Detection of Wheezes using A Wearable Distributed Array of Microphones

Wee Ser, T. T. Zhang, Jufeng Yu and Jianmin ZhangCenter For Signal Processing

Nanyang Technological UniversitySingapore

ewser@ntu.edu.sg, ttzhang@ntu.edu.sg, jfyu@ntu.edu.sg, zhangjm@ntu.edu.sg

Abstract—This paper presents a wheeze detection methodthat uses a distributed array of microphones and can beimplemented as part of a wearable health monitoring system.In order to reduce the power consumption for the wearablesystem, the method has been developed to operate at a samplingrate of 1000Hz, instead of 8000Hz. In the design, we use tworegular air conductive microphones and a bone conductivemicrophone to increase the accuracy of detection and makeit robust against environmental noise. The two air-conductivemicrophones capture breathing sound while bone-conductivemicrophone is placed over the manubrium of the sternumin patients to record chest wall lung sound. The simulationsare conducted using lung sounds from patients with wheezesand human subjects with no wheezes under different SNRconditions. The results show that the proposed method is robustagainst environmental noise and has good performance onwheeze detection. The approach has been implemented ontoa PDA and tested with some real data.

Keywords-Pulmonary sounds; Real-time classification; lungsound analysis; noise cancelation; wheezes; lung sounds;

I. INTRODUCTION

The breathing-associated sound heard on the chest of aperson is called the lung sound [2]. Breathing sound and lungsound analysis to establish diagnoses is very interested inmedicine. For example, with the invention of stethoscope therespiratory sounds heard over the chest wall have long beenused as one of the means by which pulmonary dysfunctionswere detected and diagnosed. Although auscultation byconventional stethoscope is an inexpensive and simple touse diagnostic method it has largely been restricted by sub-ject dependency. Stethoscopes do not provide a frequency-independent, uncolored transmission of sounds. They canselectively amplify or attenuate sounds within the spectrumof clinical interest [2]. It has also a high degree of subjec-tivity relative to the specialist [6]. However, recent advancesin audio signal processing, and diagnostic algorithms haveprovided many efficient and effective research improvementsto resurrect e-auscultation as an important diagnostic option.

Sound signal digitization and processing techniques havebeen developed to make more objective the method bymeans of quantitative data. For instance, detection of wheezeby way of breathing sounds analysis has increased interestin recent years, since lung diseases are difficult to diagnoseby X-ray machine and others medical instruments.

Wheezy sounds in signal analysis are represented asadventitious sounds which are overlapped to the normalrespiratory sounds with a duration long enough to perceive amusical tone (80-250 ms) and with a frequency range from100 to over 2000 Hz [6]. Wheezes are caused by narrowing,constriction, or spasm in the very small airways. They canoccur because of asthma, congestive heart failure, fibrosis,pneumonia, and tuberculosis [3]. However, by means ofE-auscultation, the detection of wheeze has largely beenconfined in the face of insufficient experiment data and unfitclassification methods.

Many works on wheeze detection were based on method-ologies and techniques that combined spectra analysis withcriteria or rules concerning the amplitude, duration andpitch range of wheezes [5]- [9]. For instance, wheezeswere detected in running spectra of lung sounds by useof a frequency domain peak detection algorithm in [5].The frequencies and patterns of wheeze spectrograms wereevaluated for gas density effects. It is shown that bothwheeze frequency and patterns did not exhibit consistentchanges with gas density.

In [6], the investigation that the use of a Shabtai-Musih in-spired algorithm is performed to detect wheezes on the time(flow)-frequency domain. It scans the signal with a constantmoving window and calculates its spectral representation.The spectrum was controlled by subtracting its mean andnormalizing by its standard deviation. Peak frequencies (FP)were selected as wheezing sounds if they comply with a setof fuzzy rules. This algorithm was proved effective to detectlong and powerful wheezes which are mainly originateddue to airway flutter mechanisms. It can not work on otherwheeze types.

The frequency analysis of respiratory sounds during spon-taneous ventilation was studied in [7]. In this work, the anal-ysis of respiratory sounds in frequency domain and duringspontaneous ventilation was lead to a simple classificationmethod by evaluate peak frequency of expiration. Using anautoregressive model, it calculated the power spectral density(PSD) in each expiration phase and estimated the FP. Highvalues of FP were found in wheezy patients with severeobstruction.

The investigations by [8] and [9] are all based on thespectrogram of the breathing sounds recordings. In [8],

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Figure 1. Distributed Array of Microphones Experimental Setup

the amplitude criteria were applied to the peaks of thespectrogram in order to discriminate the wheezing from thenormal sounds. To improve the detection results, frequencyand time continuity criteria were used at same time. Thestudy in [9] developed an algorithm to detect wheezes at allsound levels and wheezing episodes with higher sensitivity.It eliminated the effect of patient-dependent transmissionmedia. All of above mentioned works are conducted toimprove the algorithms of detection wheeze in time and/orfrequency domain. They achieved their best performanceonly when the noise condition of the test data matches thatof the training data. It is not clear how well their techniquewill work in noisy environments.

