Design of Wearable Breathing Sound Monitoring System for Real-Time Wheeze Detection

sensors

Article Design of Wearable Breathing Sound Monitoring System for Real-Time Wheeze Detection

Shih-Hong Li 1,5, Bor-Shing Lin 2, Chen-Han Tsai 3, Cheng-Ta Yang 4,5 and Bor-Shyh Lin 3,*

1 Department of Thoracic Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan; [email protected] 2 Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 23741, Taiwan; [email protected] 3 Institute of Imaging and Biomedical Photonics, National Chiao Tung University, Tainan 71150, Taiwan; [email protected] 4 Department of Thoracic Medicine, Chang Gung Memorial Hospital at Taoyuan, Taoyuan 33378, Taiwan; [email protected] 5 Department of Respiratory Therapy, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan * Correspondence: [email protected]; Tel.: +886-6-303-2121 (ext. 57835)

Academic Editor: Vittorio M. N. Passaro Received: 13 November 2016; Accepted: 13 January 2017; Published: 17 January 2017

Abstract: In the clinic, the wheezing sound is usually considered as an indicator symptom to reﬂect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this study, a wearable and wireless breathing sound monitoring system for real-time wheeze detection was proposed. Moreover, a breathing sounds analysis algorithm was designed to continuously extract and analyze the features of breathing sounds to provide the objectively quantitative information of breathing sounds to professional physicians. Here, normalized spectral integration (NSI) was also designed and applied in wheeze detection. The proposed algorithm required only short-term data of breathing sounds and lower computational complexity to perform real-time wheeze detection, and is suitable to be implemented in a commercial portable device, which contains relatively low computing power and memory. From the experimental results, the proposed system could provide good performance on wheeze detection exactly and might be a useful assisting tool for analysis of breathing sounds in clinical diagnosis.

Keywords: airway obstruction; wheeze detection; short-term breathing sound; spectral integration

1. Introduction Abnormal breathing sounds (such as crackles, rhonchus, and wheezing sounds) are usually considered as indicator symptoms in chronic respiratory diseases [1], such as chronic obstructive pulmonary disease (COPD), chronic bronchitis, and bronchial asthma, etc. For these diseases, unnecessary secretions (such as sputum) would be produced in the respiratory tract and then cause chronic inflammation leading to airway obstruction. When the state of airway obstruction acutely exacerbates, the inside diameter of airway will be narrowed, and the smooth muscle in the outer wall of the airway will also be tightened during breathing [2]. Therefore, the airflow velocity will be changed when the air flows from the normal airway into the narrowing airway, producing abnormal breathing sounds, such as wheezes [3]. In the clinic, a wheezing sound is a kind of continuously abnormal breathing sound with a specific tone [4], and it is usually considered as an indicator symptom

Sensors 2017, 17, 171; doi:10.3390/s17010171 www.mdpi.com/journal/sensors Sensors 2017, 17, 171 2 of 15 to reflect the degree of airway obstruction [5]. When the wheezing sound occurs, the patient might have no sufficient amount of air to maintain normal breathing, resulting in dyspnea, asphyxia or other life-threatening situations [6]. Therefore, it is important to detect wheezing sounds automatically, and then to provide prompt medical treatment for patients with acute airway obstruction. Currently, investigation of breathing sounds is mainly based on the auscultation approach by the experienced physicians. This method is simple, convenient, and non-invasive, but it is also subjectively dependent on the experience of the physicians, the variability of the human auditory system and the specifications of stethoscopes [7,8]. In previous studies [9–20], several approaches, such as a spirometer, vibration response image (VRI), piezoelectric sensor and air-coupled microphone, had also been used to evaluate the feature of breathing sounds objectively. In 1994, Schreur et al. determined the parameters of lung sound intensity (LSI), frequency content in quartile point and peak frequency from the flow-dependent power spectrum of lung sounds to investigate the lung sound features of patients with bronchial asthma [15]. In 1999, Kiyokawa et al. investigated the feature pattern of hourly nocturnal wheezing count (NWC), the duration of wheezing sounds, and the forced expiratory volume in one second (FEV1), which was obtained from a spirometer to evaluate the severity of bronchoconstriction [16]. In 2002, Gross et al. used the ratio of relative power at maximal flow (RPMF) between inspiratory and expiratory in the frequency band of 300–600 Hz to investigate the lung sound features of pneumonia [17]. In 2014, Içer et al. used the ratio of maximum and minimal frequency in power spectral density (PSD), and the normalized instantaneous frequency of lung sounds to distinguish different abnormal lung sounds [18]. Most of the above methods just provide the lung sound information in frequency or time domain, respectively. In 2008, Sello et al. compared three quartile frequencies in the global wavelet spectrum to investigate the severity of respiratory insufficiency in patients with COPD [19]. In 2009, Riella et al. used image processing techniques to extract the spectral projection of lung sound spectrogram to identify wheezing sounds [20]. From the spectrogram of lung sounds, the information of lung sounds in both time and frequency domains could be extracted. However, it also requires a relatively long raw data and computational complexity for time frequency analysis, and this is inconvenient for several commercial portable devices, which contains relatively low computing ability and memory, to perform real-time wheeze detection. Moreover, most of the devices used in the above studies were not wearable, and were also not convenient for long-term monitoring of breathing sounds in daily life. In order to improve the above issues, a wearable breathing sound monitoring system for real-time wheeze detection was proposed in this study. In this proposed system, a wearable breathing sound acquisition module was designed to collect breathing sounds wirelessly in daily life. A breathing sound analysis algorithm was also proposed in this study. By using this algorithm, the spectral features of short-term breathing sounds would be real-time and continuously extracted to construct a characteristic pattern, and from the feature pattern, the information of breathing sounds in both of time and frequency domains could be effectively obtained and applied in wheeze detection. This algorithm required only lower computational complexity to perform real-time wheeze detection, and it is suitable to be implemented in a commercial portable device, which contains relatively low computing capability. Finally, the difference between features of wheezing and healthy breathing sounds was also investigated in this study.

