginning of the next cycle’s S1 is called diastole [9]. ,
Most heart sound segmentation methods are based on the
assumption that systole is shorter than a diastole which is
now taken as the general parameter for doing analysis.
Figure 2: Stethoscope With One End Connected To Smartphone
Figure 3: Steps In Proposed Heart Rate Analysis Algorithm
In order to detect the heart rate from heart sounds, the
accurate peaks need to be identified. Figure 2 shows the
algorithm proposed in this study for the purpose of the
detection and analysis of heart sound components, and it is
divided into the following 4 steps :- First the input audio
signal is recorded with a stethoscope and is transferred into
the mobile. Preprocessing of the input audio signal is done
by scaling its amplitude between the range of 1 to -1. To
reduce the number of computations, fast fourier
transformation (FFT) of scaled input signal is performed.
Then filtering using a low pass filter at 15Hz is done to
obtain the envelope of the signal. The filtering is used to
identify the peak, and components with peak time intervals
under a certain value are removed. The components with
time intervals under 0.1 second are regarded as noise. The
time intervals between the peaks detected during the
second procedure are used to determine S1, and the interval
between initial S1 and the next S1 is used to calculate the
heart rate using eq. (1) [10]
HR(n)=60/(tS1(n)-tS1(n-1))
Where tS1(n) denotes the detected value for the nth S1
time.
Figure 1: Normal Cardiac Cycle [9]
Our system has a stethoscope with an embedded
microphone is connected by one end to the smartphone.
The other end- diaphragm is placed at heart as shown in
Figure 2. It is designed to record the acquired data on a
real-time basis and display the FFT of heart sounds in a
graph on the mobile. Also, the peak of the heart sounds is
identified on a real-time basis to determine the heart rate,
thereby quantifying the information related to the
cardiovascular system.
91
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 12, December 2015)
For audio capturing the parameters settings are:
Encoding: AudioFormat.ENCODING_PCM_16BITS
Sampling rate: 8000
BufferSize: Sample rate*2*2*3
Channel: AudioFormat CHANNEL_IN_MONO
IV. RESULTS
Using the proposed approach, we develop an android
app. Figure 4 shows the graphical interface of the app. It
displays the FFT obtained of the heart sound, with varying
frequency on x axis. The app computes the heart rate, if the
heart rate lies in the normal range, message of person being
well is displayed as shown in Figure 5 else message
advising the patient to consult a doctor is displayed as
shown in Figure 6. The app is tested on 20 patients. Actual
heart rate was obtained by feeling the pulse on wrist for 15
secs. The number of beats so obtained is multiplied by 4 to
calculate beats per minute. The results obtained are shown
in Table 1.
V. LIMITATIONS AND FUTURE WORK
Due to the interference with the environmental noise and
improper functioning of the hardware (stethoscope), the
expected results were not achieved. More advanced
algorithms like normalized Shannon energy, homomorphic
filtering, complexity signature, energy of wavelet
coefficients could have been used. Out of the top three
peaks calculated, we are assuming first and third peaks to
be S1. Due to inadequate noise removal techniques used in
our algorithm, the S1 peaks are often being miscalculated.
We plan to send the recorded audio on a cloud where the
above mentioned advanced algorithms can be implemented
in MATLAB and the calculated heart rate could be send
back to the user.
Figure 4: Snapshot Of Graphical Interface Of The App.
92
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 12, December 2015)
Figure 6: Snapshot Of App When Heart Rate Not In Normal Range
Figure 5: Snapshot Of App When Heart Rate In Normal Range
93
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 12, December 2015)
Table I
Results Obtained
Person Id
Measured
heart rate
(bpm)
Actual
heart rate
VI. CONCLUSIONS
A smartphone-based stethoscope application has been
developed on Android platform. The application acquires
and records the heart sounds, and displays the recorded
sounds as FFT. Also, the heart rate is calculated and
displayed on a real-time basis. When the electric
stethoscope is used, the application can be used to provide
diagnosis or education. When used for educational
purposes, teachers and students can listen to the heart
sounds together and observe the heart sound wave shapes
to enhance the effectiveness of the education. By tightly
placing the internal microphones at their chests without any
extra sensors, individuals can use the device for selfassessment. Also, when using an external microphone and
an esophagus catheter together by connecting them, the
device can be used as an esophagus stethoscope in an
operation room or when transporting an emergency patient.
Using Bluetooth headset, anesthesiologist can hear the
patient' s heart sound, going around tens of meters range
freely in operating room. In the case of emergent patient' s
transfer, ambulance staffs can transmit the patient' s data to
the specialist in the emergency center using e-mail and can
consult about patient' s health status before they will arrive
at the hospital.
Error
percentage
(%)
(bpm)
(|measuredactual|) /
actual *100
1
70
80
12.5
2
60
63
4.7
3
103
90
0.1
4
90
120
25
5
110
130
15.3
6
75
65
15.3
7
78
64
21.8
8
75
90
16.6
9
60
80
25
10
80
90
11.1
11
90
91
1.0
12
95
100
5
13
120
100
20
14
84
90
6.6
15
65
60
8.3
16
82
78
5.1
17
83
85
2.3
18
85
90
5.5
19
87
100
13
20
90
120
25
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Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 12, December 2015)
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