Dataset Information

Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces.

ABSTRACT: Physiological mechano-acoustic signals, often with frequencies and intensities that are beyond those associated with the audible range, provide information of great clinical utility. Stethoscopes and digital accelerometers in conventional packages can capture some relevant data, but neither is suitable for use in a continuous, wearable mode, and both have shortcomings associated with mechanical transduction of signals through the skin. We report a soft, conformal class of device configured specifically for mechano-acoustic recording from the skin, capable of being used on nearly any part of the body, in forms that maximize detectable signals and allow for multimodal operation, such as electrophysiological recording. Experimental and computational studies highlight the key roles of low effective modulus and low areal mass density for effective operation in this type of measurement mode on the skin. Demonstrations involving seismocardiography and heart murmur detection in a series of cardiac patients illustrate utility in advanced clinical diagnostics. Monitoring of pump thrombosis in ventricular assist devices provides an example in characterization of mechanical implants. Speech recognition and human-machine interfaces represent additional demonstrated applications. These and other possibilities suggest broad-ranging uses for soft, skin-integrated digital technologies that can capture human body acoustics.

SUBMITTER: Liu Y

PROVIDER: S-EPMC5262452 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces.

Liu Yuhao Y Norton James J S JJ Qazi Raza R Zou Zhanan Z Ammann Kaitlyn R KR Liu Hank H Yan Lingqing L Tran Phat L PL Jang Kyung-In KI Lee Jung Woo JW Zhang Douglas D Kilian Kristopher A KA Jung Sung Hee SH Bretl Timothy T Xiao Jianliang J Slepian Marvin J MJ Huang Yonggang Y Jeong Jae-Woong JW Rogers John A JA

Science advances 20161116 11

Physiological mechano-acoustic signals, often with frequencies and intensities that are beyond those associated with the audible range, provide information of great clinical utility. Stethoscopes and digital accelerometers in conventional packages can capture some relevant data, but neither is suitable for use in a continuous, wearable mode, and both have shortcomings associated with mechanical transduction of signals through the skin. We report a soft, conformal class of device configured speci ...[more]

PMID: 28138529

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Project description:Objective: In emergency medicine, airway management is a core skill that includes endotracheal intubation (ETI), a common technique that can result in ineffective ventilation and laryngotracheal injury if executed incorrectly. We present a method for automatically generating performance feedback during ETI simulator training, potentially augmenting training outcomes on robotic simulators. Method: Electret microphones recorded ultrasonic echoes pulsed through the complex geometry of a simulated airway during ETI performed on a full-size patient simulator. As the endotracheal tube is inserted deeper and the cuff is inflated, the resulting changes in geometry are reflected in the recorded signal. We trained machine learning models to classify 240 intubations distributed equally between six conditions: three insertion depths and two cuff inflation states. The best performing models were cross validated in a leave-one-subject-out scheme. Results: Best performance was achieved by transfer learning with a convolutional neural network pre-trained for sound classification, reaching global accuracy above 98% on 1-second-long audio test samples. A support vector machine trained on different features achieved a median accuracy of 85% on the full label set and 97% on a reduced label set of tube depth only. Significance: This proof-of-concept study demonstrates a method of measuring qualitative performance criteria during simulated ETI in a relatively simple way that does not damage ecological validity of the simulated anatomy. As traditional sonar is hampered by geometrical complexity compounded by the introduced equipment in ETI, the accuracy of machine learning methods in this confined design space enables application in other invasive procedures. By enabling better interaction between the human user and the robotic simulator, this approach could improve training experiences and outcomes in medical simulation for ETI as well as many other invasive clinical procedures.

Dataset Information

Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces.

Publications

Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces.

Similar Datasets

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