2. Methods and Materials

2.1. System Hardware Design and Implementation Figure1 shows the basic scheme of the proposed wearable and wireless breathing sound monitoring system, including a wireless breathing sound acquisition module, a wearable mechanical design, and a host system. The wireless breathing sound acquisition module is embedded into the wearable mechanical design, and it is placed on the upper right anterior chest surface of the user to acquire the breathing sound. The wearable mechanical design aims to not only embed with the Sensors 2017, 17, 171 3 of 15 proposedSensors 2017, module,17, 171 but also provide a suitable pressure on the wireless breathing sound acquisition3 of 15 module to maintain a good contacting condition between the acoustic sensor and the chest wall. theIn this chest mechanical wall. In this design, mechanical the acoustic design, sensor the couldacoust acquireic sensor a goodcould signal acquire quality a good of breathingsignal quality sound, of breathingand users couldsound, be and easy users to wear could and be use easy in to daily wear life. and After use the in daily breathing life. After sound the is acquired,breathing ampliﬁed, sound is acquired,and digitized amplified, by the and proposed digitized module, by the itproposed will then module, be transmitted it will then to thebe transmitted host system to wirelessly. the host systemFinally, thewirelessly. real-time Finally, breathing the sound real-time monitoring breathing program sound built monitoring in the host program system will built receive, in the display, host systemrecord andwill analyze receive, the display, acquired record breathing and sound analyze to providethe acquired the information breathing of sound the breathing to provide sound the to informationthe medical staff.of the breathing sound to the medical staff.

Figure 1. BasicBasic scheme scheme of proposed wearable and wi wirelessreless breathing sound monitoring system.

2.1.1. Wireless Breathing Sound Acquisition Module Figure 22a,ba,b showshow thethe blockblock diagramdiagram andand photographphotograph ofof thethe proposedproposed wirelesswireless breathingbreathing soundsound acquisition module,module, and and it it mainly mainly contains contains several several parts, parts, including including an acoustic an acoustic sensor, sensor, a sensor a drivingsensor drivingcircuit, acircuit, front-end a front-end amplifier amplifier circuit, circuit, a microprocessor, a microprocessor, and a and wireless a wireless transmission transmission circuit. circuit. In the In wirelessthe wireless breathing breathing sound sound acquisition acquisition module, module, the the acoustic acoustic sensor sensor consists consists ofof anan omnidirectional condenser microphonemicrophone (TS-6022A, (TS-6022A, TRANSOUND, TRANSOUND, Dongguan, Dongguan, China) China) and a stethoscopeand a stethoscope bell (Harvey bell™ (Harvey™DLX, Welch DLX, Allyn, Welch Skaneateles Allyn, Skaneateles Falls, New Falls, York, New NY, USA),York, NY, as shown USA), inas Figureshown2 c.in ItFigure is designed 2c. It is designedto acquire to breathing acquire breathing sound effectively, sound effectively, and transduced and transduced sound intosound an into electrical an electrical signal. signal. Here, Here,the sensor the drivingsensor circuitdriving is circuit intended is tointended provide ato stableprovide driving a stable voltage driving for the voltage omnidirectional for the omnidirectionalcondenser microphone, condenser and microp eliminatehone, variation and eliminate from variation the power from source. the power Next, source. the signal Next, of the signalbreathing of the sound breathing will besound amplified will be and amplified filtered and by filtered a front-end by a front-end amplifier ampl circuit.ifier The circuit. total The gain total of gainthe front-end of the front-end amplifier amplifier circuit iscircuit set to is 500 set times,to 500 andtimes, the and frequency the frequency band is band set to is >set 150 to Hz.> 150 Next, Hz. Next,the pre-processed the pre-processed breathing breathing sound sound will be will digitized be digitized by a 12-bit by a analog-to-digital 12-bit analog-to-digital converter converter built in builtthe microprocessor in the microprocessor (MSP430, (MSP430, Texas Instruments, Texas Instruments, Dallas, TX, Dallas, USA) TX, with USA) the with sampling the sampling rate of 2048 rate Hz, of 2048and thenHz, and be sent then to be a sent wireless to a wireless transmission transmission circuit, whichcircuit, consistswhich consists of a printed of a printed circuit circuit board board (PCB) (PCB)antenna antenna and a Bluetoothand a Bluetooth module module (Ct-BT02, (Ct-BT02, Connectec, Connectec, Taiwan) withTaiwan) the Bluetoothwith the Bluetooth v2.0+ enhanced v2.0+ enhanceddata rate (EDR) data rate specification. (EDR) specification. Here, the powerHere, the consumption power consumption and data rate and of data the wirelessrate of the acquisition wireless acquisitionmodule are module about 75 are mW about and 75 33k mW bps. and Finally, 33k bps. the wirelessFinally, the transmission wireless transmission circuit will transmit circuit will the transmitcollected the breathing collected sound breathing to the sound host system to the wirelessly. host system This wirelessly. module isThis operated module at is 33 operated mA with at a 33commercial mA with 250a commercial m-Ah Li-ion 250 battery, m-Ah Li-ion and can battery, continuously and can operate continuously over seven operate hours. over seven hours.

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FigureFigure 2. (a ) 2. Block((a)) Block diagram diagram and and ( b( b)) photograph photograph of of proposed proposed wirelesswirele wireless breathingss breathing sound sound acquisition acquisition modulemodule and and( c) photograph ( c) photograph of of acoustic acoustic sensor. sensor.

2.1.2. 2.1.2.Wearable Wearable Wearable Mechanical Mechanical Design Design Figure3 a,b3a,b shows shows the the photograph photograph of the of wearablethe wearable mechanical mechanical design design and photograph and photograph of wearing of Figure 3a,b shows the photograph of the wearable mechanical design and photograph of wearingthe wireless the wireless breathing breathing sound monitoringsound monitoring system system during during experiments, experiments, and it and mainly it mainly consists consists of a wearingofshoulder a theshoulder wireless brace, brace, an breathing elastican elastic band soundband and and an monitoring areaan area of Velcro.of Velcro. system The The structure during structure experiments, of theof the shoulder shoulder and brace brace it ismainly usedis used to consists of a shouldertoembed embed the brace, the proposed proposed an elastic wireless wireless band breathing breathing and an sound soundarea acquisition of acqu Velcro.isition module The module structure and and also also support of supportthe theshoulder weightthe weight brace of this of is used to embedthismodule. module. the Moreover,proposed Moreover, by wireless adjusting by adjusting breathing the tightnessthe tightness sound of elastic ofacqu elastic band,isition band, the module proposedthe proposed and module also module support can can easily easily the fit the weightfit of this module.thechest chest contour Moreover, contour of the of user theby adjusting touser maintain to maintain the a good tightness a contactinggood contacting of elastic condition condition band, between the betw proposed theeen acoustic the acousticmodule sensor andsensor can the easily fit the chestandchest thecontour surface chest tosurface of reduce the to user thereduce artificial to maintainthe artificial influence a influegood of motion.nce contacting of motion. In this wearable conditionIn this wearable mechanical betw mechanicaleen design, the acoustic thedesign, area sensor of Velcro is used to fix the proposed module on the shoulder brace so that the proposed system can be and thethe chest area ofsurface Velcro tois reduceused to thefix the artificial proposed influe modulence ofon motion. the shoulder In this brace wearable so that themechanical proposed design, systemeasy to can wear be and easy use to inwear daily and life. use in daily life. the area of Velcro is used to fix the proposed module on the shoulder brace so that the proposed system can be easy to wear and use in daily life.