The main objective of our study is to develop a jointhardware and software solution to detect wheeze in noisyenvironment by use of a very simple classification algorithm.Continuing our previous work [1] on using a wearable mi-crophone array system for health condition monitoring, thiswork is designed to make the detection system robust againstenvironmental noise. The detection method is designed tooperate at a sampling rate of 1000 Hz which is way belowthe typical sampling rate of 44 kHz for audio signals. Thewheeze signal detector has been implemented on a PDA.In particular, the paper presents a hardware prototype thatintegrates two air-conductive microphones and one bone-conductive microphone in acquisitions of breathing soundsand analyze automatically the respiratory sound generatedby the wearer. The method intelligently fuses the outputsignals from the distributed array of microphones and extractthe undistorted clean signals. Therefore, undistorted cleansignals lead to much high detection accuracy in noisyenvironments.

Figure 2. System schematic

II. SYSTEM DESCRIPTION:

A. Distributed Array of Microphones Design

The experimental setup of the wheeze detection system isshown in Figure 1. It consists of three modules: a distributedarray of microphones, a front-end circuit and a PDA. ThePDA is Hp iPAQ Hx2700 series and with the Intel PXA270processor. The distributed array of microphones consist oftwo air-conductive and one bone-conductive microphone.The air-microphones(Panasonic WM-61B) have a flat fre-quency response in frequency band of 20-5000Hz. Thebone-microphone (BU-23173K, Knowles Acoustics) has flatfrequency response from 100Hz to 1000Hz. The detectionprocess of the system is similar to the human auscultation.The distributed array of microphones record the breathingsounds. The array output is fed to a frontend circuit toamplify the signals of interest and suppress the unwantedinterferences and noise. The processed signals are fed tothe PDA to extract concerned parameters and produce acrude pattern which is analyzed, compared with storedthresholds. The result is displayed on the PDA. The pictureof the front-end circuit is shown in Figure 2. In this study,multidimensional data are analyzed by a simple entropybased wheeze detection algorithm [16]. The thresholds areobtained from an adequate amount of training data.

The acquisition of breath and lung sounds by array ofmicrophones are taken with inpatients in stabilized respi-ratory conditions. Lung sound amplitude differs betweenpersons and different locations on the chest surface [11]. Thelocation of bone-conductive microphone in our study shouldbe specified in order to record lung sound clearly. Further,to cancel the noise sound which is caused in auscultationthrough clothes, the bone-conductive microphone should beplaced over the manubrium of the sternum in patients torecord bronchial lung sound. It also should be touching thepatient’s bare skin.

The use of bone-conductive microphone can be foundin [12]. The hardware solution was developed to improve

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speech quality and combat against highly non-stationaryacoustic noise, including background interfering speech. Inour proposed study, the bone-conductive microphone is usedto record the bronchial lung sounds through patient’s chestwall in noisy environment.

B. Data Processing and Analysis

The general system model is given in Figure 2. Thesignal y(t) captured by two air conductive microphone (aand b) consist of the clean breathing sound s(t) and noisen(t) which is caused by the environments and backgroundspeech. The signal is collected near to the patient’s neck,which is

ya(t) = s(t) + na(t)yb(t) = s(t) + nb(t)

(1)

The outputs of two air conductive microphones are fed toa beamformer where the unwanted interferences and noiseare suppressed and the signals of interest are enhanced. Theoutput of the beamformer is,

yab(t) = s(t) + nab(t) (2)

The lung sound recorded by the bone conductive micro-phone b(t) on the chest wall consists of clean breathingsound which is filtered by bone and tissues in the patient’schest, and noise.

b(t) = h(t) ∗ s(t) + nbone(t) (3)

Where, h(t) is the impulse repones of the bone conduc-tive microphone. The bone conductive microphone capturesmostly the breathing sounds uttered by the patient buttransmitted via the bone and tissues in the patient’s chest.Therefore, the external noise is heavily reduced in the bonesignal. We can see the frequency spectral view of lungsounds in Figure 3. The sounds are captured from no-wheezysubject and wheezy patient.

The main frequency components of the heart sound arein the range of 20-100Hz. The lung sound contains a muchwider frequency range, which overlaps with the heart sound.In order to cancel heart sound in this study, the frequencycomponent below 100Hz is filtered off from the capturedlung sound and breathing sounds.

In frequency domain, the output signals of the beam-former and bone sensor are represented as,

Yabt(k) = St(k) + Nabt(k)Bt(k) = H(k)St(k) + Nbone(k) (4)

Where k is the frequency band and Yabt(k) is the kthfrequency component of ym[n] = y[n]w[m− n], windoweddata around time t. We assume Nabt(k) and Nbone(k) arezero-mean Gaussian random variables

Nabt(k) ∼ N(0, σ2ab)

Nbone(k) ∼ N(0, σ2bone).

(5)

The challenge of this study is to intelligently fuse twocomplementary signals to extract the useful undistorted cleanbreath signals with specified band width and distinct signalproperties. These properties of captured sounds are mostinterested features in wheeze detection. The clean breathsignal would not be available in the real detection process.The output sounds of the bone-conductive microphone andthe beamformer are fused together to estimate the cleanbreathing sound. The estimation of clean breathing soundcan be explored by an approach which fuses all availablenoisy measurements [13].

