Technology for Enhancing Chest Auscultation in Clinical Simulation

Ward, Jeffrey J; Wattier, Bryan A

doi:10.4187/respcare.01072

Cited by 29 publications

(28 citation statements)

References 53 publications

(45 reference statements)

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“…Currently, there are several electronic stethoscopes with added functionality to record heart and/or lung sounds as digital files [32,33]. Most have some capability to amplify sounds and to reduce ambient room noise [32]. The electronic model Littmann 3100 was considered better for listening to heart sounds, even in flight on a Falcon 50 jet aircraft, than the traditional stethoscope [34].…”

Section: Discussionmentioning

confidence: 99%

“…Several microprocessor-based stethoscopes were developed that enable clinicians to hear, see, and record heart sounds [18,31]. Currently, there are several electronic stethoscopes with added functionality to record heart and/or lung sounds as digital files [32,33]. Most have some capability to amplify sounds and to reduce ambient room noise [32].…”

Section: Discussionmentioning

confidence: 99%

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Development of a Smartphone App for Visualizing Heart Sounds and Murmurs

Mamorita¹,

Arisaka²,

Isonaka³

et al. 2017

Cardiology

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Background: Auscultation is one of the basic techniques for the diagnosis of heart disease. However, the interpretation of heart sounds and murmurs is a highly subjective and difficult skill. Objectives: To assist the auscultation skill at the bedside, a handy phonocardiogram was developed using a smartphone (Samsung Galaxy J, Android OS 4.4.2) and an external microphone attached to a stethoscope. Methods and Results: The Android app used Java classes, “AudioRecord,” “AudioTrack,” and “View,” that recorded sounds, replayed sounds, and plotted sound waves, respectively. Sound waves were visualized in real-time, simultaneously replayed on the smartphone, and saved to WAV files. To confirm the availability of the app, 26 kinds of heart sounds and murmurs sounded on a human patient simulator were recorded using three different methods: a bell-type stethoscope, a diaphragm-type stethoscope, and a direct external microphone without a stethoscope. The recorded waveforms were subjectively confirmed and were found to be similar to the reference waveforms. Conclusions: The real-time visualization of the sound waves on the smartphone may help novices to readily recognize and learn to distinguish the various heart sounds and murmurs in real-time.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Development of a Smartphone App for Visualizing Heart Sounds and Murmurs

Mamorita¹,

Arisaka²,

Isonaka³

et al. 2017

Cardiology

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“…The simulator was originally used for medical training [38]. Therefore, the evaluation of respiratory sound using this device yielded more accurate results.…”

Section: Simulator-observed Respiratory Soundsmentioning

confidence: 99%

Validation of a Body-Conducted Sound Sensor for Respiratory Sound Monitoring and a Comparison with Several Sensors

Joyashiki

Wada

2020

Sensors

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The ideal respiratory sound sensor exhibits high sensitivity, wide-band frequency characteristics, and excellent anti-noise properties. We investigated the body-conducted sound sensor (BCS) and verified its usefulness in respiratory sound monitoring through comparison with an air-coupled microphone (ACM) and acceleration sensor (B & K: 8001). We conducted four experiments for comparison: (1) estimation by equivalent circuit model of sensors and measurement by a sensitivity evaluation system; (2) measurement of tissue-borne sensitivity-to-air-noise sensitivity ratio (SRTA); (3) respiratory sound measurement through a simulator; and (4) actual respiratory sound measurement using human subjects. For (1), the simulation and measured values of all the sensors showed good agreement; BCS demonstrated sensitivity~10 dB higher than ACM and higher sensitivity in the high-frequency segments compared with 8001. In (2), BCS showed high SRTA in the 600-1000 and 1200-2000-Hz frequency segments. In (3), BCS detected wheezes in the high-frequency segments of the respiratory sound. Finally, in (4), the sensors showed similar characteristics and features in the high-frequency segments as the simulators, where typical breathing sound detection was possible. BCS displayed a higher sensitivity and anti-noise property in high-frequency segments compared with the other sensors and is a useful respiratory sound sensor.

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“…Pulmonary auscultation is an essential part of the physical examination of patients with respiratory conditions [2]. Although auscultation is commonly used among health professionals [1], the mastering of this procedure requires complex acoustic skills to distinguish between different respiratory sounds (RS) with similar frequencies, intensities and timings [36,27]. Currently, health students are taught these skills by repeatedly listening to recordings of typical RS [36,15] and visualizing their waveforms [28].…”

Section: Introductionmentioning

confidence: 99%

“…Such tools show great potential to be used in the teaching of auscultation, as they would allow students to interact with a diversity of RS recorded in clinical environments, from patients with different conditions and test the knowledge acquired. However, only few have been developed in the area of respiratory medicine [36]. CompuLung [20,19] and R.A.L.E.…”

Section: Introductionmentioning

confidence: 99%

Usability of Computerized Lung Auscultation–Sound Software (CLASS) for learning pulmonary auscultation

Machado

Oliveira

Jácome

et al. 2017

Med Biol Eng Comput

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The mastering of pulmonary auscultation requires complex acoustic skills. Computer-assisted learning tools (CALTs) have potential to enhance the learning of these skills; however, few have been developed for this purpose and do not integrate all the required features. Thus, this study aimed to assess the usability of a new CALT for learning pulmonary auscultation. Computerized Lung Auscultation-Sound Software (CLASS) usability was assessed by eight physiotherapy students using computer screen recordings, think-aloud reports, and facial expressions. Time spent in each task, frequency of messages and facial expressions, number of clicks and problems reported were counted. The timelines of the three methods used were matched/synchronized and analyzed. The tasks exercises and annotation of respiratory sounds were the ones requiring more clicks (median 132, interquartile range [23-157]; 93 [53-155]; 91 [65-104], respectively) and where most errors (19; 37; 15%, respectively) and problems (n = 7; 6; 3, respectively) were reported. Each participant reported a median of 6 problems, with a total of 14 different problems found, mainly related with CLASS functionalities (50%). Smile was the only facial expression presented in all tasks (n = 54). CLASS is the only CALT available that meets all the required features for learning pulmonary auscultation. The combination of the three usability methods identified advantages/disadvantages of CLASS and offered guidance for future developments, namely in annotations and exercises. This will allow the improvement of CLASS and enhance students' activities for learning pulmonary auscultation skills.

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Technology for Enhancing Chest Auscultation in Clinical Simulation

Cited by 29 publications

References 53 publications

Development of a Smartphone App for Visualizing Heart Sounds and Murmurs

Development of a Smartphone App for Visualizing Heart Sounds and Murmurs

Validation of a Body-Conducted Sound Sensor for Respiratory Sound Monitoring and a Comparison with Several Sensors

Usability of Computerized Lung Auscultation–Sound Software (CLASS) for learning pulmonary auscultation

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