Figure 3. ((a)) Photograph Photograph of of wearable wearable mechanical mechanical design, design, and and ( (b)) photograph photograph of of wearing wearing the wireless breathing sound monitoring system.

2.1.3. Host System 2.1.3. Host System Figure 3. (a) Photograph of wearable mechanical design, and (b) photograph of wearing the wireless In this study, the platform of the host system is a commercial tablet with the operation system In this study, the platform of the host system is a commercial tablet with the operation system of breathingof Window sound 10. monitoringMoreover, asystem. real-time breathing sound monitoring program, developed using Window 10. Moreover, a real-time breathing sound monitoring program, developed using Microsoft Microsoft C#, is also built in the host system, and this program also implements the proposed C#, is also built in the host system, and this program also implements the proposed breathing 2.1.3. breathingHost System sound analysis algorithm to provide the basic function of monitoring, recording, and analyzing the breathing sound. In this study, the platform of the host system is a commercial tablet with the operation system of Window 10. Moreover, a real-time breathing sound monitoring program, developed using Microsoft C#, is also built in the host system, and this program also implements the proposed breathing sound analysis algorithm to provide the basic function of monitoring, recording, and analyzing the breathing sound.

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2.2. Breathing Sound Analysis Algorithm sound analysis algorithm to provide the basic function of monitoring, recording, and analyzing Wheezingthe breathing sound sound. is a continuously abnormal breathing sound, which is commonly disclosed in patients 2.2.with Breathing COPD Sound or other Analysis airw Algorithmays’ obstructive respiratory diseases. Wheezing presented with a specific characteristic in duration and frequency domain than other breathing sounds. The range of Wheezing sound is a continuously abnormal breathing sound, which is commonly disclosed in frequency of normal breathing sound evenly distributes between 100 Hz and 1000 Hz. For a patients with COPD or other airways’ obstructive respiratory diseases. Wheezing presented with a wheezingspecific sound, characteristic the frequency in duration range and is mainly frequency between domain than250 otherHz and breathing 800 Hz, sounds. and it The can range be presented of as a specificfrequency narrow of normal line breathing pattern, sound which evenly is distributesmaintained between over 100 about Hz and 250 1000 milliseconds Hz. For a wheezing [21], in the spectrogramsound, of the breathing frequency sounds. range is mainly In this between study, 250 th Hze breathing and 800 Hz, sound and it can analysis be presented algorithm as a specific is designed to extractnarrow the features line pattern, of breathing which is maintainedsound in time over aboutand frequency 250 milliseconds domains, [21], inand the its spectrogram flowchart of is shown breathing sounds. In this study, the breathing sound analysis algorithm is designed to extract the in Figure 4. features of breathing sound in time and frequency domains, and its flowchart is shown in Figure4.

Breathing sounds Pre- Fourier Feature Segmentation processing transform extraction

Detecting output of Criteria for wheezing sounds wheezes detection

Peak frequency Median frequency Bandwidth Spectral integration FigureFigure 4. Flowchart 4. Flowchart of ofbreathing breathing sound analysis analysis algorithm. algorithm.