To estimate channel impulse response H , we use severalframes of observation data. Let T be the number of framesused for estimation of H . To estimate clean breathing soundS, we only use one frame of observation data. The estima-tion of H and S are conducted by minimizing maximumlikelihood estimation

R =T∑

t=1( 12σ2

ab

|Yabt − St|2 + 12σ2

bone

|Bt −HSt|2). (6)

Notice that St is complex variable, and R is a real functionof the real part and imaginary part of St. Thus the partialderivatives of R with respect to the real part and imaginarypart of R are zero at the optimum.

By setting

∂R

∂St= 0 (7)

we have,

St =σ2

boneYabt + σ2abH

∗Bt

σ2bone + σ2

ab |H|2(8)

Substituting equation (8) to (6), we have

R =T∑

t=1

(|Bt −HYabt|2

σ2bone + σ2

ab |H|2)

(9)

Here, R is regarded as a function of H and H∗.Then by setting

∂R

∂H= 0 (10)

we obtain,

H =q ±

√q2 + 4 |p|2 σ2

boneσ2ab

2pσ2bone

(11)

where,

p =∑

B∗t Yabt

q =∑(

σ2ab |Bt|2 − σ2

bone |Yabt|2) (12)

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Figure 3. The Frequency Spectral View (0-1Khz) of Lung Sounds fromTop: a Healthy Subject and Bottom: a Wheezy Patient

Thus, the solutions for H and St can be obtained adap-tively.

III. PRELIMINARY RESULTS AND DISCUSSIONS

In experiments, the lung sounds from fourteen patientsand nineteen normal subjects are obtained from open re-sources that can be obtained online [14] [15]. It should benoted that, all tested data are re-sampled at the sampling rateof 1000Hz which is far below the original sampling rates.

Table 1 shows the wheeze and no-wheezy sound detec-tion results with the SNR at −5dB, 0dB, 5dB and 10dB,respectively. The first column represents SNR values. Theresults of detection accuracy for wheeze sounds being feedto wheeze detection system are listed in the second column.The last column are the results of detection accuracy byfeeding the no-wheezy breathing sounds into the system.We define detection accuracy as the probability that thewheeze detection system makes the correct detection, orequivalently, the probability of error in the detection results.We can see that the detection accuracy for no-wheezy soundswith different SNR values are a little bit higher than wheezesounds.

Table IEFFECT OF SNR OF INPUT SOUNDS ON WHEEZE DETECTION

ACCURACY

SNR Detection Accuracy Detection Accuracy(Wheeze) (no-Wheeze)

-5dB 85.7% 84.2%0dB 85.7% 89.4%5dB 92.8% 94.7%

10dB 92.8% 94.7%

Table IIWHEEZE DETECTION ACCURACY WITH/WITHOUT BONE CONDUCTIVE

MICROPHONE, SNR=5DB

Input Sound Detection Accuracy Detection Accuracy(with Bone-Mic) (without Bone-Mic)

wheeze 92.8% 71.4%no-wheeze 94.7% 78.9%

The reason for higher accuracy of detection results in no-wheezy sound could be interpreted from the point of view ofabounding test data leading to more accurate classificationthreshold settings. Whereas, wheeze sounds have their spec-trum changes from patient to patient, it is not easy to identifymultiply changes through a simple classification method.

To study further, let us compare the results in samecolumn. No significant differences were found among theresults of detection accuracy which are obtained at differentSNR. The results are less relevant to the SNR. In otherwords, the wheeze detection system are less sensitive tonoise of surrounding environment.

Table 2 shows the wheeze sound detection resultswith/without use of the bone sensor, respectively. The de-tection experiment is conducted at SNR= 5dB. The resultsof detection accuracy for wheeze sounds being feed to thesystem are listed in the second row. The last row are theresults of no-wheezy breathing sounds. It is obvious thatthe results of detection accuracy for use of bone conductivemicrophone system are much higher than without use ofbone microphone system.

The simulation algorithms are developed in Matlab. Forthe method in this study, the detection process time is lessthan two seconds. In case of need, the detection system canbe implemented and optimized for a much faster processor.

IV. CONCLUSION

We presented a novel method to detect and analyzewheeze sounds. The method has a distributed microphonearray and is able to operate at a sampling rate of 1000Hz.The investigation was to use two regular air conductivemicrophones and a bone conductive microphone to increasethe accuracy of detection and make it robust against environ-mental noise. The two air-conductive microphones capturebreathing sound while bone-conductive microphone recordchest wall lung sound. The simulations were conductedusing lung sounds from patients with wheezes and human

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subjects with no wheezes under different SNR conditions.The results show that the system is robust against environ-ment noise and has good performance on wheeze detection.The proposed design has been implemented onto a PDA andtested with some real data.

ACKNOWLEDGMENT

This work was supported by the Embedded and HybridSystem (EHS) programme supported by the Agency forScience, Technology and Research (A*STAR) under theGrant 052-118-0053.

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