Before extractingBefore extracting the features the features of breathing of breathing soun sounds,ds, the the received received breathingbreathing sound sound has has to beto be first first pre-processed by a band-pass filter (frequency band: 150 Hz–1000 Hz) to reserve meaningful pre-processedcomponents by a of band-pass breathing sounds filter and (frequency also remove heartband: sound, 150 muscleHz–1000 interference Hz) to sound reserve and bloodmeaningful componentssound of [ 2breathing]. Next, the sounds raw breathing and also sound remove will be heart split into sound, 250-ms muscle breathing interference sound segments sound with and blood sound [2].200-ms Next, overlapping, the raw breathing and then thesound power will spectrums be split ofinto these 250-ms breathing breathing sound segments sound segments will be with 200-ms overlapping,calculated by using and Fastthen Fourier the power Transform spectrums with a Hanning of these window. breathing After obtainingsound segments the power will be calculatedspectrum by using of each Fast breathing Fourier sound Transform segment, thenwith the a features Hanning of breathing window. sound After in frequency obtaining domain the power can be calculated. Here, the ratios of the spectral integration (SI) features SI (from 0 Hz spectrum of each breathing sound segment, then the features of breathing0Hz–250Hz sound in frequency to 250 Hz), SI250Hz–500Hz (from 250 Hz to 500 Hz), and SI500Hz–1000Hz (from 500 Hz to 1000 Hz) to 0Hz–250Hz domain canSI0Hz–1000Hz be calculated.(from 0 Hz Here, to 1000 the Hz) ratios are defined of the as thespectral normalized integration spectral integration(SI) features (NSI) SI features (from 0 Hz to 250NSI0Hz–250Hz Hz), SI,250Hz–500Hz NSI250Hz–500Hz (from, and 250 NSI Hz500Hz–1000Hz to 500 ,Hz), respectively. and SI In500Hz–1000Hz this study, Fisher(from linear 500 discriminantHz to 1000 Hz) to SI0Hz–1000Hzanalysis (from (LDA)0 Hz wasto 1000 used toHz) separate are defined wheezing as and the normal normalized breathing spectral sounds. integration After experimenting, (NSI) features the normalized spectral integration NSI0Hz–250Hz, NSI250Hz–500Hz, and NSI500Hz–1000Hz were used as NSI0Hz–250Hz, NSI250Hz–500Hz, and NSI500Hz–1000Hz, respectively. In this study, Fisher linear discriminant the frequency-domain features to detect the wheezing sounds, and they have better recognizing analysis performance(LDA) was thanused other to separate features. If wheezing the feature ofand breathing normal sound breathing fits the followingsounds. criteria:After experimenting, the normalized spectral integration NSI0Hz–250Hz, NSI250Hz–500Hz, and NSI500Hz–1000Hz were used as the frequency-domain(i) Score1 =features –230.54489 to + 402.72499detect the× NSI whee0HZ−zing250HZ +sounds, 500.32269 and× NSI they250HZ −have500HZ +better 677.28994 recognizing× NSI . performance than500HZ other−1000HZ features. If the feature of breathing sound fits the following criteria: Score2 = –266.87228 + 418.88239 × NSI0HZ−250HZ + 554.36286 × NSI250HZ−500HZ + 699.35894 × (i) Score1 =NSI –230.54489500HZ−1000HZ +. 402.72499 × NSI + 500.32269 × NSI + 677.28994 × NSI If Score1. < Score 2, then this segment will be recognized as an abnormal breathing sound. Score2(ii) =If –266.87228 the duration of+ abnormal418.88239 breathing × NSI sound > 250 ms+ 554.36286 [22]. × NSI + 699.35894 × NSI. If Score1Then, < Score it will 2, then be recognized this segment as a wheezing will be recognized sound. All theas an quantitative abnormal information breathing of sound. this wheezing event, as illustrated in Figure5, such as the peak frequency, median frequency, bandwidth, (ii) If the duration of abnormal breathing sound > 250 ms [22]. Then, it will be recognized as a wheezing sound. All the quantitative information of this wheezing event, as illustrated in Figure 5, such as the peak frequency, median frequency, bandwidth, and duration, will then also be extracted and reserved. Here, the peak frequency is defined as the frequency corresponding to the highest peak in the power spectrum of the breathing sound. The median frequency is defined as the frequency that is at the center of the power spectrum of the breathing sounds. The bandwidth is defined as the frequency range between the frequencies corresponding to 25% and 75% of total integration area in the power spectrum of the breathing sound. Here, the peak frequency and median frequency are most frequently used to describe the

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and duration, will then also be extracted and reserved. Here, the peak frequency is defined as the frequency corresponding to the highest peak in the power spectrum of the breathing sound. Sensors 2017The, 17 median, 171 frequency is defined as the frequency that is at the center of the power spectrum of 6 of 15 the breathing sounds. The bandwidth is defined as the frequency range between the frequencies feature ofcorresponding breathing sounds. to 25% and Therefore, 75% of total they integration are also area extracted in the power in spectrumthis study. of the The breathing changes sound. of the peak frequencyHere, and the the peak bandwidth frequency andwill median contribute frequency to the are mostchange frequently of the used spectral to describe integration the feature of ofbreathing sound directly.breathing Moreover, sounds. Therefore, the peak they frequency are also extracted usually in this shifts study. within The changes a breathing of the peak cycle. frequency Therefore, in and the bandwidth will contribute to the change of the spectral integration of breathing sound this study, the spectral integrations of breathing sound are used as the major factors to detect directly. Moreover, the peak frequency usually shifts within a breathing cycle. Therefore, in this study, wheezingthe sounds. spectral integrations of breathing sound are used as the major factors to detect wheezing sounds.

10 Peak frequency 8 Median frequency

Intensity 4

Bandwidth 0 0 200 400 600 800 1000 Frequency (Hz) SI0-250Hz SI250-500Hz SI500-1000Hz FigureFigure 5. Illustration 5. Illustration for for quantitative quantitative informationinformation of breathingof breathing sounds. sounds.

2.3. Experimental2.3. Experimental Design Design In this study, the experimental data was collected from 40 adult patients and 11 healthy adults In this(males study, and femalesthe experimental are 42 and 9data participants, was collecte respectively),d from 40 at Changadult patients Gung Memorial and 11 Hospital, healthy adults (males andTaiwan. females The mean are age42 ofand these 9 participantsparticipants, was 61.9respectively),± 24.7 years old.at Chang The Institutional Gung Memorial Review Board Hospital, Taiwan. hasThe approved mean age this of experiment these participants (Institutional was review 61.9 board, ± 24.7 No.103-3295A3), years old. The Chang Institutional Gung Memorial Review Board has approvedHospital, this Taiwan, experiment and all participants (Institutional provided review an informed board, consentNo.103-3295A3), before the experiment. Chang Gung Memorial Before patients enrolling to study, physicians had to complete history taking and physical Hospital, Taiwan, and all participants provided an informed consent before the experiment. examination including breathing sounds. When a doctor disclosed wheezing via conventional Beforestethoscope, patients this enrolling patient was to enrolled study, into physicians the study. The had doctor to alsocomplete recorded history breathing taking sounds and at the physical examinationsame time.including In this study,breathing every doctorsounds. is a qualifiedWhen a Pulmonologist doctor disclosed in Taiwan. wheezing They have via abundant conventional stethoscope,clinical this practice patient experience. was enrolled After recording into the breathingstudy. The sounds, doctor pulmonologists also recorded had tobreathing discuss and sounds at the sameconfirm time. theIn recordedthis study, file and every voice qualitydoctor first. is a Then, qualified engineers Pulmonologist analyzed voice accordingin Taiwan. to study They have protocol and program. Finally, engineers and pulmonologists discussed final result after analysis abundant clinical practice experience. After recording breathing sounds, pulmonologists had to together and made sure the data was correct. The acoustic sensor of the proposed system was placed discuss onand the confirm right upper the anterior recorded chest wall file (at and the firstvoice intercostal quality space first. on theThen, midclavicular engineers line) toanalyzed collect voice accordingtwo-minute to study data protocol of breathing and sounds.program. In this Finally, study, everyengineers physician and was pulmonologists a qualified pulmonologist discussed final result afterin Taiwan. analysis They together had abundant and made clinical sure practice the experiences. data was Aftercorrect. recording The breathingacoustic sounds,sensor of the proposedpulmonologists system was hadplaced to discuss on the and right confirm upper the recorded anterior file chest and voicewall quality(at the first. first Then, intercostal engineers space on the midclavicularanalyzed voice line) according to collect to two-minute study protocol data and of program. breathing Finally, sounds. engineers In this and study, pulmonologists every physician discussed final results after analysis together and made sure that the data was correct. Here, was a qualifiedthe analysis pulmonologist of variance (ANOVA) in Taiwan. method was They used had to analyze abundant the feature clinical difference practice between experiences. wheezing After recording breathing sounds, pulmonologists had to discuss and confirm the recorded file and voice quality first. Then, engineers analyzed voice according to study protocol and program. Finally, engineers and pulmonologists discussed final results after analysis together and made sure that the data was correct. Here, the analysis of variance (ANOVA) method was used to analyze the feature difference between wheezing and normal breathing sounds, and the software MATLAB (MATLAB, Math Works, Natick, MA, USA) was used to perform ANOVA. The difference significance was defined as p < 0.05 in this study.

3. Results

3.1. Feature Patterns of Wheezing and Healthy Breathing Sounds In this section, the spectral features of wheezing and healthy breathing sounds were investigated. Figure 6a shows the power spectrum of wheezing and healthy breathing sounds. The spectral distribution of wheezing sounds was mainly from 250 Hz to 500 Hz, and is relatively narrower than that of normal breathing sounds. Moreover, the peak intensity in the power spectrum

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and normal breathing sounds, and the software MATLAB (MATLAB, Math Works, Natick, MA, USA) was used to perform ANOVA. The difference signiﬁcance was deﬁned as p < 0.05 in this study.

3. Results

3.1. Feature Patterns of Wheezing and Healthy Breathing Sounds

Sensors 2017In, 17 this, 171 section, the spectral features of wheezing and healthy breathing sounds were investigated.7 of 15 Figure6a shows the power spectrum of wheezing and healthy breathing sounds. The spectral of thedistribution wheezing sounds of wheezing was also sounds obviously was mainly larger from than 250 that Hz of tothe 500 normal Hz, and breathing is relatively sounds. narrower Figure 6b showsthan the that time of normalfrequency breathing feature sounds. pattern Moreover, for wheezing the peak and intensity healthy in thebreathing power spectrumsounds. ofHere, the the wheezing sounds was also obviously larger than that of the normal breathing sounds. Figure6b shows values of NSI0Hz–250Hz, NSI250Hz–500Hz, and NSI500Hz–1000Hz were used as the frequency-domain feature in the time frequency feature pattern for wheezing and healthy breathing sounds. Here, the values this feature pattern. In the feature pattern of healthy breathing sounds, the values of NSI0Hz–250Hz were of NSI0Hz–250Hz, NSI250Hz–500Hz, and NSI500Hz–1000Hz were used as the frequency-domain feature in similar to that of NSI250Hz–500Hz, and greater than that of NSI500Hz–1000Hz. However, for the feature this feature pattern. In the feature pattern of healthy breathing sounds, the values of NSI0Hz–250Hz 250Hz–500Hz 0Hz–250Hz patternwere of similarwheezing to that sounds, of NSI the250Hz–500Hz values of, and NSI greater than were that obviously of NSI500Hz–1000Hz larger than. However, that of NSI for the and NSIfeature500Hz–1000Hz pattern. From of wheezing the difference sounds, of the different values of feature NSI250Hz–500Hz patterns,were wheezing obviously sounds larger thancould that easily of be distinguishedNSI0Hz–250Hz fromand the NSI normal500Hz–1000Hz breathing. From the sounds difference. Moreover, of different the feature duration patterns, of wheezing wheezing sound sounds was aboutcould 736.86 easily ± 311.40 be distinguished ms. from the normal breathing sounds. Moreover, the duration of wheezing sound was about 736.86 ± 311.40 ms.

(a) 1.4 Normal breathing sound15 Wheezing sound 1.2 12 1

0.8 9

0.6 Intensity Intensity 6 Intensity 0.4 Intensity 3 0.2

0 0 0 200 400 600 800 1000 0 200 400 600 800 1000 Frequency (Hz) Frequency (Hz) Frequency (Hz) Frequency (Hz) (b) Normal breathing sound

Normal breathing sound

Wheezing sound

FigureFigure 6. (a 6.) Spectrum(a) Spectrum of wheezing of wheezing and and healthy healthy breathing breathing sounds,sounds, and and (b ()b raw) raw data data and and feature feature patternspatterns for wheezing for wheezing and and healthy healthy breathing breathing sounds. sounds.

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3.2. Feature Difference between Wheezing and Healthy Breathing Sounds In this section, the differences between the features of wheezing and healthy breathing sounds were investigated. Figure7a–c shows the means and standard deviations of SI 0Hz–250Hz, SI250Hz–500Hz, and SI500Hz–1000Hz for different groups, respectively. The value of SI250Hz–500Hz for wheezing sounds was significantly stronger than that of normal breathing sounds. However, the values of SI0Hz–250Hz and SI500Hz–1000Hz for wheezing sounds were similar to that of normal breathing sounds. Figure8a–c show the means and standard deviations of NSI0Hz–250Hz, NSI250Hz–500Hz, and NSI500Hz–1000Hz for different Sensors 2017, groups.17, 171 For wheezing sounds, the value of NSI250Hz–500Hz was obviously larger than the values of 8 of 15 NSI0Hz–250Hz and NSI500Hz–1000Hz. However, for normal breathing sounds, the value of NSI0Hz–250Hz healthy breathingwas similar sounds. to the value Moreover, of NSI250Hz–500Hz the, andbandwidt was largerh thanof wheezing the value of NSI sounds500Hz–1000Hz was. Figure also9 a–csignificantly show the comparison of the peak frequencies, the median frequencies, and the bandwidths for narrower differentthan that groups, of healthy respectively. breathing The peak sounds frequency. However, of wheezing the sounds median was significantly frequency higher of wheezing sounds wasthan similar that of healthyto that breathing of healthy sounds. breathing Moreover, sounds. the bandwidth The features of wheezing for soundsdifferent was alsogroups were summarizedsignificantly in Table narrower 1. In this than study, that of the healthy method breathing of Fisher sounds. LDA However, was theused median to discriminate frequency of the two groups. Thewheezing performance sounds was of similarusing toparameters that of healthy of breathing SI and sounds.NSI and The the features combination for different of groups SI and NSI to were summarized in Table1. In this study, the method of Fisher LDA was used to discriminate the discriminatetwo the groups. two The groups performance were of compared. using parameters We offound SI and that NSI andusing the combinationthe parameters of SI and of NSI to discriminateNSI the to discriminate two groups the two could groups provide were compared. a better We foundperformance. that using the Moreover, parameters ofthe NSI criterion to of wheezing discriminatedetection was the two defined groups as could the provide mentioned a better criterion performance. in Moreover,Section 2.2. the criterion of wheezing detection was defined as the mentioned criterion in Section 2.2.

*p=8.480 x 10-6 *p=2.200 x 10-9 (a) (b) 240 700 210 600

180 500 150 400

120 250-500Hz 0-250Hz 300 SI SI 90 200 60 30 100 0 0 Wheezing group Healthy group *p=8.312 x 10-5 Wheezing group Healthy group (c) 160 140 120 100 80 500-1000Hz

SI 60 40 20 0 Wheezing group Healthy group Figure 7. Means and standard deviations of (a) SI ;(b) SI ; and (c) SI for Figure 7. Means and standard deviations of (a)0Hz-250Hz SI0Hz-250Hz; (250Hz-500Hzb) SI250Hz-500Hz; and500Hz-1000Hz (c) SI500Hz-1000Hz for different groups. Here, * denotes signiﬁcant difference. different groups. Here, * denotes significant difference.

*p=0.01696 *p=8.466 x 10-13 (a) (b) 0.4 0.8 0.35 0.7 0.3 0.6 0.25 0.5 0-250Hz

0.2 250-500Hz 0.4 NSI 0.15 NSI 0.3 0.1 0.2 0.05 0.1 0 0 Wheezing group Healthy group *p=4.720 x 10-5 Wheezing group Healthy group (c) 0.25

0.2

0.15 500-1000Hz 0.1 NSI

0.05

0 Wheezing group Healthy group

Figure 8. Means and standard deviations of (a) NSI0Hz-250Hz; (b) NSI250Hz-500Hz; and (c) NSI500Hz-1000Hz for different groups. Here, * denotes significant difference.

Sensors 2017, 17, 171 8 of 15

healthy breathing sounds. Moreover, the bandwidth of wheezing sounds was also significantly narrower than that of healthy breathing sounds. However, the median frequency of wheezing sounds was similar to that of healthy breathing sounds. The features for different groups were summarized in Table 1. In this study, the method of Fisher LDA was used to discriminate the two groups. The performance of using parameters of SI and NSI and the combination of SI and NSI to discriminate the two groups were compared. We found that using the parameters of NSI to discriminate the two groups could provide a better performance. Moreover, the criterion of wheezing detection was defined as the mentioned criterion in Section 2.2.

*p=8.480 x 10-6 *p=2.200 x 10-9 (a) (b) 240 700 210 600

180 500 150 400

120 250-500Hz 0-250Hz 300 SI SI 90 200 60 30 100 0 0 Wheezing group Healthy group *p=8.312 x 10-5 Wheezing group Healthy group (c) 160 140 120 100 80 500-1000Hz

SI 60 40 20 0 Wheezing group Healthy group

Sensors 2017Figure, 17, 171 7. Means and standard deviations of (a) SI0Hz-250Hz; (b) SI250Hz-500Hz; and (c) SI500Hz-1000Hz for 9 of 15 different groups. Here, * denotes significant difference.

*p=0.01696 *p=8.466 x 10-13 (a) (b) 0.4 0.8 0.35 0.7 0.3 0.6 0.25 0.5 0-250Hz

0.2 250-500Hz 0.4 NSI 0.15 NSI 0.3 0.1 0.2 0.05 0.1 0 0 Wheezing group Healthy group *p=4.720 x 10-5 Wheezing group Healthy group (c) 0.25

0.2

0.15 500-1000Hz 0.1 NSI

0.05

0 Wheezing group Healthy group

FigureFigure 8. Means 8. Means and and standard standard deviations deviations of of (a )(aNSI) NSI0Hz-250Hz0Hz-250Hz; ;((bb)) NSINSI250Hz-500Hz250Hz-500Hz; and; and(c) NSI (c)500Hz-1000HzNSI500Hz-1000Hz for Sensorsfor differentdifferent 2017, 17, groups. 171 Here, Here, * *deno denotestes significant signiﬁcant difference. difference. 9 of 15

*p=3.137x 10-6 p=0.0752 (a) (b) 400 400 350 350 300 300 250 250 200 200 150 150 100 100 Median frequency (Hz) Peak frequency Peak frequency (Hz) 50 50 0 0

Wheezing group Healthy group Wheezing group Healthy group *p=5.720x 10-9 (c) 400 350 300 250 200 150

Bandwidth (Hz) Bandwidth 100 50 0 Wheezing group Healthy group FigureFigure 9. Means9. Means and and standard standard deviationsdeviations of ( a)) peak peak frequencies; frequencies; (b () bmedian) median frequencies; frequencies; and and (c) bandwidths(c) bandwidths for for different different groups. groups. Here, Here, * * denotesdenotes significant signiﬁcant difference. difference.

Table 1. Features in time and frequency domains for wheezing and healthy breathing sound groups. Table 1. Features in time and frequency domains for wheezing and healthy breathing sound groups. Wheezing Sound Group Normal Breathing Sound Group p-Value Peak frequency (Hz) Wheezing Sound310.52 Group ± 40.94 Normal Breathing Sound 219.13 Group± 49.79 p-Value * 3.137 × 10−6 PeakMedian frequency frequency (Hz) (Hz) 310.52 ± 40.94323.52 ± 36.70 219.13 ± 49.79 350.88 ± 59.34 * 3.137 × 0.075210−6 Median frequencyBandwidth (Hz) (Hz) 323.52 ± 36.70151.47 ± 48.43 350.88 ± 59.34 323.44 ± 68.85 0.0752 * 5.720 × 10−9 −9 BandwidthSI (Hz)0–250Hz 151.47 ± 48.43161.12 ± 64.64 323.44 ± 68.85 52.42 ± 55.045 * 5.720 * 8.480× 10 × 10−6 SI 161.12 ± 64.64 52.42 ± 55.045 × −6 0–250HzSI250–500Hz 475.38 ± 215.21 60.66 ± 50.43 * 8.480 * 2.20010 × 10−9 SI 475.38 ± 215.21 60.66 ± 50.43 × −9 250–500Hz * 2.200 10 −5 SI500–1000Hz 101.97 ± 75.31 24.92 ± 18.65 * 8.312− ×5 10 SI500–1000Hz 101.97 ± 75.31 24.92 ± 18.65 * 8.312 × 10

NSI0–250HzNSI0–250Hz 0.243 ± 0.0910.243 ± 0.091 0.315 ± 0.088 0.315 ± 0.088 * 0.01696 * 0.01696 −13 NSI250–500HzNSI250–500Hz 0.623 ± 0.0710.623 ± 0.071 0.417 ± 0.028 0.417 ± 0.028 * 8.466 * 8.466× 10 × 10−13 NSI 0.118 ± 0.042 0.190 ± 0.046 × −5 500–1000HzNSI500–1000Hz 0.118 ± 0.042 0.190 ± 0.046 * 4.720 * 4.72010 × 10−5 Duration of wheezing －－ Duration of wheezing sounds (milliseconds)736.86 ± 311.40 736.86 ± 311.40 — — sounds (milliseconds) * means significance difference (p < 0.05). * means signiﬁcance difference (p < 0.05).

3.3. Performance of Breathing Sound Analysis Algorithm In this section, the performance of detecting wheezing events by using the proposed breathing sound analysis algorithm was investigated. Before evaluating the performance of the proposed algorithm, several parameters of the binary classification test have to be defined first: true positive (TP) denotes that a real wheezing sound event was correctly detected as a wheezing sound event. False positive (FP) denotes that a non-wheezing sound event was incorrectly detected as a wheezing sound event. True negative (TN) denotes that a non-wheezing sound event was correctly detected as a non-wheezing sound event. False negative (FN) denotes that a real wheezing sound event was incorrectly detected as a non-wheezing sound event. The experimental results for the performance of detecting wheezing sound events are shown in Table 2. Here, a total number of 952 breathing sound events were used for this test. The sensitivity and positive predictive values (PPVs) of detecting wheezing sound events were about 91.51% and 100%, respectively. The false detections mainly resulted from the decayed breathing sound, the spectral shift of wheezing sound (>250 Hz), or the interference of environmental noise, such as the speech sound of the staff or families.

Sensors 2017, 17, 171 10 of 15

3.3. Performance of Breathing Sound Analysis Algorithm In this section, the performance of detecting wheezing events by using the proposed breathing sound analysis algorithm was investigated. Before evaluating the performance of the proposed algorithm, several parameters of the binary classification test have to be defined first: true positive (TP) denotes that a real wheezing sound event was correctly detected as a wheezing sound event. False positive (FP) denotes that a non-wheezing sound event was incorrectly detected as a wheezing sound event. True negative (TN) denotes that a non-wheezing sound event was correctly detected as a non-wheezing sound event. False negative (FN) denotes that a real wheezing sound event was incorrectly detected as a non-wheezing sound event. The experimental results for the performance of detecting wheezing sound events are shown in Table2. Here, a total number of 952 breathing sound events were used for this test. The sensitivity and positive predictive values (PPVs) of detecting wheezing sound events were about 91.51% and 100%, respectively. The false detections mainly resulted from the decayed breathing sound, the spectral shift of wheezing sound (>250 Hz), or the interference of environmental noise, such as the speech sound of the staff or families.

Table 2. Performance of proposed method on wheeze detection.

Wheezing Sounds Events Detected by Proposed Algorithm + – Total + 496 (TP) 46 (FN) 542 Real breathing sound event – 0 (FP) 410 (TN) 410 Total 496 456 952 Here, + denotes wheezing event, and – denotes non-wheezing event.

4. Discussion

For the normal breathing sounds, the value of NSI0Hz–250Hz was similar to NSI250Hz–500Hz, and larger than NSI500Hz–1000Hz. Therefore, the spectral feature of healthy breathing sounds was exhibited as the characteristic of the low-pass filter due to the chest surface [23–25]. From the experimental results, it was shown that the value of NSI250Hz-500Hz for wheezing sounds was larger than that of NSI0Hz–250Hz and NSI500Hz–1000Hz, and this also indicated that the spectral distribution of wheezing sounds mainly concentrated at a frequency range from 250 Hz to 500 Hz due to the phenomenon of bronchoconstriction. In the previous study [26], it indicated that, for wheezing sounds, the main frequency range in the spectrum was between 200 Hz and 600 Hz, and this also fits our experimental results. In the clinic, a wheezing sound is a kind of continuous musical sounds containing a specific tone and the sinusoidal wave appearance, resulting from the airway wall oscillation and vortex shedding in central airways [27–32]. Because of the obstructive airways, the given airflow, which goes through the narrowing bronchus, will result in the change of airflow’s velocity, and this is also associated with louder breathing sounds, and the larger peak intensity in the power spectrum [33–36]. From the experimental results in this study, the peak frequency and its intensity for wheezing sounds were higher and larger than that of normal breathing sounds (about 100 Hz–300 Hz [37]). Moreover, the bandwidth of wheezing sounds was narrower than that of normal breathing sounds. This result also fits the above-mentioned phenomenon. However, the difference of the median frequency between different groups was not obvious. The duration of wheezing sounds was about 736.86 ± 311.40 ms, and this also fits the results in previous studies (over 150 ms) [23,25]. Here, the occurrences of the FP events are mainly caused by the influence of background noise (speech voice) or artificial motion (the friction between chest wall and acoustic sensor), and that of FN events are mainly caused from the weaker amplitude or the shift of the wheezing features in frequency domain. In previous studies, many methods have been proposed to investigate the features of various abnormal breathing sounds (such as wheezing sounds) for patients with obstructive pulmonary Sensors 2017, 17, 171 11 of 15 diseases, and the comparison between the proposed system and other methods is summarized in Table3. Schreur et al. investigated the power spectrum to analyze the frequency features of breathing sounds in patients with asthma [15]. They indicated that even though the lung function was within the normal range, the generation or transmission of breathing sounds for the asthma group was still different from that of the healthy control group. Moreover, the quartile frequencies in the power spectrum of asthma group were higher than that of healthy control group, and they considered that wheezing sounds contained the peak frequency which was above 150 Hz and was at least three times higher than the baseline level. Uwaoma et al. developed a time frequency threshold-dependent (TFTD) algorithm to detect wheezing sounds in a smartphone [38]. The peak frequency, the number of consecutive harmonics, and the duration was used as the wheezing features, but they also indicated that the peak frequency of breathing sounds, that is highly similar to that of wheezing sounds, would easily result in the detecting failure. Jin et al. proposed a time frequency decomposition method to obtain a noise-resistant time frequency contour and detect wheezing sounds under background noise [39]. Riella et al. proposed an image processing method to extract the time frequency features of wheezing sounds from the spectral projection pattern of a spectrogram [20]. They indicated that the wheezing sounds obviously appeared as an isolated higher amplitude feature in the spectrogram. However, it might be inconvenient for real-time wheeze detection in commercial mobile phones or tablets, which may contain relatively low computing capability, due to the higher computational complexity and the requirement of longer raw data. Içer et al. extracted the frequency features from the power spectral density (PSD) based on the Welch method, and used the technique of support vector machine (SVM) to classify different abnormal breathing sounds [18]. They indicated that the ratios of the minimum and maximum frequency in PSD for rhonchus and crackles could be distinguished. Lin et al. employed the order truncate average (OTA) method to enhance the features of wheezing sounds in a spectrogram, and used the technique of back-propagation neural network (BPNN) to detect wheezing sounds [40]. Several parameters extracted from the shape feature of breathing sounds in spectrogram were used as the features of wheezing sounds. Lin et al. also used Mel frequency cepstral coefficient (MFCC) and Gaussian mixture model (GMM) to detect wheezing sounds [41]. The above method could provide a good performance of detecting wheezing sounds, but they also required more computational complexity. In Table3, studies [ 20,39,40] processed spectrograms to obtain the features of wheezes, and their computational complexities are all in O(n2). Refs. [18,38,41] and our proposed system processed spectra from fast Fourier transform (FFT) to obtain the features of wheezes, and their computational complexities are all in O(nlogn). Thus, Refs. [18,38,41] and our proposed system have lower computational complexities than Refs. [20,39,40]. In Refs. [18,38,41], they have to use classifiers to recognize wheezes, but our proposed system only uses addition and compares with thresholds, so our proposed system has lower computational complexity than Refs. [18,38,41]. Most of the above methods only provide the simple information in frequency domain, such as peak frequency and median frequency, and this could not provide sufficient information to distinguish wheezing sounds. By using time frequency analysis and the technique of supervised learning classifier (SVM, BPNN, GMM, etc.), they could provide good performance on detecting wheezing sounds, but they also required more computational complexity. To improve the above issue, the NSI obtained from short-term breathing sounds was used to analyze the time frequency feature of breathing sounds in this study. Different from other methods, which required relatively longer raw data and more computational complexity, the proposed method could easily extract the time frequency feature from short-term breathing sounds, and required lower computational complexity. The proposed method could easily be implemented in commercial mobile phones or tablets, which may contain relatively low computing capability. Moreover, by using the wearable mechanical design and wireless communication, the proposed system could be more convenient and more free to collect the breathing sounds in daily life than other studies in Table3. Sensors 2017, 17, 171 12 of 15

Table 3. System comparison between proposed system and other systems.

R. J. Riella et al. [20] F. Jin et al. [39] C. Uwaoma et al. [38] S. Içer et al. [18] B. S. Lin et al. [40] B. S. Lin et al. [41] Proposed System Wheezing and Rhonchus and Breathing sounds Wheezing sounds Wheezing sounds Wheezing sounds Wheezing sounds Wheezing sounds crackle sounds crackles sounds Electrical condenser Electrical condenser Electrical condenser Electrical condenser Sensing technique – Smart-phone Electronic stethoscope microphone microphone microphone microphone Six zones on Measurement Location – – Anterior chest posterior chest Trachea Trachea Anterior chest Spectral projection, Time-frequency Time-frequency PSD based on Order truncate average, Mel frequency cepstral Feature extraction Normalized spectral decomposition, threshold dependent welch method, support back-propagation technique artiﬁcial neural coefﬁcient, Gaussian integration network k-nearest neighbor algorithm vector machine neural network mixture model Computational High High Low Medium High Medium Low complexity Wearable device – No No No No No Yes Wireless transmission No No No No No No Yes

Analysis of abnormal Wheeze detection, Applications Wheeze detection Wheeze detection Wheeze detection Wheeze detection Wheeze detection breathing sound breathing sound analysis Sensors 2017, 17, 171 13 of 15

5. Conclusions We proposed a wearable and wireless breathing sound monitoring system. Here, a wireless breathing sound acquisition module and a wearable mechanical design were also designed to collect breathing sounds wirelessly in daily life. Moreover, a breathing sounds analysis algorithm, which uses the NSI obtained from short-term breathing sounds, was also proposed to analyze the time frequency feature of breathing sounds. The proposed algorithm required only short-term breathing sound data and lower computational complexity. Therefore, it is suitably implemented in commercial mobile phones or tablets, which may contain relatively low computing capability to perform real-time wheeze detection. It could also provide the objectively quantitative information of breathing sounds (peak frequency, median frequency, bandwidth, duration, and NSI) to the professional physicians. From the experimental results, the features of SI250Hz-500Hz, NSI0Hz-250Hz, NSI250Hz-500Hz, peak frequency, and bandwidth of wheezing sounds were signiﬁcantly different from that of normal breathing sounds. Therefore, the proposed system contains the potential for being developed as a useful monitoring system for assisting with diagnosis of chronic respiratory diseases in the future.

Acknowledgments: This research was partly supported by the Chang Gung Medical Foundation in Taiwan (R. O. C.). The project number is CMRPG3D1891 and CMRPG3D1892. This research was also partly supported by the Ministry of Science and Technology of Taiwan, under grants MOST-105-2221-E-009-013 and MOST-105-2221-E-305-001. Author Contributions: Bor-Shing Lin provided the breathing sound analysis algorithm and the graphical user interface of the system software. Cheng-Ta Yang and Shih-Hong Li supported the experiment design and helped with the clinical data collection. Chen-Han Tsai and Bor-Shyh Lin provided the circuit design of the wireless breathing sound acquisition module and the wearable mechanical design. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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